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Concepts in Chemistry

What’re the Three Levels of Representation in Chemistry?

posted on August 6, 2017

 What’re the Three Levels of Representation in Chemistry?

The three levels of representation are:

• Macroscopic
• Particulate

What’s the Macroscopic level of representation?

This level describes the things we can perceive with our senses (smell, taste, sight, touch, and hear) or measure with basic instruments such as thermometer. Some examples of properties we can determine at the macroscopic level include color, taste, texture, temperature, pressure, or density. For example, you can see and describe the color of things you observe. You can also describe the taste of things you taste and texture of things you feel. These properties are usually produced as a result of the collective interactions of atoms or molecules at the particulate or molecular level of the substance. What then is the particulate level?

 What’s the Particulate level of representation?

This level describes the things we cannot perceive with our senses, but they are there. These things include atoms, molecules, ions, electrons, protons, and neutrons. Because these particles are microscopic and invisible to our eyes, the particulate level is sometimes called the, atomic, submicroscopic, microscopic or molecular level. The way these particles arrange and interact at this level determine the properties we observe at the macroscopic level. Because of this, the particulate level is the most valuable level to chemists, and so chemists usually rely on technology to extend their senses so that they can study and understand the behavior of molecules at the molecular level. Once chemists understand their behavior, they can take advantage of this understanding to design materials with unique properties. From this, we can see that science and technology depend on each other. That is technology is necessary to advance science and science is necessary to advance technology.

What’s the Symbolic level of representation?

This level uses symbols to describe the invisible particles that exist at the particulate level and their relationship to one another. These symbols include

• Chemical symbols

For example, the chemical symbol for sodium is Na

• Chemical formulas

For example, the chemical formula for vinegar is CH 3 COOH

• Mathematical formulas

For example, the density (D) of substance is a ratio of its mass (M) to its volume (V). We can express this relationship as: D = M/V

• Chemical equations

For example, hydrogen reacts with oxygen to form water. We can write a balanced chemical equation for this reaction as: 2H 2 + O 2  → 2H 2 O

For example, chemists usually draw a circle to depict an atom . Keep in mind that a model is not the actual thing is representing. It is an approximation to help us study and predict the properties and behavior of the actual thing. In science, models can be abstract, conceptual, mathematical, and graphical.

What’s the Triplet Representation?

When we write each level on one of the vertices of a triangle, we get what we call the Triplet Representation. The triplet representation was proposed by a well known chemical educator called Alex Johnstone.

How can the Triplet Representation Help Us Understand Chemistry?

It can help us in many ways. First, it hints at the idea that when we study a chemical substance or phenomena, we should at least think about it at the macroscopic-, particulate-, and symbolic- level.

What does that mean? 

Let’s explain this further by using a typical example we have all experienced in our lives: water .

At the macroscopic level ,  water can exists as a solid, liquid or gas. When it exists as pure liquid it is colorless. When it exists as solid like ice or snow it is white. When it exists as a gas, it is invisible.

At the particulate level , water consists of two hydrogen atoms united (bonded) with one oxygen atom. Thus, two parts of hydrogen molecules always react with one part of oxygen molecules to make 2 parts of  water molecules. Depending on energy, temperature, and attractive forces, water molecules can arrange at the molecular level to form solid, liquid or gas.

At the symbolic level , water is depicted as H 2 O, where H: hydrogen, and O: oxygen. The subscripts reflect the ratio in which these two different atoms always react to form water. Thus, you always need 2 parts of hydrogen molecules to 1 part of oxygen molecules. As you can tell, the subscript “1” is not written by oxygen. When the subscript of any atom is “1”, it is usually left out when we write its chemical formula.

What is Conceptual Understanding?

Conceptual understanding is achieved when a student can explain beyond the macroscopic level the cause of a chemical phenomenon, just like we did with water.

Why is chemistry difficult to understand?

As you can tell, we can’t perceive the particulate level with our senses. This difficiency makes the particulate level abstract and difficult to understand. However, with the help of technology chemists can extend their senses so that they can explore the behavior and structure of molecules at the molecular level. Once chemists understand the structure and behavior of molecules, they can often predict and make new materials with unique properties.

How can we apply the three-levels to teach a chemistry concept?

• Macroscopic level:  provide students with hands-on activities or experiments so that they can explore to discover things for themselves. For example, if you want to teach about density, let students drop different objects of the same mass or volume in water to see whether an object will float or sink. Once they do that, let them determine the mass and volume of the objects they used.
• Particulate level:  let students draw particle models to explain why they think two objects can have the same mass, but one object may float while the other may sink. And why two objects can have the same volume, but one may float while the other may sink in water.
• Symbolic level:  let students use symbols to discuss the relationship between mass and volume for objects that float and those that sink based on the data they gathered during the activities.

This same approach can be applied to teach chemical reactions and many concepts in chemistry. But in some situations you may need to show animations of the molecular process to help students visualize how the molecules are behaving at the molecular level.

As you do this in your class, you may need to guide students through discussion and questioning so that they can connect all the three levels to the chemistry concept they are learning about.

Check your understanding

• Macroscopic level
• Particulate level
• Symbolic level

Click here to grade your answers.

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Definition of Representative Element

Parts of the Periodic Table

The Periodic Table of Elements as it is known today was developed by Russian chemist Dmitri Mendeleev and first presented in the German chemistry priodical Zeitschrift f?r Chemie in 1869. Mendeleev had originally created his “Periodic System” by writing properties of the elements onto pieces of cards and arranging them in order of increasing atomic weight. Mendeleev also determined that the relative atomic mass of some elements was wrongly calculated. By correcting this, he was able to place the elements into their correct place in the table. Mendeleev also left places for elements that had not yet been discovered. As of June 2010, the periodic table contain 118 confirmed elements.

On the periodic table of elements, columns of elements define element groups that share many common properties. There are two sets of groups in the periodic table. The first set are Group A elements and are also known as representative elements. The second set are Group B elements and are also known as transition metals. Representative elements are the most abundant elements on earth.

Representative Elements and Layout of the Periodic Table

On the Periodic Table of Elements, elements are arranged in columns known as \"groups,\" and rows known as \"periods.\" Groups contain elements with similar properties that have the same electron arrangement in their outer shells, known as \"valence electrons,\" which determine the properties of the element and it's chemical reactivity, and how it will take part in chemical bonding. The Roman numerals above each group dictate the usual number of valence electrons.

Groups are further divided into Representative Elements and Transition Metals. Groups 1A and 2A on the left and 3A through 8A on the right are classified as Representative Elements, while those elements in between are classified as Transition Metals. Representative Elements are also known as \"Group A,\" \"S and P Block Elements,\" or \"Main Group Elements.\"

Significance of the Layout

The layout of the periodic table demonstrates recurring chemical properties. Elements are listed in order of increasing atomic number (the number of protons in the atomic nucleus) and arranged so that elements with similar properties fall into the same columns. Elements are listed with, among other information, their element symbol, atomic number and atomic mass.

List of Representative Elements in S Block

The S Block elements or the elements in columns 1A and 2A on the left of the periodic table include Hydrogen (H), Lithium (Li), Sodium (Na), Potassium (K). Rubidium (Rb), Cesium (Cs), Francium (Fr), Beryllium (Be), Magnesium (Mg), Calcium (Ca), Strontium (Sr), Barium (Ba) and Radium (Ra).

List of Representative Elements in P Block

The P Block elements or the elements in columns 3A through 8A on the right of the periodic table include:

• Aluminum (Al)
• Gallium (Ga)
• Indium (In)
• Thallium (Tl)
• Silicon (Si)
• Germanium (Ge)
• Ununquadium (Uuq)
• Nitrogen (N)
• Phosphorus (P)
• Arsenic (As)
• Antimony (Sb)
• Bismuth (Bi)
• Selenium (Se)
• Tellurium (Te)
• Polonium (Po)
• Fluoride (F)
• Chlorine (Cl)
• Bromine (Br)
• Astatine (At)
• Helium (He)
• Krypton (Kr)

Uses of the Periodic Table

One of the main uses of the periodic table is to predict the chemical properties of an element based on its location. Mendeleev used the trends in his table to predict the properties of five elements that had not yet been discovered at the time he constructed his table. Atom size, the ability to form a chemical bond, and the energy needed to remove an electron all decrease as one moves from left to right across a period and increase as one moves down a column.

