is chemistry a problem solving subject

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1.1 Problems and Problem Solving

1.2 types and kinds of problems, 1.3 novice versus expert problem solvers/problem solving heuristics, 1.4 chemistry problems, 1.4.1 problems in stoichiometry, 1.4.2 problems in organic chemistry, 1.5 the present volume, 1.5.1 general issues in problem solving in chemistry education, 1.5.2 problem solving in organic chemistry and biochemistry, 1.5.3 chemistry problem solving under specific contexts, 1.5.4 new technologies in problem solving in chemistry, 1.5.5 new perspectives for problem solving in chemistry education, chapter 1: introduction − the many types and kinds of chemistry problems.

• Published: 17 May 2021
• Special Collection: 2021 ebook collection Series: Advances in Chemistry Education Research
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G. Tsaparlis, in Problems and Problem Solving in Chemistry Education: Analysing Data, Looking for Patterns and Making Deductions, ed. G. Tsaparlis, The Royal Society of Chemistry, 2021, ch. 1, pp. 1-14.

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Problem solving is a ubiquitous skill in the practice of chemistry, contributing to synthesis, spectroscopy, theory, analysis, and the characterization of compounds, and remains a major goal in chemistry education. A fundamental distinction should be drawn, on the one hand, between real problems and algorithmic exercises, and the differences in approach to problem solving exhibited between experts and novices on the other. This chapter outlines the many types and kinds of chemistry problems, placing particular emphasis on studies in quantitative stoichiometry problems and on qualitative organic chemistry problems (reaction mechanisms, synthesis, and spectroscopic identification of structure). The chapter concludes with a brief look at the contents of this book, which we hope will act as an appetizer for more systematic study.

According to the ancient Greeks, “The beginning of education is the study of names”, meaning the “examination of terminology”. 1 The word “problem” (in Greek: «πρόβλημα » /“ problēma” ) derives from the Greek verb “ proballein ” (“pro + ballein”), meaning “to throw forward” ( cf. ballistic and ballistics ), and also “to suggest”, “to argue” etc. Hence, the initial meaning of a “ problēma ” was “something that stands out”, from which various other meanings followed, for instance that of “a question” or of “a state of embarrassment”, which are very close to the current meaning of a problem . Among the works of Aristotle is that of “ Problēmata ”, which is a collection of “why” questions/problems and answers on “medical”, “mathematical”, “astronomical”, and other issues, e.g. , “Why do the changes of seasons and the winds intensify or pause and decide and cause the diseases?” 1  

Problem solving is a complex set of activities, processes, and behaviors for which various models have been used at various times. Specifically, “problem solving is a process by which the learner discovers a combination of previously learned rules that they can apply to achieve a solution to a new situation (that is, the problem)”. 2   Zoller identifies problem solving, along with critical thinking and decision making, as high-order cognitive skills, assuming these capabilities to be the most important learning outcomes of good teaching. 3   Accordingly, problem solving is an integral component in students’ education in science and Eylon and Linn have considered problem solving as one of the major research perspectives in science education. 4  

Bodner made a fundamental distinction between problems and exercises, which should be emphasized from the outset (see also the Foreword to this book). 5–7   For example, many problems in science can be simply solved by the application of well-defined procedures ( algorithms ), thus turning the problems into routine/algorithmic exercises. On the other hand, a real/novel/authentic problem is likely to require, for its solution, the contribution of a number of mental resources. 8  

According to Sternberg, intelligence can best be understood through the study of nonentrenched ( i.e. , novel) tasks that require students to use concepts or form strategies that differ from those they are accustomed to. 9   Further, it was suggested that the limited success of the cognitive-correlates and cognitive-components approaches to measuring intelligence are due in part to the use of tasks that are more entrenched (familiar) than would be optimal for the study of intelligence.

The division of cognitive or thinking skills into Higher-Order (HOCS/HOTS) and Lower-Order (LOCS/LOTS) 3,10   is very relevant. Students are found to perform considerably better on questions requiring LOTS than on those requiring HOTS. Interestingly, performance on questions requiring HOTS often does not correlate with that on questions requiring LOTS. 10   In a school context, a task can be an exercise or a real problem depending on the subject's expertise and on what had been taught. A task may then be an exercise for one student, but a problem for another student. 11   I return to the issue of HOT/LOTS in Chapters 17 and 18.

Johnstone has provided a systematic classification of problem types, which is reproduced in Table 1.1 . 8   Types 1 and 2 are the “normal” problems usually encountered in academic situations. Type 1 is of the algorithmic exercise nature. Type 2 can become algorithmic with experience or teaching. Types 3 and 4 are more complex, with type 4 requiring very different reasoning from that used in types 1 and 2. Types 5–8 have open outcomes and/or goals, and can be very demanding. Type 8 is the nearest to real-life, everyday problems.

Classification of problems. Reproduced from ref. 8 with permission from the Royal Society of Chemistry.

Problem solving in chemistry, as in any other domain, is a huge field, so one cannot really be an expert in all aspects of it. Complementary to Johnstone's classification scheme, one can also identify the following forms: quantitative problems that involve mathematical formulas and computations, and qualitative ones; problems with missing or extraordinary data, with a unique solution/answer, or open problems with more than one solution; problems that cannot be solved exactly but need mathematical approximations; problems that need a laboratory experiment or a computer or a data bank; theoretical/thought problems or real-life ones; problems that can be answered through a literature search, or need the collaboration of specific experts, etc.

According to Bodner and Herron, “Problem solving is what chemists do, regardless of whether they work in the area of synthesis, spectroscopy, theory, analysis, or the characterization of compounds”. 12   Hancock et al. comment that: “The objective of much of chemistry teaching is to equip learners with knowledge they then apply to solve problems”, 13   and Cooper and Stowe ascertain that “historically, problem solving has been a major goal of chemistry education”. 14   The latter authors argue further that problem solving is not a monolithic activity, so the following activities “could all be (and have been) described as problem solving:

solving numerical problems using a provided equation

proposing organic syntheses of target compounds

constructing mechanisms of reactions

identifying patterns in data and making deductions from them

modeling chemical phenomena by computation

identifying an unknown compound from its spectroscopic properties

However, these activities require different patterns of thought, background knowledge, skills, and different types of evidence of student mastery” 14   (p. 6063).

Central among problem solving models have been those dealing with the differences in problem solving between experts and novices. Experts ( e.g., school and university teachers) are as a rule fluent in solving problems in their own field, but often fail to communicate to their students the required principles, strategies, and techniques for problem solving. It is then no surprise that the differences between experts and novices have been a central theme in problem solving education research. Mathematics came first, in 1945, with the publication of George Polya's classic book “ How to solve it: A new aspect of mathematical method ”: 15  

“The teacher should put himself in the student's place, he should see the student's case, he should try to understand what is going on in the student's mind, and ask a question or indicate a step that could have occurred to the student himself ”.

Polya provided advice on teaching problem solving and proposed a four-stage model that included a detailed list of problem solving heuristics. The four stages are: understand the problem, devise a plan, carry out the plan, and look back . In 1979, Bourne, Dominowski, and Loftus modeled a three-stage process, consisting of preparation, production , and evaluation . 16   Then came the physicists. According to Larkin and Reif, novices look for an algorithm, while experts tend to think conceptually and use general strategies . Other basic differences are: (a) the comprehensive and more complete scheme employed by experts, in contrast to the sketchy one used by novices; and (b) the extra qualitative analysis step usually applied by experts, before embarking on detailed and quantitative means of solution. 17,18   Reif (1981, 1983) suggested further that in order for one to be able to solve problems one must have available: (a) a strategy for problem solving; (b) the right knowledge base, and (c) a good organization of the knowledge base. 18,19  

Chemistry problem solving followed suit providing its own heuristics. Pilot and co-workers proposed useful procedures that include the steps that characterize expert solvers. 20–22   They developed an ordered system of heuristics, which is applicable to quantitative problem solving in many fields of science and technology. In particular, they devised a “ Program of Actions and Methods ”, which consists of four phases, as follows: Phase 1, analysis of the problem; Phase 2, transformation of the problem; Phase 3, execution of routine operations; Phase 4, checking the answer and interpretation of the results. Genya proposed the use of “sequences” of problems of gradually increasing complexity , with qualitative problems being used at the beginning. 23  

Randles and Overton compared novice students with expert chemists in the approaches they used when solving open-ended problems. 24     Open-ended problems are defined as problems where not all the required data are given, where there is no one single possible strategy and where there is no single correct answer to the problem. It was found that: undergraduates adopted a greater number of novice-like approaches and produced poorer quality solutions; academics exhibited expert-like approaches and produced higher quality solutions; the approaches taken by industrial chemists were described as transitional.

Finally, one can justify the differences between novices and experts by employing the concept of working memory (see Chapter 5). Experienced learners can group ideas together to see much information as one ‘ chunk ’, while novice learners see all the separate pieces of information, causing an overload of working memory, which then cannot handle all the separate pieces at once. 25,26  

Chemistry is unique in the diversity of its problems, some of which, such as problems in physical and analytical chemistry, are similar to problems in physics, while others, such problems in stoichiometry, in organic chemistry (especially in reaction mechanisms and synthesis), and in the spectroscopic identification of compounds and of molecular structure, are idiosyncratic to chemistry. We will have more to say about stoichiometry and organic chemistry below, but before that there is a need to refer to three figures whom we consider the originators of the field of chemistry education research: the Americans J. Dudley Herron and Dorothy L. Gabel and the Scot Alex H. Johnstone, for it is not a coincidence that all three dealt with chemistry problem solving.

For Herron, successful problem solvers have a good command of basic facts and principles; construct appropriate representations; have general reasoning strategies that permit logical connections among elements of the problem; and apply a number of verification strategies to ensure that the representation of the problem is consistent with the facts given, the solution is logically sound, the computations are error-free, and the problem solved is the problem presented. 27–29   Gabel has also carried out fundamental work on problem solving in chemistry. 30   For instance, she determined students’ skills and concepts that are prerequisites for solving problems on moles, through the use of analog tasks, and identified specific conceptual and mathematical difficulties. 31   She also studied how problem categorization enhances problem solving achievement. 32   Finally, Johnstone studied the connection of problem solving ability in chemistry (but also in physics and biology) with working memory and information processing. We will deal extensively with his relevant work in this book (see Chapter 5). In the rest of this section, reference will be made to some further foundational research work on problem solving in chemistry.

Working with German 16-year-old students in 1988, Sumfleth found that the knowledge of chemical terms is a necessary but not sufficient prerequisite for successful problem solving in structure-properties relationships and in stoichiometry. 33   In the U.S. it was realized quite early (in 1984) that students often use algorithmic methods without understanding the relevant underlying concepts. 32   Indeed, Nakhleh and Mitchell confirmed later (1993) that little connection existed between algorithmic problem solving skills and conceptual understanding. 34   These authors provided ways to evaluate students along a continuum of low-high algorithmic and conceptual problem solving skills, and admitted that the lecture method teaches students to solve algorithms rather than teaching chemistry concepts. Gabel and Bunce also emphasized that students who have not sufficiently grasped the chemistry behind a problem tend to use a memorized formula, manipulate the formula and plug in numbers until they fit. 30   Niaz compared student performance on conceptual and computational problems of chemical equilibrium and reported that students who perform better on problems requiring conceptual understanding also perform significantly better on problems requiring manipulation of data, that is, computational problems; he further suggested that solving computational problems before conceptual problems would be more conducive to learning, so it is plausible to suggest that students’ ability to solve computational/algorithmic problems is an essential prerequisite for a “progressive transition” leading to a resolution of novel problems that require conceptual understanding. 35–37  

Stoichiometry problems are unique to chemistry and at the same time constitute a stumbling block for many students in introductory chemistry courses, with students often relying on algorithms. A review of some fundamental studies follows.

Hans-Jürgen Schmid carried out large scale studies in 1994 and 1997 in Germany and found that when working on easy-to-calculate problems students tended to invent/create a “non-mathematical” strategy of their own, but changed their strategy when moving from an easy-to-calculate problem to a more difficult one. 38,39   Swedish students were also found to behave in a similar manner. 40   A recent (2016) study with junior pre-service chemistry teachers in the Philippines reported that the most prominent strategy was the (algorithmic) mole method, while very few used the proportionality method and none the logical method. 41  

Lorenzo developed, implemented, and evaluated a useful problem solving heuristic in the case of quantitative problems on stoichiometry and solutions. 42   The heuristic works as a metacognitive tool by helping students to understand the steps involved in problem solving, and further to tackle problems in a systematic way. The approach guides students by means of logical reasoning to make a qualitative representation of the solution to a problem before undertaking calculations, thus using a ‘backwards strategy’.

The problem format can serve to make a problem easier or more difficult. A large scale study with 16-year-old students in the UK examined three stoichiometry problems in a number of ways. 43   In Test A the questions were presented as they had previously appeared on National School Examinations, while in Test B each of the questions on Test A was presented in a structured sequence of four parts. An example of one of the questions from both Test A and Test B is given below.

• Test A. Silver chloride (AgCl) is formed in the following reaction: AgNO 3 + HCl → AgCl + HNO 3 Calculate the maximum yield of solid silver chloride that can be obtained from reacting 25 cm 3 of 2.0 M hydrochloric acid with excess silver nitrate. (AgCl = 143.5)

Test B. Silver chloride (AgCl) is formed in the following reaction:

(a) How many moles of silver chloride can be made from 1 mole of hydrochloric acid?

(b) How many moles are there in 25 cm 3 of 2.0 M hydrochloric acid?