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• Royal Society of Chemistry: Dmitri Mendeleev
• \"The Periodic Table: It's Story and it's Significance,\" Eric Scerri, 2007

About the Author

Joshua Wade has been a freelance writer since 2006. Wade's poetry and short fiction have appeared in "The Frequent and Vigorous Quarterly" and "The Litter Box Magazine." He has also written for various online publications. Wade attended West Virginia University where he studied English and creative writing.

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Structure Representation and Line Notations in C hemistry

The chemical structural formulas that we usually use are difficult for computers to understand. Therefore, when handling structural information on a computer, it is necessary to convert the structural information into a computer-friendly method before handling it.  

Graphical representation of compounds

Chemical graph theory is a branch of mathematics which combines graph theory and chemistry. A molecule can be thought of as a graph with atoms as nodes and bonds as edges. A graph can represent how an atom is connected to other atoms. Hydrogen atoms can be added later if the number of bonds between atoms is known, so hydrogen atoms are often omitted when expressing molecules on a computer. Each carbon atom has four chemical bonds and each hydrogen atom has one chemical bond. Therefore, the hydrogen atoms can be removed without losing information about the molecule.  

For example, Ethane should be expressed as:

In the graph structure, the positions of atoms are not taken into consideration, and only the connections between atoms are important .

In this way, the method of notating the molecular structure in one line according to a certain rule is called " line notation ".

Line Notation

Line notations are highly desirable structure representation methods. they tend to offer a compact representation of the constitution and connectivity of a topological representation of chemical structures, but tend to lack additional information, such as protonation and geometry, that is necessary for many modelling techniques. here, three line notations will be introduced:  Simplified Molecular-Input Line-Entry Specification (SMILES), SMARTS, and IUpaC International Chemical Identifier (InChI)

SMILES Specification

The SMILES is one of the "line notation" which is an acronym for " Simplified Molecular Input Line Entry System". A rguably the most commonly used line notation is the SMILES string. The SMILES representation uses alphanumeric characters that closely mimic atoms and bonds as drawn in two-dimensional chemical structures.

Atoms in a SMILES string are represented by their elemental symbol in the periodic table of the elements, within square brackets. however, the square brackets can be implicit for the organic subset of elements: ‘B’, ‘C’, ‘N’, ‘O’, ‘S’, ‘p’, ‘Br’, ‘Cl’, ‘F’, and ‘I’. The hydrogens are typically implicit, but can be defined in certain cases. an atom that contains one or more charges must be enclosed in square brackets followed by the ‘h’ symbol and number of hydrogens bonded to it—if only one then it may be omitted. Following this, a plus symbol represents a positive charge and a subtraction symbol represents a negative charge. the number of charges can be included after the charge symbol, with one charge again being implicit. the number of charges can also be included explicitly by additional charge symbols. therefore, methane is simply ‘C’ and water ‘O’.

Bonds in a SMILES string are represented by symbols that mimic the chemical structure diagram representations: a single bond is ‘-’; a double bond is ‘=’; a triple bond is ‘#’.

H owever, bonds in a SMILES string are implied in a large number of cases. Bonds between aliphatic atoms are implicitly assumed to be single bonds and therefore the single bond symbol is not required. therefore, ethanol, starting the SMILES string from the monovalent carbon, is written as ‘CCO’, but is equally valid as ‘C–C–O’. Bonds between aromatic atoms are implicitly assumed to be aromatic.

Branching in a SMILES string is defined by round brackets. therefore, Isobutyl alcohol, would be ‘CC(C)CO '.

Ring systems in a SMILES string are encoded by ring closure tags, which indicate that two atoms in the string are connected and therefore form a ring system. So, hexane would be ‘CCCCCC’, whereas cyclohexane would be ‘C1CCCCC1’. For a second ring, the ring closure tag would be ‘2’, and so on. If the number of ring closure tags needed exceeds ‘9’ then a percentage symbol must be used in front of the symbol. this is important since a single atom may encode two different ring closures, e.g. ‘–C12–’.

Aromaticity in a SMILES string is encoded by using the lowercase characters for carbon, nitrogen, oxygen, and sulphur: ‘c’, ‘n’, ‘o’, ‘s’, respectively. therefore, cyclohexane, as we have already seen, is ‘C1CCCCC1’, whereas benzene is ‘c1ccccc1’. aromatic bonds are implied between aromatic atoms, but may be explicitly defined using the ‘:’ symbol. an aromatic nitrogen bonded to a hydrogen must be explicitly defined as ‘[nh]’: pyrrole is ‘c1cc[nh]c1’ and imidazole is ‘c1cnc[nh]1’.

Stereochemistry in a SMILES string is encoded by the special characters ‘\’, ‘/’, ‘@’, and ‘@@’. around two double bonds, the configuration specifies the cis and trans configurations. therefore, valid SMILES strings of cis- and trans-butene are ‘C\C=C\C’ and ‘C\C=C/C’, respectively.

For example, E- and Z-1,2-difluoroethene can be represented by the following isomeric SMILES:

F/C=C/F or F\C=C\F (E)-1,2-difluoroethene (trans isomer)

F/C=C\F or F\C=C/F (Z)-1,2-difluoroethene (cis isomer)

Configuration around tetrahedral centers are indicated by the symbols “@” or “@@”

C[C@@H](C(=O)O)N L-Alanine

C[C@H](C(=O)O)N D-Alanine

SMARTS are straightforward extensions of SMILES. It is an acronym for SM ILES AR britrary T arget S pecification (SMARTS) notation and allows us to search in certain databases (like PubChem) for generic structures. It is a language used for describing molecular patterns. I t is a notation developed especially for expressing substructures and performing structural searches in databases.  

Some representative symbols and examples are summarized below.  

For details on SMARTS notation , go for "Daylight Theory Manual: SMARTS – A Language for Describing Molecular Patterns " by Daylight .  

the IUpaC International Chemical Identifier (InChI™) is an international standard in structure representation. InChI is a representation of molecular information in a form understandable to humans. Since every compound gives a different InChI, it can be thought of as analogous to the IUPAC name of the compound. As mentioned earlier from the development history, the difference from canonical SMILES is that the generation algorithm can be used freely for non-commercial purposes.  

the InChI identifier provides a layered representation of a molecule to

allow for the representation of differing levels of resolution depending on

the application in mind. the layers defined by InChI are as follows:

Chemical Formula Layer 

Connections- bonds between atoms and may have sublayers, with the last one dealing with mobile hydrogens.

Charge Layer

Component Charge

Stereochemical Layer

Double Bond sp 2 (Z/E) Sterochemistry

Tetrahedral Sterochemistry

Isotopic Layer

Fixed Hydrogen Layer 

While InChIs may seem difficult for humans to decipher, they are primarily designed for computer processing and contain valuable molecular data hidden within their layers. Unlike simpler chemical representations like SMILES, InChIs cannot be easily read by people. However, computers excel in interpreting this complex code, enabling them to access vital information about a given compound or structure. So while it might prove challenging for an individual reader, InChIs serve as powerful tools for computers to analyze chemical structures effectively. To make sense of these notations, researchers typically rely on specialized software and databases capable of decoding InChIs. By employing these resources, users can obtain essential details about the molecules in question without needing direct comprehension of the underlying code. 

How to get a chemical representation of molecules ?

From Databases

SMILES and other chemical representations of a certain molecule are compatible with compound databases such as ChemSpider, PubChem, ChEMBL , and DrugBank, so naturally you can get information from each entry in the database.