(c) How many moles of silver chloride can be made from the number of moles of acid in (b)?

(d) What is the mass of the number of moles of silver chloride in (c)? (AgCl = 143.5)

Student scores on Test B were significantly higher than those on Test A, both overall and on each of the individual questions, showing that structuring serves to make the questions easier.

Drummond and Selvaratnam examined students’ competence in intellectual strategies needed for solving chemistry problems. 44   They gave students problems in two forms, the ‘standard’ one and one with ‘hint’ questions that suggested the strategies which should be used to solve the problems. Although performance in all test items was poor, it improved for the ‘hint’ questions.

Finally, Gulacar and colleagues studied the differences in general cognitive abilities and domain specific skills of higher- and lower-achieving students in stoichiometry problems and in addition they proposed a novel code system for revealing sources of students’ difficulties with stoichiometry. 45,46   The latter topic is tackled in Chapter 4 by Gulacar, Cox, and Fynewever.

Stoichiometry problems have also a place in organic chemistry, but non-mathematical problem solving in organic chemistry is quite a different story. 47   Studying the mechanisms of organic reactions is a challenging activity. The spectroscopic identification of the structure of organic molecules also requires high expertise and a lot of experience. On the other hand, an organic synthesis problem can be complex and difficult for the students, because the number of pathways by which students could synthesise target substance “X” from starting substance “A” may be numerous. The problem is then very demanding in terms of information processing. In addition, students find it difficult to accept that one starting compound treated with only one set of reagents could lead to more than one correct product. A number of studies have dealt with organic synthesis. 48–50   The following comments from two students echo the difficulties faced by many students (pp. 209–210): 50  

“… having to do a synthesis problem is one of the more difficult things. Having to put everything together and sort of use your creativity, and knowing that I know everything solid to come up with a synthesis problem is difficult… it's just you can remember… you can use H 2 and nickel to add hydrogen to a bond but then there's like four other ways so if you're just looking for like what you react with, you can remember just that one but if you need five options just in case it's one of the other options that's given on the test… So, you have to know like multiple ways… and some things are used to maybe reduce… for example, something is used to reduce like a carboxylic acid and something else, the same thing, is used to reduce an aldehyde but then something else is used to like oxidize”.

Qualitative organic chemistry problems are dealt with in Chapters 6 and 7.

The present volume is the result of contributions from many experts in the field of chemistry education, with a clear focus on what can be identified as problem solving research. We are particularly fortunate that George Bodner , an authority in chemistry problem solving, has written the foreword to this book. (George has also published a review of research on problem solving in chemistry. 8   )

The book consists of eighteen chapters that cover many aspects of problem solving in chemistry and are organized under the following themes: (I) General issues in problem solving in chemistry education; (II) Problem solving in organic chemistry and biochemistry; (III) Chemistry problem solving in specific contexts; (IV) New technologies in problem solving in chemistry, and (V) New perspectives for problem solving in chemistry education. In the rest of this introductory chapter, I present a brief preview of the following contents.

The book starts with a discussion of qualitative reasoning in problem solving in chemistry. This type of reasoning helps us build inferences based on the analysis of qualitative values ( e.g. , high, low, weak, and strong) of the properties and behaviors of the components of a system, and the application of structure–property relationships. In Chapter 2, Talanquer summarizes core findings from research in chemistry education on the challenges that students face when engaging in this type of reasoning, and the strategies that support their learning in this area.

For Graulich, Langner, Vo , and Yuriev (Chapter 3), chemical problem solving relies on conceptual knowledge and the deployment of metacognitive problem solving processes, but novice problem solvers often grapple with both challenges simultaneously. Multiple scaffolding approaches have been developed to support student problem solving, often designed to address specific aspects or content area. The authors present a continuum of scaffolding so that a blending of prompts can be used to achieve specific goals. Providing students with opportunities to reflect on the problem solving work of others – peers or experts – can also be of benefit in deepening students’ conceptual reasoning skills.

A central theme in Gulacar, Cox and Fynewever 's chapter (Chapter 4) is the multitude of ways in which students can be unsuccessful when trying to solve problems. Each step of a multi-step problem can be labeled as a subproblem and represents content that students need to understand and use to be successful with the problem. The authors have developed a set of codes to categorize each student's attempted solution for every subproblem as either successful or not, and if unsuccessful, identifying why, thus providing a better understanding of common barriers to success, illustrated in the context of stoichiometry.

In Chapter 5, Tsaparlis re-examines the “working memory overload hypothesis” and associated with it the Johnstone–El Banna predictive model of problem solving. This famous predictive model is based on the effect of information processing, especially of working-memory capacity on problem solving. Other factors include mental capacity or M -capacity, degree of field dependence/independence, and developmental level/scientific reasoning. The Johnstone–El Banna model is re-examined and situations are explored where the model is valid, but also its limitations. A further examination of the role of the above cognitive factors in problem solving in chemistry is also made.

Proposing reaction mechanisms using the electron-pushing formalism, which is central to the practice and teaching of organic chemistry, is the subject of Chapter 6 by Bahttacharyya . The author argues that MR (Mechanistic Reasoning) using the EPF (Electron-Pushing Formalism) incorporates several other forms of reasoning, and is also considered as a useful transferrable skill for the biomedical sciences and allied fields.

Flynn considers synthesis problems as among the most challenging questions for students in organic chemistry courses. In Chapter 7, she describes the strategies used by students who have been successful in solving synthetic problems. Associated classroom and problem set activities are also described.

We all know that the determination of chemical identity is a fundamental chemistry practice that now depends almost exclusively on the characterization of molecular structure through spectroscopic analysis. This analysis is a day-to-day task for practicing organic chemists, and instruction in modern organic chemistry aims to cultivate such expertise. Accordingly, in Chapter 8, Connor and Shultz review studies that have investigated reasoning and problem solving approaches used to evaluate NMR and IR spectroscopic data for organic structural determination, and they provide a foundation for understanding how this problem solving expertise develops and how instruction may facilitate such learning. The aim is to present the current state of research, empirical insights into teaching and learning this practice, and trends in instructional innovations.

The idea that variation exists within a system and the varied population schema described by Talanquer are the theoretical tools for the study by Rodriguez, Hux, Philips, and Towns , which is reported in Chapter 9. The subject of the study is chemical kinetics in biochemistry, and especially of the action and mechanisms of inhibition agents in enzyme catalysis, where a sophisticated understanding requires students to learn to reason using probability-based reasoning.

In Chapter 10, Phelps, Hawkins and Hunter consider the purpose of the academic chemistry laboratory, with emphasis on the practice of problem solving skills beyond those of an algorithmic mathematical nature. The purpose represents a departure from the procedural skills training often associated with the reason we engage in laboratory work (learning to titrate for example). While technical skills are of course important, if part of what we are doing in undergraduate chemistry courses is to prepare students to go on to undertake research, somewhere in the curriculum there should be opportunities to practice solving problems that are both open-ended and laboratory-based. The history of academic chemistry laboratory practice is reviewed and its current state considered.

Chapter 11 by Broman focuses on chemistry problems and problem solving by employing context-based learning approaches, where open-ended problems focusing on higher-order thinking are explored. Chemistry teachers suggested contexts that they thought their students would find interesting and relevant, e.g. , chocolate, doping, and dietary supplements. The chapter analyses students’ interviews after they worked with the problems and discusses how to enhance student interest and perceived relevance in chemistry, and how students’ learning can be improved.

Team Based Learning (TBL) is the theme of Chapter 12 by Capel, Hancock, Howe, Jones, Phillips , and Plana . TBL is a structured small group collaborative form of learning, where learners are required to prepare for sessions in advance, then discuss and debate potential solutions to problems with their peers. It has been found to be highly effective at facilitating active learning. The authors describe their experience with embedding TBL into their chemistry curricula at all levels, including a transnational degree program with a Chinese university.

The ability of students to learn and value aspects of the chemistry curriculum that delve into the molecular basis of chemical events relies on the use of models/molecular representations, and enhanced awareness of how these models connect to chemical observations. Molecular representations in chemistry is the topic of Chapter 13 by Polifka, Baluyut and Holme , which focuses on technology solutions that enhance student understanding and learning of these conceptual aspects of chemistry.

In Chapter 14, Limniou, Papadopoulos, Gavril, Touni , and Chatziapostolidou present an IR spectra simulation. The software includes a wide range of chemical compounds supported by real IR spectra, allowing students to learn how to interpret an IR spectrum, via a step by step process. The chapter includes a report on a pilot trial with a small-scale face-to-face learning environment. The software is available on the Internet for everyone to download and use.

In Chapter 15, Sigalas explores chemistry problems with computational quantum chemistry tools in the undergraduate chemistry curriculum, the use of computational chemistry for the study of chemical phenomena, and the prediction and interpretation of experimental data from thermodynamics and isomerism to reaction mechanisms and spectroscopy. The pros and cons of a series of software tools for building molecular models, preparation of input data for standard software, and visualization of computational results are discussed.

In Chapter 16, Stamovlasis and Vaiopoulou address methodological and epistemological issues concerning research in chemistry problem solving. Following a short review of the relevant literature with emphasis on methodology and the statistical modeling used, the weak points of the traditional approaches are discussed and a novel epistemological framework based on complex dynamical system theory is described. Notably, research using catastrophe theory provides empirical evidence for these phenomena by modeling and explaining mental overload effects and students’ failures. Examples of the application of this theory to chemistry problem solving is reviewed.

Chapter 17 provides extended summaries of the chapters, including a commentary on the chapters. The chapter also provides a brief coverage of various important issues and topics related to chemistry problem solving that are not covered by other chapters in the book.

Finally, in Chapter 18, a Postscript address two specific problem solving issues: (a) the potential synergy between higher and lower-order thinking skills (HOTS and LOTS,) and (b) When problem solving might descend to chaos dynamics. The synergy between HOTS and LOTS is demonstrated by looking at the contribution of chemistry and biochemistry to overcoming the current coronavirus (COVID-19) pandemic. One the other hand, chaos theory provides an analogy with the time span of the predictive power of problem solving models.

«Ἀρχὴ παιδεύσεως ἡ τῶν ὀνομάτων ἐπίσκεψις» (Archē paedeuseōs hē tōn onomatōn episkepsis). By Antisthenes (ancient Greek philosopher), translated by W. A. Oldfather (1925).

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• Research Matters — to the Science Teacher

Problem Solving in Chemistry

One of the major difficulties in teaching introductory chemistry courses is helping students become efficient problem solvers. Most beginning chemistry students find this one of the most difficulty aspects of the introductory chemistry course. What does research tell us about problem solving in chemistry? Just why do students have such difficulty in solving chemistry problems? Are some ways of teaching students to solve problems more effective than others? Problem solving in any area is a very complex process. It involves an understanding of the language in which the problem is stated, the interpretation of what is given in the problem and what is sought, an understanding of the science concepts involved in the solution, and the ability to perform mathematical operations if these are involved in the problem. The first requirement for successful problem solving is that the problem solver understand the meaning of the problem. In order to do so there must be an understanding of the vocabulary and its usage in the problem. There are two types of words that occur in problems, ordinary words that science teachers generally assume that students know and more technical terms that require understanding of concepts specific to the discipline. Researchers have found that many students do not know the meaning of common words such as contrast, displace, diversity, factor, fundamental, incident, negligible, relevant, relative, spontaneous and valid. Slight changes in the way a problem is worded may make a difference in whether a students is able to solve it correctly. For example, when "least" is changed to "most" in a problem, the percentage getting the question correct may increase by 25%. Similar improvements occur for changing negative to positive forms, for rewording long and complex questions, and for changing from the passive to the active voice. Although teachers would like students to solve problems in whatever way they are framed they must be cognizant of the fact that these subtle changes will make a difference in students' success in solving problems. From several research studies on problem solving in chemistry, it is clear that the major reason why students are unable to solve problems is that they do not understand the concepts on which the problems are based. Studies that compare the procedures used by students who are inexperienced in solving problems with experts show that experts were able to retrieve relevant concepts more readily from their long term memory. Studies have also shown that experts concepts are linked to one another in a network. Experts spend a considerable period of time planning the strategy that will be used to solve the problem whereas novices jump right in using a formula or trying to apply an algorithm. In the past few years, science educators have been trying to determine which science concepts students understand and which they do not. Because chemistry is concerned with the nature of matter, and matter is defined as anything that has mass and volume, students must understand these concepts to be successful problem solvers in chemistry. Research studies have shown that a surprising number of high school students do not understand the meaning of mass, volume, heat, temperature and changes of state. One reason why students do not understand these concepts is because when they have been taught in the classroom, they have not been presented in a variety of contexts. Often the instruction has been verbal and formal. This will be minimally effective if students have not had the concrete experiences. Hence, misconceptions arise. Although the very word "misconception" has a negative connotation, this information is important for chemistry teachers. They are frameworks by which the students view the world around them. If a teacher understands these frameworks, then instruction can be formulated that builds on student's existing knowledge. It appears that students build conceptual frameworks as they try to make sense out of their surroundings. In addition to the fundamental properties of matter mentioned above, there are other concepts that are critical to chemical calculations. One of these is the mole concept and another is the particulate nature of matter. There is mounting evidence that many students do not understand either of these concepts sufficiently well to use them in problem solving. It appears that if chemistry problem solving skills of students are to improve, chemistry teachers will need to spend a much greater period of time on concept acquisition. One way to do this will be to present concepts in a variety of contexts, using hands-on activities.