And we will show you in the upcoming tutorials how to deal with ChemSpider and PubChem programatically with python to obtain chemical information.

• Create one using software or website

Since SMILES notation is a general-purpose compound information format, many software supports exporting in these formats. Using ChemDraw, which is familiar to experimental chemists, you can Select the drawn structure and select " Edit -> Copy As " to export in SMILES or InChI format.  

Another one is using an online website. For example, the SMILES generator/checker website generates SMILES in real time when you enter a structural formula. It is also possible to generate InChI.

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Evaluation of chemical representations in physical chemistry textbooks

James M. Nyachwaya * and Nathan B. Wood Department of Chemistry and Biochemistry, and School of Education, North Dakota State University, P.O. Box 6050, Fargo, ND 58108, USA. E-mail: [email protected]

First published on 14th July 2014

That different levels of representation are important for complete understanding of chemistry is an accepted fact in the chemistry education community. This study sought to uncover types of representations used in given physical chemistry textbooks. Textbooks play a central role in the teaching and learning of science (chemistry), and in some cases textbooks are the curriculum (Chiappetta and Fillman, 2007; Gkitzia et al. , 2011). The books are not only central to instructors' curriculum; they are also a major resource for students' reference especially outside of class. Using a coding rubric developed by Gkitzia et al. (2011), the physical chemistry textbooks were analyzed to determine at what level(s) the included representations conveyed chemistry content. Representations were also analyzed for their characteristics or features. Results indicate that the analyzed texts contain at least one representation on 95% of the sampled pages and, on average each page contains about 1.4 representations. The vast majority of the representations are symbolic level representations, accounting for about 85% of representations. Particulate or submicroscopic representations were in a slightly higher proportion than macroscopic and multiple representations, but these collectively only accounted for about 15% of the representations in the textbooks. Our results indicate no significant difference in the types of representations used in textbooks for chemistry and life science majors. Across editions of a number of textbooks, there does not seem to be a difference in the type and proportion of representations used. An analysis of the representations showed that virtually all were completely related to the accompanying text, had surface features that were clear and explicit, and captions were concise, explicit and completely described associated representations. Implications of our findings to the chemistry education community are described.

Introduction

Despite the importance of the different levels of representation to the learning of chemistry, research in chemistry education has consistently revealed that students struggle with the levels of representation—they struggle with both understanding the chemistry at each level, and translating between the levels. Research studies have documented struggles within individual levels, e.g. with interpreting chemical formulas ( Ben-Zvi et al. , 1987 ; Kern et al. , 2010 ; Nyachwaya et al. , 2011 ); chemical equations ( Kozma and Russell, 1997 ; Nyachwaya et al. , 2011 ; Naah and Sanger, 2012 ), translating between formulas, electron configurations and models ( Keig and Rubba, 1993 ). Students also struggle translating between the levels ( Chittleborough and Treagust, 2008 ; Davidowitz and Chittleborough, 2009 ; Kern et al. , 2010 ; Nyachwaya et al. , 2011 ; Naah and Sanger, 2012 ).

An important question derived from the documented students' struggles with representations in chemistry pertains to the exposure that students get to the different representations as they go through the ‘chemistry pipeline’. Textbooks, as an important resource, should present content, and therefore expose students to different levels of representation in chemistry. This study looks at representations in physical chemistry textbooks. Given that physical chemistry is traditionally offered as an upper level course in the chemistry track in most departments in the United States, (and we assume other places as well) this study will shed some light into the kinds of exposure students get in upper level courses.

Theoretical background

Representations in chemistry.

One of the outcomes of interest in and use of Johnstone's ‘triangle’ ( Lorenzo et al. , 2010 ; Jaber and BouJaoude, 2011 ) is the different ‘faces’, interpretations and modifications of the levels of representation by the chemistry education community ( Talanquer, 2011 ). This is also problematic because the chemical education community does not seem to have a common view of the ‘triplet’ relationship ( Taber, 2013 ). Referring to the same ideas, researchers in the chemistry education community have used different terms. For example, Talanquer (2011) talked about ‘three main ways’, while Gilbert and Treagust (2009) used the terms ‘types of representation’. Even while referring to Johnstone's original levels, Gilbert and Treagust (2009) used the terms phenomenological, model and symbolic as equivalents to the macro, submicro and representational levels. Over the years, a similar trend is seen as researchers use different terms. For example, Gabel et al. (1987) used levels of description, while Gabel (1993, 1999) used levels of instruction and levels of representation respectively. As Talanquer (2011) noted, the different interpretations can be a source of confusion, especially if they are all meant to refer to the same ‘objects’ or components of the triplet.

Another area that is problematic in the way the idea of the three levels of representation has been used is the fact that the three levels of representations occupy three apices of ‘Johnstone's triangle’. Specifically, representations that have been traditionally ascribed to the apices of the triangle may transcend the one apex of the ‘triangle’ they are assigned to. For example, as Taber (2013) noted, an equation of a reaction showing formulas of reactants and products, which is a symbolic representation according to Johnstone's definition (2000), can be ascribed to the macroscopic and symbolic levels simultaneously. A graph in a physical chemistry textbook could represent a macroscopic level observation. Designating the graph as only a symbolic level representation is therefore problematic. In our past research (Nyachwaya et al. , 2011), as well as others, ( e.g. Naah and Sanger, 2012 ) and in accordance with Johnstone's original work, formulas and chemical symbols have been classified as part of the symbolic language. Unless care is taken during instruction, while referring to a representation, students will not necessarily know what level a teacher is referring to ( Taber, 2013 ).

In addition to Johnstone's three levels, Dori and Hameiri (2003) proposed a fourth level, the process level, which they defined as encompassing the process of chemical reactions and how it pertains to the macroscopic, symbolic and particulate levels. Gkitzia et al. (2011) noted that there are representations that combine elements from the different levels, such as hybrid, multiple and mixed representations. According to the authors, a hybrid representation is one that combines characteristics of two levels of representation, while a multiple representation depicts a phenomenon at more than one level of representation simultaneously. A mixed representation on the other hand has one of the three levels of representation and another kind of representation such as an analogy ( Gkitzia et al. , 2011 ). More recently, Dangur et al. (2014) suggested the addition of a fifth level, which they called the quantum level, which involves understanding of the electronic structure of atoms, molecules and the solid state, and the relationship to quantum mechanics.

In response to the existence of different (and problematic) interpretations and use of the levels of representation, Talanquer (2011) proposed a ‘multi-dimensional knowledge space’ (p. 186), which he suggested could better capture chemistry knowledge than ‘Johnstone's triangle’. According to Talanquer (2011) , the knowledge space characterizes chemistry knowledge according to different approaches of teaching (mathematical, conceptual, historical and contextual), types of knowledge (experiences, models and visualizations), dimensions (structure/composition, energy and time), and levels or scales (subatomic, molecular, supramolecular, multi-particle, mesoscopic and macroscopic).

Representations in chemistry textbooks

For a representation in a textbook to be useful, it has to facilitate understanding of target chemistry concepts. According to Gkitzia et al. (2011) , the surface features of the representation have to be explicitly communicated to the reader (students). Indeed, representations will not be useful if students cannot infer the right and intended meaning from them. According to the authors, representations have to be also completely related to the associated content in a text. In case a representation has an accompanying caption, the caption has to be clear, completely define the representation and enable a reader to understand the representation ( Gkitzia et al. , 2011 ). Equally important is the need for a representation to be presented in a form that students can understand in textbooks ( Giordan, 1991 ).

Why physical chemistry textbooks?

Could the dominance of the quantitative approach to teaching physical chemistry ( Dangur et al. , 2014 ) be a function of types of representation predominantly used in physical chemistry textbooks? An instructor's curriculum is derived from resources such as textbooks ( Chiappetta and Fillman, 2007 ; Gkitzia et al. , 2011 ). Ideally, physical chemistry textbooks should present content at and using different levels of representation. Integrating different levels of representation is known to enhance the understanding of chemistry ( Johnstone, 1991 ; Treagust et al. , 2003 ; Chandrasegaran et al. , 2008 ).