What does this research imply about procedures that are useful for helping students become more successful at problem solving?

Chemistry problems can be solved using a variety of techniques. Many chemistry teachers and most introductory chemistry texts illustrate problem solutions using the factor-label method. It has been shown that this is not the best technique for high school students of high mathematics anxiety and low proportional reasoning ability. The use of analogies and schematic diagrams results in higher achievement on problems involving moles, stoichiometry, and molarity. The use of analogs is not profitable for certain types of problems. When problems became complex (such as in dilution problems) students are unable to solve even the analog problems. For these types of problems, using analogs in instruction would be useless unless teachers are willing to spend additional time teaching students how to solve problems using the analog. Many students are unable to match analogs with the chemistry problems even after practice in using analogs. Students need considerable practice if analogs are used in instruction. When teaching chemistry by the lecture method, concept development needed for problem solving may be enhanced by pausing for a two minute interval at about 8 to 12 minute intervals during the lecture. This provides students time to review what has been presented, fill in the gaps, and interpret the information for others, and thus learn it themselves. The use of concept maps may also help students understand concepts and to relate them to one another. Requiring students to use a worksheet with each problem may help them solve them in a more effective way. The worksheet might include a place for them to plan a problem, that is list what is given and what is sought; to describe the problem situation by writing down other concepts they retrieve from memory (the use of a picture may integrate these); to find the mathematical solution; and to appraise their results. Although the research findings are not definitive, the above approaches offer some promise that students' problem solving skills can be improved and that they can learn to solve problems in a meaningful way.

For further information about this research area, please contact:

Dr. Dorothy Gabel Education Building 3rd and Jordan Bloomington, Indiana 47405

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Graduate Doctoral Dissertations

The activity of abstraction in physical chemistry problem solving and instruction.

Jessica M. Karch , University of Massachusetts Boston Follow

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Doctor of Philosophy (PhD)

Chemistry/Chemistry Education Research

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Hannah Sevian

Second Advisor

Jason Green

Third Advisor

Jonathan Rochford

Productive problem solving, concept construction, and sense making occur through the core process of abstraction. Although the capacity for domain-general abstraction is developed at a young age, the role of abstraction in increasingly complex and disciplinary environments, such as those encountered in undergraduate STEM education, is not well understood. Undergraduate physical chemistry relies particularly heavily on abstraction because it uses many overlapping and imperfect mathematical models to represent and interpret phenomena occurring on multiple scales. To reconcile these models, extract meaning from them, and recognize when to apply them in problem solving requires processes of abstraction. This dissertation aims to develop a framework that can be used to make abstraction in physical chemistry visible to better understand how undergraduate physical chemistry students navigate these processes and abstract in problem solving scenarios.

Using an activity theoretical lens, this dissertation has three aims: (1) to operationalize abstraction as a series of epistemic actions, and to use this operationalization to investigate (2) what motivates and influences whether abstraction is realized in the moment, and (3) the role abstraction plays in physical chemistry instruction. First, problem solving teaching interviews with individuals and pairs (n=18) on thermodynamics and kinetics topics are analyzed using a constant comparative approach. The resulting Epistemic Actions of Abstraction framework characterizes eight epistemic actions along two dimensions: increasing abstractness relative to the context (concretizing, manipulating, restructuring, and generalizing) and nature of the object the action operates on (conceptual or symbolic). These teaching interviews are then inductively analyzed to identify what sparks abstraction and the influence of interaction on abstraction. Three types of needs (task-directed, situational-insufficient, and situational-emergent), and three major themes (framing, interviewer intervention, and peer interaction) are found. Finally, a multiple-case study of physical chemistry instructors (n=2) at two different institutions is conducted to investigate how and why instructors model abstraction. Analysis of classroom video and video-stimulated recall interviews yields two identified roles abstraction plays in physical chemistry instruction: developing mathematical tools grounded in conceptual understanding, and developing conceptual understanding grounded in mathematics. Implications for research and teaching are discussed.

Recommended Citation

Karch, Jessica M., "The Activity of Abstraction in Physical Chemistry Problem Solving and Instruction" (2021). Graduate Doctoral Dissertations . 692. https://scholarworks.umb.edu/doctoral_dissertations/692

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Studying the student’s perceptions of engagement and problem-solving skills for academic achievement in chemistry at the higher secondary level

• Published: 29 August 2023
• Volume 29 , pages 8347–8368, ( 2024 )

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• Sankar E. 1 &
• A. Edward William Benjamin 1  

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Student engagement has emerged as a crucial factor in higher education, playing a vital role in shaping the overall quality of learning outcomes. It refers to the active involvement and participation of students in specific activities that research has consistently linked to improved academic achievements. The pervasiveness of the term ‘student engagement’ has significantly shaped the higher education landscape, reinforcing its importance in fostering effective learning environments. In the realm of higher education, educators are continuously exploring diverse pedagogical approaches to enhance student engagement through active learning. This study focuses on the problem-solving learning model and its implementation to foster a deeper understanding of student engagement, including their positive behaviour, participation in activities, and cognitive capabilities. In this study, a quasi-experimental design was employed, incorporating pre-test, post-test, and non-equivalent control group elements. This specific design was chosen due to the constraints of randomly assigning students to groups. Instead, intact classes were randomly selected and assigned to either the control or experimental groups. The sample study was 476 higher secondary-level chemistry students collected from different higher secondary schools. A multi-stage sampling technique was used to select schools from the target population. Initially, schools were selected using a purposive sampling technique, focusing on those with fully equipped chemistry laboratories and qualified chemistry teachers. Additionally, consideration was given to including both female and male students in co-educational chemistry classes, as gender was considered a relevant variable for the study. This study adopts a quasi-experimental design, utilizing an achievement and retention test in chemistry as its primary instrument. The validity of this instrument was ensured through face validation by three expert evaluators. To eliminate the errors of non-equivalence arising from the non-randomization of the research subjects, the analysis of covariance (ANOVA) was used in analysing the data and to remove the error of initial differences in ability levels among the research subjects. The findings of the study demonstrated that students in the experimental group experienced a notable increase in problem-solving success compared to their counterparts in the control group, a difference that became evident right from the first intervention. This study establishes a positive correlation between student engagement and their learning outcomes, indicating that higher engagement leads to better academic performance. Additionally, it observes that the correlation between boys’ and girls’ problem-solving skills and their learning outcomes is comparatively weaker, suggesting potential variations in how problem-solving abilities impact academic achievement among genders. It also reveals that there is a positive influence on student engagement and problem-solving skills in students’ academic achievement. Despite the challenges encountered, the results demonstrated the vital role of the problem-solving learning model, when coupled with student engagement, in fostering students’ critical thinking skills concerning reaction rate material. These instructional practices were observed to foster higher levels of student engagement, ultimately resulting in enhanced academic achievement among students.

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E., S., Benjamin, A.E.W. Studying the student’s perceptions of engagement and problem-solving skills for academic achievement in chemistry at the higher secondary level. Educ Inf Technol 29 , 8347–8368 (2024). https://doi.org/10.1007/s10639-023-12165-x

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Mysterious element promethium finally reveals its chemical properties

The highly unstable radioactive element promethium is hard to study in the lab, but chemists have now coaxed it into forming a compound in water so they can observe its bonding behaviour

By Alex Wilkins

22 May 2024

Conceptual art showing a compound of promethium

Jacquelyn DeMink, art; Thomas Dyke/photography; ORNL, UA.S. Dept. of Energy

A new compound containing one of the rarest elements in the world, promethium, has revealed its mysterious properties for the first time.

Promethium only exists naturally in minuscule amounts – Earth’s crust contains just about half a kilogram of the element. In 1945, researchers at Oak Ridge National Laboratory in Tennessee managed to produce it as a byproduct of the Manhattan Project’s plutonium enrichment programme. Its nuclear origins led to its name, after the Greek titan Prometheus, who stole fire and brought it to humans.

It is now routinely produced, albeit in tiny quantities, from the radioactive decay of uranium and can be incorporated in simple compounds for uses like luminous paint or nuclear batteries. But its extremely radioactive nature means it is inherently unstable, making it difficult to form long-lasting compounds that are easy to study. The crystal structures that it does exist in also exert forces on promethium’s chemical bonds, obscuring its fundamental chemistry, such as how long its atomic bonds are and how they form with other compounds.

Now, Alexander Ivanov at Oak Ridge National Laboratory and his colleagues have found a way to form a promethium compound in water. This dampens some of the damaging effects of radioactivity and avoids the obscuring effects of crystal structures, allowing the team to study the element’s chemistry in detail for the first time.

First, they synthesised a compound called bispyrrolidine diglycolamide (PyDGA), which is based on molecules that form compounds with elements similar to promethium. When promethium was added to this molecule in a solution, it formed the compound Pm-PyDGA, which has a bright pink colour due to its electron structure.

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Ivanov and his team then fired X-rays at the compound and measured which frequencies it absorbed, revealing how the promethium was chemically bonded. This showed that the bond length between promethium and nearby oxygen atoms was about a quarter of a nanometre, which matched computer simulations they had run.

“It’s rather beautiful chemistry, and to see the delicate pink colour of this complex is a real joy,” says Andrea Sella at University College London.

Information about promethium’s bonding behaviour will help improve processes for producing purer samples in larger quantities from radioactive waste, says Ivanov, and could also be used to design new medical compounds, such as for radioactive imaging or cancer treatment. “This kind of fundamental information could help us to drive new technologies,” he says.

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Nature DOI: 10.1038/s41586-024-07267-6

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Wavefunction matching for solving quantum many-body problems

Strongly interacting systems play an important role in quantum physics and quantum chemistry. Stochastic methods such as Monte Carlo simulations are a proven method for investigating such systems. However, these methods reach their limits when so-called sign oscillations occur. This problem has now been solved by an international team of researchers from Germany, Turkey, the USA, China, South Korea and France using the new method of wavefunction matching. As an example, the masses and radii of all nuclei up to mass number 50 were calculated using this method. The results agree with the measurements, the researchers now report in the journal " Nature ."

All matter on Earth consists of tiny particles known as atoms. Each atom contains even smaller particles: protons, neutrons and electrons. Each of these particles follows the rules of quantum mechanics. Quantum mechanics forms the basis of quantum many-body theory, which describes systems with many particles, such as atomic nuclei.

One class of methods used by nuclear physicists to study atomic nuclei is the ab initio approach. It describes complex systems by starting from a description of their elementary components and their interactions. In the case of nuclear physics, the elementary components are protons and neutrons. Some key questions that ab initio calculations can help answer are the binding energies and properties of atomic nuclei and the link between nuclear structure and the underlying interactions between protons and neutrons.

However, these ab initio methods have difficulties in performing reliable calculations for systems with complex interactions. One of these methods is quantum Monte Carlo simulations. Here, quantities are calculated using random or stochastic processes. Although quantum Monte Carlo simulations can be efficient and powerful, they have a significant weakness: the sign problem. It arises in processes with positive and negative weights, which cancel each other. This cancellation leads to inaccurate final predictions.

A new approach, known as wavefunction matching, is intended to help solve such calculation problems for ab initio methods. "This problem is solved by the new method of wavefunction matching by mapping the complicated problem in a first approximation to a simple model system that does not have such sign oscillations and then treating the differences in perturbation theory," says Prof. Ulf-G. Meißner from the Helmholtz Institute for Radiation and Nuclear Physics at the University of Bonn and from the Institute of Nuclear Physics and the Center for Advanced Simulation and Analytics at Forschungszentrum Jülich. "As an example, the masses and radii of all nuclei up to mass number 50 were calculated -- and the results agree with the measurements," reports Meißner, who is also a member of the Transdisciplinary Research Areas "Modeling" and "Matter" at the University of Bonn.

"In quantum many-body theory, we are often faced with the situation that we can perform calculations using a simple approximate interaction, but realistic high-fidelity interactions cause severe computational problems," says Dean Lee, Professor of Physics from the Facility for Rare Istope Beams and Department of Physics and Astronomy (FRIB) at Michigan State University and head of the Department of Theoretical Nuclear Sciences.

Wavefunction matching solves this problem by removing the short-distance part of the high-fidelity interaction and replacing it with the short-distance part of an easily calculable interaction. This transformation is done in a way that preserves all the important properties of the original realistic interaction. Since the new wavefunctions are similar to those of the easily computable interaction, the researchers can now perform calculations with the easily computable interaction and apply a standard procedure for handling small corrections -- called perturbation theory.

The research team applied this new method to lattice quantum Monte Carlo simulations for light nuclei, medium-mass nuclei, neutron matter and nuclear matter. Using precise ab initio calculations, the results closely matched real-world data on nuclear properties such as size, structure and binding energy. Calculations that were once impossible due to the sign problem can now be performed with wavefunction matching.

While the research team focused exclusively on quantum Monte Carlo simulations, wavefunction matching should be useful for many different ab initio approaches. "This method can be used in both classical computing and quantum computing, for example to better predict the properties of so-called topological materials, which are important for quantum computing," says Meißner.

The first author is Prof. Dr. Serdar Elhatisari, who worked for two years as a Fellow in Prof. Meißner's ERC Advanced Grant EXOTIC. According to Meißner, a large part of the work was carried out during this time. Part of the computing time on supercomputers at Forschungszentrum Jülich was provided by the IAS-4 institute, which Meißner heads.

• Quantum Computers
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Materials provided by University of Bonn . Note: Content may be edited for style and length.