With increased interest in chemistry students' representational fluency ( Kozma, 2000 ), textbooks have been analyzed to catalog the nature and types of representations therein. Most of the textbook analyses looking at representations have involved general chemistry textbooks, mostly used at the high school and college freshman levels ( e.g. Gkitzia, et al. , 2011 ). Physical chemistry is usually offered as an upper level course in most schools in the United States. Students enrolling in physical chemistry would have normally taken general chemistry, organic chemistry and biochemistry, where they get exposure to different levels of representation ( e.g. Gkitzia et al. , 2011 ). In analyzing representations in physical chemistry textbooks, our goal was to find out what opportunities or exposure if any, students get to learn about and use the different levels of representations in an upper level course such as physical chemistry. To the best of our knowledge, no such analyses exist for physical chemistry textbooks. Also, not much work has been done on upper level college textbooks. This study involves analyzing physical chemistry textbooks for the nature and types of representations used. This analysis involves textbooks spanning the undergraduate to graduate level continuum.

This study is guided by the following research question(s):

(i) What are the different types of representations used in physical chemistry textbooks?

(ii) How do the types of representations compare across texts for different audiences (chemistry majors and life science majors)?

(iii) How do representations compare across different editions of the same textbook?

(iv) What are the characteristics of representations in the physical chemistry textbooks?

These research questions are important for three reasons. First, it is now accepted in the chemistry education community that multiple representations aid in the understanding of chemistry ( Johnstone, 1991 ; Treagust et al. , 2003 ; Chandrasegaran et al. , 2008 ). Where necessary, multiple representations are combined to help describe and explain phenomena. For physical chemistry, it is important to find out what types of representations are used in textbooks. Second, given the importance of representations, partially explained by a lot of research done on the topic, curriculum materials have been developed in response to the research. One question we sought to understand is whether and how physical chemistry, as a branch of chemistry, has responded to the research. Finally, as Gkitzia et al. (2011) noted, there are certain features of a representation that make it useful in facilitating the understanding of a concept. Characterizing representations will help us understand their features, and how likely they are to facilitate the learning of associated chemistry content.

Methodology

Textbook selection and sampling, coding and analysis.

(1) Types of representation – a representation could fall into six categories: macroscopic, submicroscopic, symbolic, hybrid, multiple and mixed ( Gkitzia et al. , 2011 ). The macroscopic, submicroscopic and symbolic are based on Johnstone's original definitions (1991). A hybrid representation is one that combines characteristics of two levels of representation, while a multiple representation depicts a phenomenon at more than one level of representation simultaneously. A mixed representation on the other hand has one of the three levels of representation and another kind of representation such as an analogy ( Gkitzia et al. , 2011 ). It is worth noting that multiple, hybrid, and mixed representations are not levels of representation on their own.

(2) Surface features of a representation – they could either be explicit, implicit or ambiguous.

(3) Relationship of representations to the text refers to whether there is an explicit reference to the representation within the text.

(4) Existence and properties of a caption – for representations that have a caption, the caption should ideally be brief, explicit and comprehensive.

(5) Degree of correlation between components comprising a multiple representation, the relatedness between the components or elements of the representations.

Using the above criteria ( Gkitzia et al. , 2011 ), one researcher coded representations in two of the twelve textbooks. The same textbooks were coded by the same researcher after two weeks to determine intra-rater reliability, which came out to be 99%. The second researcher coded one of the two textbooks for comparison. The inter-rater reliability was established at 97%. Differences were resolved through discussion. In each text's sampled pages, each page was analyzed for the type of representation present if any. We should stress here that our coding is guided by the Gkitzia et al. (2011) rubric. In adopting this rubric, we are aware as discussed above that there are inherent problems in simply classifying representations into the three levels proposed by Johnstone, and in light of Talanquer's (2011) proposed complex knowledge space.

In order to answer the research questions posed in this study, different kinds of analyses were done. First, all twelve textbooks were analyzed for the different types of representations. Upon examination, we found that the physical chemistry textbooks were written for different audiences – students in the ‘chemistry majors’ track and others, such as life sciences. Texts written for these audiences (based on their titles, such as physical chemistry for life sciences) were analyzed to see the similarities and differences between representations in these texts if any. We were also interested in looking at any changes in the type and number of representations in different editions of the same book (by the same author). Four books were chosen for this analysis. Two editions of each textbook were analyzed. Four (same) chapters from each text across two editions were analyzed.

Each representation was further analyzed for surface features, relatedness to the accompanying text, and for representations with a caption, the nature of a caption. These attributes were adopted from Gkitzia et al. (2011) . Each representation was classified and characterized at the same time. Excel was used during the coding and analysis processes. Fig. 1 shows a snapshot of an Excel page of one of the textbooks analyzed.

Question 1: (i) What are the different types of representations used in physical chemistry textbooks?

As can be seen from the table, on average, each of the sampled pages in each textbook had at least one representation. Even though not every page had a representation, a very high percentage of the sampled pages had a representation on them (94% to 99%). This is an encouraging trend in the context of having representations used alongside text to convey content in the textbooks. An important next question is: What are the different types of representations in the textbooks?

Our results showed that different types or levels of representation were used in the physical chemistry textbooks sampled. Table 2 gives a summary of the types and proportion of representations used in the analyzed physical chemistry textbooks. Note that Table 2 is divided into two sections. The top section is for physical chemistry textbooks for ‘chemistry majors’, while the bottom section has textbooks for ‘life science majors’.

A number of observations can be made from Table 2 . Of the twelve books analyzed, five had four types of representations; six had three types of representations while one had only one type of representation. Symbolic representations were the most common type of representation used, followed by sub-microscopic representations. Generally, of the three common representations used in the textbooks macroscopic representations were used least across the textbooks. None of the twelve textbooks contained mixed or hybrid representations in the sampled pages. Even though the range in years of publication was 1994–2011, it is interesting to note that the proportions in individual categories of representations are not very different over that time span. One wonders whether physical chemistry as a branch of chemistry has been slow to responding to ‘reform’ in terms of including multiple representations—especially higher proportions in the macroscopic, submicroscopic and multiple levels of representation.

(ii) How do the types of representations compare across texts for different audiences (chemistry majors and life sciences)?

Worth noting from Table 3 is a very similar trend both in proportion and type of representations used in the two editions of each textbook analyzed. In general, it appears that of the changes made across the two editions of each textbook, specifically to the same topics, not much changed in terms of the type and proportions of representations and the number of representations used. For the textbooks analyzed here, across the editions, there does not seem to be an appreciable change in the proportion of the three types of representations used, as the symbolic representation is the most commonly used. In particular, if an earlier edition of a textbook lacked a type of representation (such as multiple representations), a newer edition did not have or include that representation.

Question 2: What are the characteristics of the representations found in the physical chemistry textbooks?

All of the representations identified in the analyzed textbooks were completely related to the accompanying text. In all the cases, as the phenomenon was described in the text as part of the content, reference was made to the representations, which were in most cases referred to as a figure, assigned a number. This explicit relationship enables users, more importantly students, to link representations to phenomena and content describing it.

Of the representations that had captions, 100% of the captions were brief and explicit, and completely described the accompanying representations. This makes the representations described by the caption clear and understandable to users. Representations that did not have captions were mainly mathematical equations which were derivations, which are not expected to have captions. Gkitzia et al. (2011) describe such representations as being incorporated, meaning that they form part of the text. Looking at the characteristics of the representations presented here shows an encouraging trend, especially in facilitating student understanding of the representations. That said, studies should be made to determine the utility of these characteristics to students, or if students can recognize their value.