Journal Reference :

• Serdar Elhatisari, Lukas Bovermann, Yuan-Zhuo Ma, Evgeny Epelbaum, Dillon Frame, Fabian Hildenbrand, Myungkuk Kim, Youngman Kim, Hermann Krebs, Timo A. Lähde, Dean Lee, Ning Li, Bing-Nan Lu, Ulf-G. Meißner, Gautam Rupak, Shihang Shen, Young-Ho Song, Gianluca Stellin. Wavefunction matching for solving quantum many-body problems . Nature , 2024; DOI: 10.1038/s41586-024-07422-z

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The Algebra Problem: How Middle School Math Became a National Flashpoint

Top students can benefit greatly by being offered the subject early. But many districts offer few Black and Latino eighth graders a chance to study it.

By Troy Closson

From suburbs in the Northeast to major cities on the West Coast, a surprising subject is prompting ballot measures, lawsuits and bitter fights among parents: algebra.

Students have been required for decades to learn to solve for the variable x, and to find the slope of a line. Most complete the course in their first year of high school. But top-achievers are sometimes allowed to enroll earlier, typically in eighth grade.

The dual pathways inspire some of the most fiery debates over equity and academic opportunity in American education.

Do bias and inequality keep Black and Latino children off the fast track? Should middle schools eliminate algebra to level the playing field? What if standout pupils lose the chance to challenge themselves?

The questions are so fraught because algebra functions as a crucial crossroads in the education system. Students who fail it are far less likely to graduate. Those who take it early can take calculus by 12th grade, giving them a potential edge when applying to elite universities and lifting them toward society’s most high-status and lucrative professions.

But racial and economic gaps in math achievement are wide in the United States, and grew wider during the pandemic. In some states, nearly four in five poor children do not meet math standards.

To close those gaps, New York City’s previous mayor, Bill de Blasio, adopted a goal embraced by many districts elsewhere. Every middle school would offer algebra, and principals could opt to enroll all of their eighth graders in the class. San Francisco took an opposite approach: If some children could not reach algebra by middle school, no one would be allowed to take it.

The central mission in both cities was to help disadvantaged students. But solving the algebra dilemma can be more complex than solving the quadratic formula.

New York’s dream of “algebra for all” was never fully realized, and Mayor Eric Adams’s administration changed the goal to improving outcomes for ninth graders taking algebra. In San Francisco, dismantling middle-school algebra did little to end racial inequities among students in advanced math classes. After a huge public outcry, the district decided to reverse course.

“You wouldn’t think that there could be a more boring topic in the world,” said Thurston Domina, a professor at the University of North Carolina. “And yet, it’s this place of incredibly high passions.”

“Things run hot,” he said.

In some cities, disputes over algebra have been so intense that parents have sued school districts, protested outside mayors’ offices and campaigned for the ouster of school board members.

Teaching math in middle school is a challenge for educators in part because that is when the material becomes more complex, with students moving from multiplication tables to equations and abstract concepts. Students who have not mastered the basic skills can quickly become lost, and it can be difficult for them to catch up.

Many school districts have traditionally responded to divergent achievement levels by simply separating children into distinct pathways, placing some in general math classes while offering others algebra as an accelerated option. Such sorting, known as tracking, appeals to parents who want their children to reach advanced math as quickly as possible.

But tracking has cast an uncomfortable spotlight on inequality. Around a quarter of all students in the United States take algebra in middle school. But only about 12 percent of Black and Latino eighth graders do, compared with roughly 24 percent of white pupils, a federal report found .

“That’s why middle school math is this flashpoint,” said Joshua Goodman, an associate professor of education and economics at Boston University. “It’s the first moment where you potentially make it very obvious and explicit that there are knowledge gaps opening up.”

In the decades-long war over math, San Francisco has emerged as a prominent battleground.

California once required that all eighth graders take algebra. But lower-performing middle school students often struggle when forced to enroll in the class, research shows. San Francisco later stopped offering the class in eighth grade. But the ban did little to close achievement gaps in more advanced math classes, recent research has found.

As the pendulum swung, the only constant was anger. Leading Bay Area academics disparaged one another’s research . A group of parents even sued the district last spring. “Denying students the opportunity to skip ahead in math when their intellectual ability clearly allows for it greatly harms their potential for future achievement,” their lawsuit said.

The city is now back to where it began: Middle school algebra — for some, not necessarily for all — will return in August. The experience underscored how every approach carries risks.

“Schools really don’t know what to do,” said Jon R. Star, an educational psychologist at Harvard who has studied algebra education. “And it’s just leading to a lot of tension.”

In Cambridge, Mass., the school district phased out middle school algebra before the pandemic. But some argued that the move had backfired: Families who could afford to simply paid for their children to take accelerated math outside of school.

“It’s the worst of all possible worlds for equity,” Jacob Barandes, a Cambridge parent, said at a school board meeting.

Elsewhere, many students lack options to take the class early: One of Philadelphia’s most prestigious high schools requires students to pass algebra before enrolling, preventing many low-income children from applying because they attend middle schools that do not offer the class.

In New York, Mr. de Blasio sought to tackle the disparities when he announced a plan in 2015 to offer algebra — but not require it — in all of the city’s middle schools. More than 15,000 eighth graders did not have the class at their schools at the time.

Since then, the number of middle schools that offer algebra has risen to about 80 percent from 60 percent. But white and Asian American students still pass state algebra tests at higher rates than their peers.

The city’s current schools chancellor, David Banks, also shifted the system’s algebra focus to high schools, requiring the same ninth-grade curriculum at many schools in a move that has won both support and backlash from educators.

And some New York City families are still worried about middle school. A group of parent leaders in Manhattan recently asked the district to create more accelerated math options before high school, saying that many young students must seek out higher-level instruction outside the public school system.

In a vast district like New York — where some schools are filled with children from well-off families and others mainly educate homeless children — the challenge in math education can be that “incredible diversity,” said Pedro A. Noguera, the dean of the University of Southern California’s Rossier School of Education.

“You have some kids who are ready for algebra in fourth grade, and they should not be denied it,” Mr. Noguera said. “Others are still struggling with arithmetic in high school, and they need support.”

Many schools are unequipped to teach children with disparate math skills in a single classroom. Some educators lack the training they need to help students who have fallen behind, while also challenging those working at grade level or beyond.

Some schools have tried to find ways to tackle the issue on their own. KIPP charter schools in New York have added an additional half-hour of math time to many students’ schedules, to give children more time for practice and support so they can be ready for algebra by eighth grade.

At Middle School 50 in Brooklyn, where all eighth graders take algebra, teachers rewrote lesson plans for sixth- and seventh-grade students to lay the groundwork for the class.

The school’s principal, Ben Honoroff, said he expected that some students would have to retake the class in high school. But after starting a small algebra pilot program a few years ago, he came to believe that exposing children early could benefit everyone — as long as students came into it well prepared.

Looking around at the students who were not enrolling in the class, Mr. Honoroff said, “we asked, ‘Are there other kids that would excel in this?’”