Discussion and implications

One would ideally expect to see attempts in textbooks to not only use different representations, but to also integrate the representations as well. The closest such integration was seen in the very few multiple representations, as evidenced in Table 1 . An advantage of such a presentation is that it enables learners to see phenomena at multiple levels ( e.g. the same phenomena at symbolic and submicroscopic levels), which could help students transfer between the different levels ( Chandrasegaran et al. , 2007 ). In contrast, students learning and experiencing phenomena at one level are likely to end up with fragmented knowledge ( Treagust et al. , 2003 ), not knowing how different levels or representation of the same phenomena are related. Saying this does not downplay the important fact that representations are used in the textbooks.

Given the central role that textbooks play in science (chemistry), there is a need for increasing the use of other representations. It is true that physical chemistry has long been characterized by mathematical derivations involving symbols, tables and graphs. Is there room for other representations beside the symbolic level? While it is encouraging that other levels of representation are used, their proportions are very low. As noted above, representations in one of the textbooks analyzed were 100% symbolic. How and where will students get exposure to other representations if textbooks don't use them?

The results of this study communicate an important message about the nature of education in physical chemistry; specifically as a sub-discipline of chemistry that is heavy in symbolic level representations. The results analyzed here show that mathematical derivations are the most common symbolic level representations. Research has shown that students struggle with symbolic level representations ( Kozma and Russell, 1997 ). If representations in textbooks are predominantly symbolic, we expect students to struggle with making sense of them. There is a real opportunity here for other representations to be brought in. As part of reform efforts in chemistry, it is important for this image of the nature of chemistry to be addressed.

Dangur et al. (2014) noted that instruction in physical chemistry tends to be primarily quantitative. We speculate that much of this state can be attributed to the fact that representations in physical chemistry textbooks are mostly symbolic, and the symbolic representations are mostly mathematical derivations. Any teaching approach that focuses on some of the levels results in confusion, information overload, decreased student motivation and ultimately less student achievement ( Talanquer, 2011 ). Studies in other areas of chemical education, such as problem solving, have shown that instruction that focuses at the symbolic level, particularly equations and formulas, encourages rote memorization in students, where they can go through a derivation, for example, without necessarily understanding the underlying chemistry ( e.g. Nakhleh, 1993 ). The fact that symbolic representations, specifically mathematical derivations, are the most common types of representation found in the textbooks analyzed should be of concern to the chemistry education community. As noted above, textbooks are central to instruction and learning of chemistry. If instructors then rely on the textbooks as written, and students use the textbooks as resources, what is presented in the textbooks is what is learned.

The findings presented here have implications for physical chemistry textbook authors, instructors and other curriculum developers. This study analyzed textbooks published between 1994 and 2011. As the results have shown, the proportions of different types of representations across the textbooks are very similar. This suggests over the years that, as newer books get published, there has not been much change in the diversity of representations used. The results of this study have also shown that even across editions of the same textbook, by the same author, there is virtually no difference in the proportion and type of representations used. The newer editions of textbooks are not being informed by a need to include multiple representations. While most of the textbooks from this analysis have more than one representation used, it is important to increase the use (proportion) of all representations, as much as the symbolic level is used.

For classroom instructors, using different levels of representation during instruction enhances the understanding of chemistry ( Cheng and Gilbert, 2009 ). Given the influence of textbooks in the teaching of chemistry, it is incumbent upon teachers to intentionally look for other resources, especially representations to augment their teaching. Knowing the importance of using representations in teaching chemistry, teachers have to use resources that include representations as part of their content. For a teacher, this may mean combining a number of textbooks while developing curriculum to ensure that students get the necessary exposure and learning. Also, a careful choice of textbooks is necessary in this context to ensure that they include representations, and the useful features that will enhance learning.

Future studies

Limitations of this study.

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What Is a Chemical Reaction? Definition and Examples

Chemical reactions are the backbone of chemistry and, arguably, life itself. Understanding what a chemical reaction is, how to represent it, how to categorize it, and how to distinguish it from a physical change is vital.

What Is a Chemical Reaction?

A chemical reaction is a process in which the chemical structure of a substance changes, leading to the formation of a new substance with different properties. In other words, the reactants convert into products through the breaking and formation of chemical bonds .

Describing Chemical Reactions Using Chemical Equations

A chemical equation is a symbolic representation of a chemical reaction. Reactants are written on the left-hand side, and products on the right, separated by an arrow indicating the direction of the reaction. Combinations of coefficients, element symbols, subscripts, and superscripts indicate the chemical formulas of the reactants and products and their quantities. For each chemical formula, the cation (positive-charged part) of a compound gets listed before the anion (negative-charged part). For example, you write NaCl for sodium chloride rather than ClNa.

A balanced chemical equation follows conservation of mass and charge. There are exactly the same number of atoms of each element on both the reactant and product sides of the equation. The net electrical charge is also the same for both sides of the equation.

Examples of Chemical Reactions

For example, here are some chemical reactions represented as chemical equations:

• The formation of water from hydrogen and oxygen: 2H 2 + O 2 → 2H 2 O
• The combustion of methane: CH 4 + 2O 2 → CO 2 + 2H 2 O
• The decomposition of calcium carbonate: CaCO 3 → CaO + CO 2

How to Recognize a Chemical Reaction

Not all changes involving matter are chemical reactions. A chemical reaction is a chemical change , which means the starting materials are chemically different from the ending materials. In contrast, matter also changes form via physical changes. But, in a physical change , the chemical identity of matter does not change.

For example, when you melt an ice cube into liquid water, the chemical identity of the ice and the water is the same (H 2 O). Melting (and any other phase transition) is an example of a physical change. No chemical reaction occurs. However, when you combine baking soda (NaHCO 3 ) and vinegar (CH 3 COOH), the two chemical undergo a chemical reaction that produces sodium acetate (NaC 2 H 3 O 2 ), water (H 2 O), and carbon dioxide (CO 2 ).

You can’t see the atoms and molecules in action and in the examples of melting ice and reacting baking soda and vinegar, you start with a transparent substance and end with one. So, how do you know which is a physical change and which is a chemical reaction? There are several indicators of a chemical change:

• Color change
• Forming a gas or bubbles
• Forming a precipitate
• Temperature change
• Releasing or absorbing light or sound
• Irreversibility (Most chemical changes are irreversible, while most physical changes are reversible.)
• Changing chemical properties

Melting ice is reversible and does not really meet the other criteria for a chemical change, so it is a physical change. Mixing baking soda and vinegar results in bubbles, a temperature change, and new chemical properties.

Types of Chemical Reactions

There are many different types of chemical reactions , but there are four main classes:

Synthesis (Combination) Reactions

• Description : Two or more substances combine to form a single product.
• General Reaction: A + B → AB
• Example : N 2 + 3H 2 → 2NH 3

Decomposition Reactions

• Description : A single compound breaks down into two or more simpler substances.
• General Reaction: AB → A + B
• Example : 2H 2 O → 2H 2 + O 2

Single-Replacement Reactions

• Description : One element replaces another element in a compound.
• General Reaction: A + BC → AC + B
• Example : Zn + 2HCl → ZnCl 2 + H 2

Double-Replacement Reactions

• Description : The cations and anions of two different molecules switch places.
• General Reaction: AB + CD → AD + CB
• Example : AgNO 3 + NaCl → AgCl + NaNO 3

Other Types of Reactions

There are many other types of reactions, such as:

• Redox Reactions : Involves electron transfer.
• Acid-Base Reactions : Involves the transfer of a proton.
• Complexation Reactions : Formation of complex ions.
• Polymerization : Formation of polymers from monomers.

Importance of Chemical Reactions

Chemical reactions are at the heart of chemistry. Understanding their mechanisms, types, and representations helps us grasp more complex concepts and applications. From the combustion that powers our cars to the metabolic reactions that keep us alive, chemical reactions are indispensable to our daily lives. Applications include:

• Medication formulation
• Making cleaners
• Making disinfectants
• Waste treatment
• Food processing
• Energy production
• Material design
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2.1: Chemical Representations on Computer Part I

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Learning Objectives:

• Describe and be able to identify ambiguous, unambiguous, and canonical representations of chemical structure, as well as explicit and implicit information contained in these representations.
• Describe each of the four major approaches to machine representation of chemical structure (connection tables, graphic visualizations, line notation, and descriptive representations), as well as the advantages and drawbacks of each of these forms.
• Describe how database record IDs relate to representations of chemical structure.
• Describe lookup and translation approaches to exchanging chemical identifiers, including what countertranslation is and why it can be useful.

CHEMICAL REPRESENTATION FOR CHEMINFORMATICS

Most often, data and information about chemical compounds is either directly about molecular structure (for example, a 2D structural formula, or 3D atomic coordinates for a particular conformation of a compound), or is tied to a molecular structure (for example, physical properties of a compound, which you identify by its structural formula). The notion of indexing, sorting, searching and retrieving information using molecular structures originated within the domain of modern chemistry.

Almost all chemists engage in communication tasks to register, search, view, and publish molecular structures. Most forms of chemical representation were developed with these uses in mind. Cheminformatics involves storing, finding, and analyzing these structures using the data-processing power of computers to match chemical compounds with literature publications, measured properties, synthetic procedures, spectra, and computational studies. To do this work, computers need to use chemical representation to identify, exchange and validate information about chemical compounds.

In order for (human) chemists to rely on insights from cheminformatics, it is important to understand the way in which computers store and analyze chemical structure, the methods that computer programs employ, and the results that they produce. Therefore, cheminformatics depends upon the use of representations of molecular structures and related data that are understandable both to human scientists and to machine algorithms .

FORMULATING CHEMICAL STRUCTURE DATA

Interacting with a machine is a form of communication. How does communication between chemists differ from communication between a chemist and a machine? In cheminformatics, you are working within a system governed by strict rules that are explicitly defined. If you know the rules, then you can make the system work for you. If you don't know the rules for a given form of representation, sometimes features designed to satisfy the requirements of one context will appear as bugs in another context.

If one chemist was to recommend to another that a reaction should be performed using "chloroform" as a solvent for a reaction, this would generally be a successful exercise in communication. For all practical purposes, this word is understood by every chemist, and has no ambiguity. However, because "chloroform" is a so-called trivial name , there is no formula for converting it into the actual chemical structure that it represents, and a machine will not be able to participate in this exchange of information unless it has been explicitly instructed as to the chemical structure that this word represents, expressed in a format that the machine can work with.

A more descriptive way to communicate the composition that is chloroform is by chemical formula, in this case CHCl 3 . A computer program could interpret basic molecular structure rules to determine that the substance being described has 5 atoms: 1 carbon, 1 hydrogen and 3 chlorine. Assembling this into a molecule with bonds can be based on valence rules, identifying 4 of the atoms as normally monovalent and one as normally tetravalent. It is quite simple to create a software algorithm that can join the atoms together in the most obvious way, which also happens to be correct.

Beyond such tiny simple molecules, difficulties soon arise. Some of these ambiguities affect human chemists in the same way that they affect machines. Consider the molecular formula of C 3 H 6 O, which is associated with multiple reasonable structures, including a ketone, an aldehyde, a cyclic alcohol, oxygenated alkenes and cyclic ethers, one of which exists as two enantiomers:

Ambiguous representations can refer to more than one chemical entity. This is true of most chemical names when used unsystematically, such as “octane,” when employed as a common term for all saturated hydrocarbons with eight carbon atoms, rather than systematically to indicate the straight-chain isomer only. Empirical and molecular formulas are also typically ambiguous.

In an unambiguous system of representation, each name or formula refers to exactly one chemical entity, typically in a way that allows you to draw a structural formula for it. However, each chemical entity might be represented by more than one name or formula. A canonical form is a completely unique representation within a system. For example, “diethyl ketone” and “3-pentanone” are both unambiguous names: each represents one and only one compound. However, since they represent the same compound, they are not unique names. Within the system of Preferred IUPAC Names (see below), “3-pentanone” is a canonical name – an unambiguous and unique representation of this compound.

Note that, since canonical names are necessarily canonical within a system, they might not function properly if you are interested in structural information that is not addressed within the system, or if you do not have structural information that is required by the system. For example, within a system that does not address stereochemistry, the different enantiomers of a chiral compound will have the same “canonical” representation. Within a system that requires the specification of stereochemistry, on the other hand, you will have to choose between stereospecific canonical representations. If you happen to be working with a racemic mixture or a compound of unknown stereo configuration, this may lead to misrepresentation and misunderstanding.

A chemical structure representation contains two kinds of information: explicit and implicit . [H1] Explicit information is what’s directly represented in a data structure and should at minimum contain what otherwise would not be known, such as the specific atom in a carbon skeleton to which a substituent is attached. Implicit information is what you (or a computer) can figure out from a data structure, given some knowledge of general principles and a little bit of work.

In general, data structures that contain less explicit information are more simple and compact, but they require more computation to draw chemical conclusions from them. Data structures that contain more explicit information take up more space and are at greater risk of containing inconsistencies, but they can be more quickly analyzed in a wider variety of ways.

To automate functions on chemical data, the data structure needs to be systematically defined and consistently applied. These definitions are part of what constitutes explicit information that an algorithm can readily identify and parse. Balancing the level of explicit information can also impact the ambiguity of a system, and the ability to accurately exchange chemical structures between systems. These are especially important considerations for operations that range across a significant portion of the corpus of reported chemical compounds (well over 100 million), beyond the scale at which human validation of results is possible.

REPRESENTATING CHEMICAL STRUCTURE DATA

Generally, the most effective way to communicate with another chemist about the structure of a compound is to draw its structural formula. A structural formula is any formula that indicates the connectivity of a compound – that is, which of its atoms are linked to each other by covalent bonds.

It just so happens that structural formulas can be fairly directly mapped to a computer-friendly data structure: a molecular graph stored as a connection table . Connection tables do for computers what systematic nomenclature does for human chemists: they the organize structural information defined in a molecular graph in a form that is easier to read and to order in a list. The difference is that computers can read, sort, search, and group connection tables far faster than humans can work with systematic names or any other kind of formula or notation. Connection tables are covered in more depth in the second part of this module .

Chemical structure is represented on computers in several forms, usually generated from chemical connectivity data stored in connection tables. These representations are designed to facilitate many human and computer functions and are machine-actionable as long as they can be tied into a database of connection tables or an algorithm for translating a given representation into a connection table. Besides connection tables, the most common forms of machine-readable representations are graphic visualizations, line notations, and other descriptive forms such as nomenclature.

Graphic Visualizations

Chemists most frequently think about chemical structure in 2D, and molecules actually exist in 3D physical space. Most chemical data systems offer 2D and 3D visualizations that human chemists can use to communicate preferences for searching and analysis. The 2D coordinates stored in a connection table can be used to infer and display chemical information, including the basic structural formula and additional information such as the E/Z geometry of alkene-like double bonds and the cis/trans isomerism of ligands in a square planar metal complex or substituents on a cyclic alkane. 2D representations are designed to mimic the experience of drawing structural formulas on paper. Human users can further fix these electronic drawings as images to use in publications and presentations, but these image files are no longer connected directly to chemical data and are thus not machine readable.

3D (x,y,z) coordinates can also be stored for each atom and used to display the conformation of a molecule. These coordinates may be determined experimentally (typically via x-ray crystallography), or calculated (using force-fields, quantum chemistry, molecular dynamics or composite models such as docking). Understanding a molecule's actual shape, whether it be in solution, in a vacuum, or in the binding site of a protein, opens up a whole new domain of computational chemistry. Most molecules have some flexibility, and even if a given conformation is the most stable, there are often a number of competing shapes to consider. Knowing how a particular set of coordinates was determined is crucial to making intelligent use of it for cheminformatics purposes.