“The answer was 100 percent, yes,” he added. “That was not something that I could live with.”

Troy Closson reports on K-12 schools in New York City for The Times. More about Troy Closson

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6.1.1: Practice Problems- Solution Concentration

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PROBLEM \(\PageIndex{1}\)

Explain what changes and what stays the same when 1.00 L of a solution of NaCl is diluted to 1.80 L.

The number of moles always stays the same in a dilution.

The concentration and the volumes change in a dilution.

PROBLEM \(\PageIndex{2}\)

What does it mean when we say that a 200-mL sample and a 400-mL sample of a solution of salt have the same molarity? In what ways are the two samples identical? In what ways are these two samples different?

The two samples contain the same proportion of moles of salt to liters of solution, but have different numbers of actual moles.

PROBLEM \(\PageIndex{3}\)

Determine the molarity for each of the following solutions:

• 0.444 mol of CoCl 2 in 0.654 L of solution
• 98.0 g of phosphoric acid, H 3 PO 4 , in 1.00 L of solution
• 0.2074 g of calcium hydroxide, Ca(OH) 2 , in 40.00 mL of solution
• 10.5 kg of Na 2 SO 4 ·10H 2 O in 18.60 L of solution
• 7.0 × 10 −3 mol of I 2 in 100.0 mL of solution
• 1.8 × 10 4 mg of HCl in 0.075 L of solution

PROBLEM \(\PageIndex{4}\)

Determine the molarity of each of the following solutions:

• 1.457 mol KCl in 1.500 L of solution
• 0.515 g of H 2 SO 4 in 1.00 L of solution
• 20.54 g of Al(NO 3 ) 3 in 1575 mL of solution
• 2.76 kg of CuSO 4 ·5H 2 O in 1.45 L of solution
• 0.005653 mol of Br 2 in 10.00 mL of solution
• 0.000889 g of glycine, C 2 H 5 NO 2 , in 1.05 mL of solution

5.25 × 10 -3 M

6.122 × 10 -2 M

1.13 × 10 -2 M

PROBLEM \(\PageIndex{5}\)

Calculate the number of moles and the mass of the solute in each of the following solutions:

(a) 2.00 L of 18.5 M H 2 SO 4 , concentrated sulfuric acid (b) 100.0 mL of 3.8 × 10 −5 M NaCN, the minimum lethal concentration of sodium cyanide in blood serum (c) 5.50 L of 13.3 M H 2 CO, the formaldehyde used to “fix” tissue samples (d) 325 mL of 1.8 × 10 −6 M FeSO 4 , the minimum concentration of iron sulfate detectable by taste in drinking water

37.0 mol H 2 SO 4

3.63 × 10 3 g H 2 SO 4

3.8 × 10 −6 mol NaCN

1.9 × 10 −4 g NaCN

73.2 mol H 2 CO

2.20 kg H 2 CO

5.9 × 10 −7 mol FeSO 4

8.9 × 10 −5 g FeSO 4

PROBLEM \(\PageIndex{6}\)

Calculate the molarity of each of the following solutions:

(a) 0.195 g of cholesterol, C 27 H 46 O, in 0.100 L of serum, the average concentration of cholesterol in human serum (b) 4.25 g of NH 3 in 0.500 L of solution, the concentration of NH 3 in household ammonia (c) 1.49 kg of isopropyl alcohol, C 3 H 7 OH, in 2.50 L of solution, the concentration of isopropyl alcohol in rubbing alcohol (d) 0.029 g of I 2 in 0.100 L of solution, the solubility of I 2 in water at 20 °C

5.04 × 10 −3 M

1.1 × 10 −3 M

PROBLEM \(\PageIndex{7}\)

There is about 1.0 g of calcium, as Ca 2+ , in 1.0 L of milk. What is the molarity of Ca 2+ in milk?

PROBLEM \(\PageIndex{8}\)

What volume of a 1.00- M Fe(NO 3 ) 3 solution can be diluted to prepare 1.00 L of a solution with a concentration of 0.250 M ?

PROBLEM \(\PageIndex{9}\)

If 0.1718 L of a 0.3556- M C 3 H 7 OH solution is diluted to a concentration of 0.1222 M , what is the volume of the resulting solution?

PROBLEM \(\PageIndex{10}\)

What volume of a 0.33- M C 12 H 22 O 11 solution can be diluted to prepare 25 mL of a solution with a concentration of 0.025 M ?

PROBLEM \(\PageIndex{11}\)

What is the concentration of the NaCl solution that results when 0.150 L of a 0.556- M solution is allowed to evaporate until the volume is reduced to 0.105 L?

PROBLEM \(\PageIndex{12}\)

What is the molarity of the diluted solution when each of the following solutions is diluted to the given final volume?

• 1.00 L of a 0.250- M solution of Fe(NO 3 ) 3 is diluted to a final volume of 2.00 L
• 0.5000 L of a 0.1222- M solution of C 3 H 7 OH is diluted to a final volume of 1.250 L
• 2.35 L of a 0.350- M solution of H 3 PO 4 is diluted to a final volume of 4.00 L
• 22.50 mL of a 0.025- M solution of C 12 H 22 O 11 is diluted to 100.0 mL

PROBLEM \(\PageIndex{13}\)

What is the final concentration of the solution produced when 225.5 mL of a 0.09988- M solution of Na 2 CO 3 is allowed to evaporate until the solution volume is reduced to 45.00 mL?

PROBLEM \(\PageIndex{14}\)

A 2.00-L bottle of a solution of concentrated HCl was purchased for the general chemistry laboratory. The solution contained 868.8 g of HCl. What is the molarity of the solution?

PROBLEM \(\PageIndex{15}\)

An experiment in a general chemistry laboratory calls for a 2.00- M solution of HCl. How many mL of 11.9 M HCl would be required to make 250 mL of 2.00 M HCl?

PROBLEM \(\PageIndex{16}\)

What volume of a 0.20- M K 2 SO 4 solution contains 57 g of K 2 SO 4 ?

PROBLEM \(\PageIndex{17}\)

The US Environmental Protection Agency (EPA) places limits on the quantities of toxic substances that may be discharged into the sewer system. Limits have been established for a variety of substances, including hexavalent chromium , which is limited to 0.50 mg/L. If an industry is discharging hexavalent chromium as potassium dichromate (K 2 Cr 2 O 7 ), what is the maximum permissible molarity of that substance?

4.8 × 10 −6 M

Contributors

Paul Flowers (University of North Carolina - Pembroke), Klaus Theopold (University of Delaware) and Richard Langley (Stephen F. Austin State University) with contributing authors.  Textbook content produced by OpenStax College is licensed under a Creative Commons Attribution License 4.0 license. Download for free at http://cnx.org/contents/[email protected] ).

• Adelaide Clark, Oregon Institute of Technology