Machine generation and interpretation of graphical representations involve persistent challenges that can affect the accuracy of chemical information communicated between humans and machines. Because structural formulas were originally invented to represent organic compounds, both people and computer programs will tend to assume, as a default, that structural formulas represent networks of atoms linked by discrete covalent bonds. This chemical logic makes it possible for machines to handle these graphical representations, and many chemistry databases are organized around this principle of explicit connectivity. However, delocalized systems, non-covalent molecules such as coordination compounds, and other classes of chemical substances do not fit easily into these conventions for generating and interpreting graphical representations. There are different practices among chemists for depicting such structural features, and there are no broadly accepted conventions for representing them in connection tables. For example, the IUPAC standards for graphical representation in publications specify the use of curved circle bonds instead of alternating single and double bonds for the pi-system within aromatic rings. They also indicate that coordination bonds between a metal and an aromatic system should be represented as a single bond from the metal into the middle of the ring, and that formal charges should not be shown. However, these conventions are beyond the scope of basic rules of valence and are difficult to program consistently. As a result, coordination compounds represented in this way may be captured as separate fragments in connection tables. Computer software may interpret the end of the bond in the middle of an aromatic ring as an implied additional methyl group. Circles within rings may not be decipherable in a computer program, and the associated electron system may be ignored entirely. A more common representation of coordination compounds used in chemistry databases addresses these problems by including explicit bonds between the metal and each atom in the ring. This follows basic rules of valence and enables a more consistent approach to structure search. However, this notation can be misleading for human readers as the nature of the association between the metal and the ring is not covalent bonding.

Line Notations

Line notations represent chemical structures as a linear string of symbolic characters that can be interpreted by systematic rule sets. They are widely used in Cheminformatics because a) many computational processes operate more effectively on data structured as linear strings than data structured as tables, and b) line notations can be reasonably legible to human chemists designing functions with these tools. Linear representations are particularly well-suited to many identification and characterization functions, such as determining:

• whether molecules are the same;
• how similar they are, according to some metric;
• whether one molecular entity is a substructure of another;
• whether two molecules are related by a specific transformation;
• what happens when molecules are cut into pieces and grafted together at different positions.

In these and other applications of cheminformatics, linear representations have key advantages for speed and automation, especially when you’d like to handle huge numbers of structures (e.g. searching a large database).

Examples of line notations include the Wiswesser Line-Formula Notation (WLN), Sybyl Line Notation (SLN) and Representation of structure diagram arranged linearly (ROSDAL). Currently, the most widely used linear notations are the Simplified Molecular-Input Line-Entry System (SMILES) and the IUPAC Chemical Identifier (InChI), which are described in the third part of this module.

Descriptive Representation

Systematic names describe the structural formula of compounds. If you know the rules and vocabulary, you should be able to write a name based on a structural formula and vice-versa. Chemists have developed various ways of translating formulas into names, so it is nearly always possible to write more than one systematic name for a given compound.

IUPAC (International Union of Pure and Applied Chemistry) nomenclature is a well-known international system of chemical names that is generally systematic but flexible, allowing the use of certain well-established trivial names. Since systematic IUPAC names are made according to formalized rules, they could, in principle, be used by both humans and computers. However, IUPAC names are often quite difficult for chemists to read, let alone to write, and the rules are non-canonical, resulting in numerous different options for naming each compound. IUPAC has introduced even more rules for determining canonical Preferred IUPAC Names (PINs) that are oriented toward making systematic names more easily readable by machines.

Semantic technologies further enable systematic classification and organization of scientific terms, including descriptions of chemical structures, such as provided by ChEBI (Chemical Entities of Biological Interest). ChEBI describes small molecular entities based on nomenclature, symbolism and terminology endorsed by IUPAC and the Nomenclature Committee of the International Union of Biochemistry and Molecular Biology (NC-IUBMB). This dataset is highly curated by both human experts and machine processes, is openly searchable and programmatically accessible, and includes full references to original authoritative sources.

IDENTIFYING CHEMICAL STRUCTURE DATA

In Cheminformatics, working programmatically and at scale, you need to be able to automatically retrieve, organize and differentiate large numbers of chemical substances by structural data. Specific databases that collect and organize information by chemical substances will usually have a record ID system that identifies the profile of the compound or substance as assembled in that database. However, most record ID systems use alpha-numeric strings that are not defined by chemical structure rules, in contrast to linear structure notations such as SMILES and InChI. Record IDs should not be used as proxies for molecular structure for cheminformatics purposes, unless there is an automated way to look up and retrieve the original structure data files.

The most familiar system of chemical record IDs is the Chemical Abstracts Service Registry Number (CAS RN). The CAS Registry can be searched using CAS products such as SciFinder and STN . In this database, unique identifiers are assigned to each chemical substance reported in the literature, providing an unambiguous way to identify a chemical substance or system within the Registry when there are many possible systematic, generic, proprietary or trivial names. CAS RNs are numeric identifiers that can contain up to ten digits, divided by hyphens into three parts: the first consisting of two to seven digits, the second consisting of two digits, and the third consisting of a single digit. These numbers themselves have no inherent chemical meaning about structure, but are assigned in sequential order to new substances in a variety of forms as they are reported in the literature.

Record IDs can sometimes be considered de facto identifiers for chemicals by the users of these databases. However, these ID systems are specific to their originating data structure and are not necessarily suitable to use in practice to systematically identify compounds outside of these databases. CAS Registry records reflect what has been reported about substances and not necessarily a systematic gestalt of chemical structures. A CAS RN may refer to a substance profile with several structures for a multi-component system, or no structure at all, or an unspecified configuration. While CAS RNs have been widely used, the CAS database is a proprietary and controlled registry. Most CAS RNs that appear online are not verified relative to characterizing information about a substance. It may not be possible to disambiguate whether a CAS RN refers to a single chemical compound or to a component in a mixture, for example.

The PubChem CID and the ChemSpider ID are two other alphanumeric record ID systems that do not inherently contain chemical structure information in the ID notation itself. However, these IDs refer to chemical structural data that is systematically generated and organized within these databases, which can be openly and programmatically searched. Thus PubChem and ChemSpider record IDs are often used by computer programs as links to identify chemical structure data within these large systems. PubChem cheminformatics functionality will be discussed as an example more extensively later in this course.

Even if a record ID is canonical, that does not mean that it identifies a compound or substance with absolute precision. For example, there is a separate CAS RN for each enantiomer of lactic acid, as well as a third RN that might identify either the racemic mixture or the compound with unspecified stereochemistry. How do you know which isomer you have if just a number is given in a report? There is no way to tell algorithmically, and you may need to search with all three RNs and potentially retrieve many false hits if you are interested in only one particular isomer. Similarly, the canonical form of SMILES does not take R/S stereoisomerism into account, so each enantiomer of a compound will have the same canonical SMILES formula.

The International Chemical Identifier (InChI) also provides a rule set that generates canonical structure representations, but can be similarly hampered by ambiguity of higher level structure considerations. PubChem and ChemSpider incorporate the InChI algorithm as part of their data validation schemata, and many other databases accept InChI and SMILES strings as queries to search for chemical structures.

InChI can be hashed into a shorter form of 27 characters, called an InChIKey. This allows for even easier searching of general systems such as Google, which can locate chemical structures in many open databases. Once hashed, InChIKeys are not reversible and cannot algorithmically generate a chemical structure, except by looking up an InChIKey in a database record that also contains the structure. Thus the InChIKey can serve to confidentially notate proprietary structural information that has not yet been disclosed.

EXCHANGING CHEMICAL REPRESENTATIONS

Effective aggregation and re-use of chemical data often involves swapping the identifier that you’ve located for another representation for the same compound that’s more convenient for your purpose. For example, if you are interested in comparing the structures of a list of compounds for which you have registry numbers, you need to swap those registry numbers for structural formulas, connection tables, or another sort of representation that gives you the structural information you’re looking for.

This sort of re-use of notation happens a lot in cheminformatics – after all, some kinds of cheminformatics analysis weren’t even conceivable when most common forms of chemical names and formulas first caught on. But the repurposing of notation isn’t unique to cheminformatics. In fact, as long as chemical names and formulas as we know them have been around, chemists have been re-using names, deciding that they fit other purposes better than the ones for which they were intended, or trying to change them in ways that undermine their original purpose.

There are two basic approaches to exchanging chemical identifiers: lookup and translation . In the case of lookup , you locate the identifier that you have in an existing database that lists various different identifiers for each compound, and you select the other identifier that you want. This is like using a thesaurus. There are several tools available for cross-referencing identifiers from different databases, including the CACTUS , UniChem , and PubChem Identifier Exchange services. All of these lookup services accept most linear identifiers as queries.

In the case of translation , you use a set of rules (or a computer uses an algorithm) to take apart one type of representation of a compound and convert it into another type of representation for the same compound. There are several open toolkits available for translation, including RDKit , OpenBabel , Chemistry Development Kit , among others (see Blue Obelisk ).

Like words for the same object in different languages, even when two representations are meant to refer to exactly the same compound, they differ in their connotations . They describe different aspects of structure more or less explicitly, they emphasize different kinds of family relationships or functional patterns, and they draw upon different ways of interpreting chemical objects and phenomena. Identifiers are not equally specific: for example, you can translate a structural formula into a single molecular formula, but you cannot translate that molecular formula back into a structural formula.

Translation can thus be quite lossy and lookup may identify a close but not precise enough match for your need. It can be difficult to catch these problems during the exchange and different tools may present different problems. Naoki Sakai, a scholar of translation in literature and politics, has written, “Every translation calls for a countertranslation.” The same is true in chemistry. Can you use a newly generated representation and get back to the original one? When exchanging representation formats, countertranslation can identify what might have gotten lost or inadvertently added in translation.

Large chemical databases use validation and counter-translation as part of standardizing the data included in their chemical records. For example, they may collect data that includes both systematic names and molecular structures and run each of these name-to-structure and structure-to-name conversions to match any previous instances of these compounds in their databases or identify any potential errors.

As you process chemical structure data, consider how usable your output is for a diversity of unknown future cheminformatics applications. Follow common practices such as those used in the large public chemical databases, and carefully document your notation mapping and rules. Whenever you exchange chemical structure data, keep a provenance trail to the original data source and note the tools and resources you have used with the data so that others following on your work can use your data efficiently (including yourself!).

Different forms of chemical notation are more appropriate for different settings. Systematic names aren’t usually much good in casual conversation; you can’t do a google search for a sketch of a structural formula; a computer can’t analyze a reaction mechanism using trivial names. It is critical to remember both the human audience and the machine requirements for interpreting and using chemical structure information.

Chemical Structure Drawing Programs (This link is to the Fall 2015 Cheminforamtics OLCC page): http://olcc.ccce.divched.org/2015OLCCModule1P4TLO4 Of notable interest is Dr. Tamas E. Gunda's "Chemical Drawing Programs" (last link on above page): http://www.gunda.hu/dprogs/index.html

1. Using PubChem’s tool for compound search (go here and click the hexagon to search by structural formula), or other programs of your choice (SciFinder, ChemSpider, Wikipedia (if you dare)), fill in the following table. (For more information, see Exchanging Chemical Representations , above. For more on SMILES and InChI, see the third part of this module .)

2. Many chemistry databases index by structural formulas based on explicit connectivity for organic small molecules. Many molecules do not fit easily into these conventions for representing bonds, such as coordination compounds and delocalized systems. (Conventions for human-readable and computer-readable graphical representation of such compounds are discussed above .) Of the representations below for a coordination substructure, which is most likely to be acceptable for publication? Which for searching an index? How might each of these representation be interpreted in a database?

3. Resolve each of the following the systematic names listed for Vitamin C into structural formulae using each of the systems below. Is the expected stereochemistry represented? (For more information, see Formulating Chemical Structure Data , above.)

• openmolecules: http://www.openmolecules.org/name2structure
• OPSIN: http://opsin.ch.cam.ac.uk/
• CACTUS: http://cactus.nci.nih.gov/chemical/structure
• ChemSpider: http://www.chemspider.com/
• PubChem: https://pubchem.ncbi.nlm.nih.gov/
• ( R )-3,4-dihydroxy-5-(( S )-1,2-dihydroxyethyl)furan-2(5 H )-one
• ( R )-5-(( S )-1,2-dihydroxyethyl)-3,4-dihydroxyfuran-2(5 H )-one
• (2R)-2-[(1S)-1,2-dihydroxyethyl]-3,4-dihydroxy-2H-furan-5-one
• (5R)-[(1S)-1,2-dihydroxyethyl]-3,4-dihydroxy-3-oxolen-2-one

FURTHER READING & REFERENCES

• Jonathan Brecher, “Graphical Representation Standards for Chemical Structure Diagrams (IUPAC Recommendations 2008),” Pure and Applied Chemistry 80, no. 2 (January 1, 2008), 227–410. URL: http://dx.doi.org/10.1351/pac200880020277 (accessed Jan. 2017).
• Antony Williams, “Chemical Structures,” in The ACS Style Guide (American Chemical Society, 2006), 375–83. URL: http://dx.doi.org/10.1021/bk-2006-STYG.ch017 (accessed Sept. 2015).
• Neil G. Connelly and Ture Damhus, eds., IUPAC Nomenclature of Inorganic Chemistry (Cambridge: Royal Society of Chemistry, 2005), 53–67. (The “Red Book”). URL: http://old.iupac.org/publications/books/rbook/Red_Book_2005.pdf (accessed Sept. 2015).
• Wikipedia entry on the Red Book . URL: https://en.wikipedia.org/wiki/IUPAC_nomenclature_of_inorganic_chemistry_2005 (accessed Sept. 2015).
• Compound Interest , http://www.compoundchem.com/ (accessed Sept. 2015).
• (good examples of effective communication using formulas)
• “Names and Numbers for Chemical Compounds,” in The ACS Style Guide (American Chemical Society, 2006), 233–54. URL: http://dx.doi.org/10.1021/bk-2006-STYG.ch012 (accessed Sept. 2015).
• American Chemical Society, Naming and Indexing of Chemical Substances for Chemical Abstracts, 2007 Edition (Columbus, OH: American Chemical Society, 2008). URL: http://www.cas.org/File%20Library/Training/STN/User%20Docs/indexguideapp.pdf (accessed Sept 2015).
• Henri A. Favre and Warren H. Powell, eds., Nomenclature of Organic Chemistry: IUPAC Recommendations and Preferred Names 2013 (Cambridge: Royal Society of Chemistry, 2014). (The “Blue Book”). URL: http://pubs.rsc.org/en/content/ebook/9780854041824 (accessed Sept. 2015).
• Wikipedia entry on the Blue Book . URL: https://en.wikipedia.org/wiki/IUPAC_nomenclature_of_organic_chemistry (accessed Sept. 2015).

Cheminformatics

• Blue Obelisk: https://en.wikipedia.org/wiki/Blue_Obelisk
• Warr, W. A. Representation of chemical structures. Wiley Interdiscip. Rev.: Comput. Mol. Sci. 2011, 1, 557–579; DOI: 10.1002/wcms.36 (accessed May 29, 2104).
• Warr, W. A. Some Trends in Chem(o)informatics. Chemoinformatics and computational chemical biology. Methods Mol. Biol. 2011, 672, 1–37; DOI: 10.1007/978-1-60761-839-3_1 (accessed May 29, 2104).
• Wild, D. Introducing Cheminformatics: Navigating the world of chemical data. http://i571.wikispaces.com (accessed Sept. 29, 2015).
• Willet, P. Chemoinformatics: a history. WIREs Comput. Mol. Sci. 2011, 1, 46–56; DOI: 10.1002/wcms.1 (accessed May 29, 2014).

Contributors:

• Evan Hepler-Smith , Harvard University
• Leah R. McEwen, Cornell University
• Acknowledgements: Alex Clark, Sunghwan Kim

Adapted from Spring 2017 Cheminformatics OLCC