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Forests provide vital ecosystem services crucial to human well-being and sustainable development, and have an important role to play in achieving the seventeen Sustainable Development Goals (SDGs) of the United Nations 2030 Agenda. Little attention, however, has yet focused on how efforts to achieve the SDGs will impact forests and forest-related livelihoods, and how these impacts may, in turn, enhance or undermine the contributions of forests to climate and development. This book discusses the conditions that influence how SDGs are implemented and prioritised, and provides a systematic, multidisciplinary global assessment of interlinkages among the SDGs and their targets, increasing understanding of potential synergies and unavoidable trade-offs between goals. Ideal for academic researchers, students and decision-makers interested in sustainable development in the context of forests, this book will provide invaluable knowledge for efforts undertaken to reach the SDGs. This title is available as Open Access via Cambridge Core.
A novel process for Boron doping of ultrananocrystalline diamond (UNCD) films, using thermal diffusion, is described. Hall measurements show an increase in carrier concentration from 1013 to 1020 cm−3. Ultraviolet Photoelectron Spectroscopy and x-ray Photoelectron Spectroscopy show a band gap of 4.4 eV, a work function of 5.1 eV and a Fermi level at 2.0 eV above the valence band. Boron atoms distribution through UNCD films, was measured by Secondary Ion Mass Spectrometry, revealing Boron atoms diffusivity of about 10−14 cm2/s. Raman spectroscopy and x-ray Diffraction analysis revealed that UNCD films did not suffer graphitization nor structural damage during annealing.
Isotopic composition of leaf carbon (δ13C) and nitrogen (δ15N) is determined by biotic and abiotic factors. In order to determine the influence of leaf habit and site on leaf δ13C and δ15N in the understorey of two Atlantic forests in Brazil that differ in annual precipitation (1200 and 1900 mm), we measured these isotopes in the shaded understorey of 38 tropical tree species (20 in the 1200-mm site and 18 in the 1900-mm site). Mean site values for δ15N were significantly lower at the 1200-mm site (−1.4‰) compared with the 1900-mm site (+3.0‰), and δ13C was significantly greater in the 1200-mm site (−30.4‰) than in the 1900-mm site (−31.6‰). Leaf C concentration was greater and leaf N concentration was lower at 1200-mm than at 1900-mm. Leaf δ15N was negatively correlated with δ13C across the two sites. Leaf δ13C and δ15N of evergreen and deciduous species were not significantly different within a site. No significant phylogenetic signal for any traits among the study species was found. Overall, site differences were the main factor distinguishing traits among species, suggesting strong functional convergence to local climate and soils within each site for individuals in the shaded understorey.
People with anxiety disorders demand psychological attention most often. Therefore, it seems important to identify both the characteristics of the patients who demand help and the clinical variables related to that demand and its treatment. A cohort of 292 patients who requested help at a university clinical facility was studied. The typical profile of the patient was: being female, young, unmarried, with some college education, and having previously received treatment, especially pharmacological one. The three most frequent diagnoses of anxiety, which include 50% of the cases, were: Anxiety Disorder not otherwise specified, Social Phobia, and Panic Disorder with Agoraphobia. Regarding the characteristics of the intervention, the average duration of the assessment was 3.5 sessions (SD = 1.2), and the duration of the treatment was 14 sessions (SD = 11.2). The percentage of discharges was 70.2%. The average cost of treatment was around €840. The results are discussed, underlining the value of empirically supported treatments for anxiety disorders.
Building up gradually from first principles, this unique introduction to modern thermodynamics integrates classical, statistical and molecular approaches and is especially designed to support students studying chemical and biochemical engineering. In addition to covering traditional problems in engineering thermodynamics in the context of biology and materials chemistry, students are also introduced to the thermodynamics of DNA, proteins, polymers and surfaces. It includes over 80 detailed worked examples, covering a broad range of scenarios such as fuel cell efficiency, DNA/protein binding, semiconductor manufacturing and polymer foaming, emphasizing the practical real-world applications of thermodynamic principles; more than 300 carefully tailored homework problems, designed to stretch and extend students' understanding of key topics, accompanied by an online solution manual for instructors; and all the necessary mathematical background, plus resources summarizing commonly used symbols, useful equations of state, microscopic balances for open systems, and links to useful online tools and datasets.
Previous chapters established many relations between various thermodynamic properties of a fluid. We have seen, for example, that the heat capacity of a fluid is always positive. We have also shown that the thermal, mechanical, and chemical equations of state must be mutually consistent (i.e. they must satisfy the Gibbs–Duhem equation). In the previous chapter, we showed how statistical mechanics can be used to derive fundamental thermodynamic relations from simple models of molecular interactions. In this chapter we discuss in greater detail important molecular interactions and their relative magnitudes. Specific mathematical expressions taken from physical chemistry are used to estimate when interactions are important and how they might influence thermodynamic quantities. Molecular interactions ultimately determine the behavior of materials and fluids. Hence they have received considerable attention and entire texts have been devoted to their study. Interested readers are referred to [68, 80, 99] for a more complete and thorough discussion.
The equations of state and the transport properties of gases, liquids, and solids are intimately related to the forces between the molecules. The methods of statistical mechanics provide a connection between these forces and measurable thermodynamic properties. An introduction to statistical mechanics was presented in the previous chapter. Here we merely discuss the nature and the origin of intermolecular forces. In the following chapter we will illustrate how everything comes together to generate thermodynamic property predictions from intermolecular interactions. Finally, we note that calculation of intermolecular forces requires knowledge of several fundamental properties of molecules.
The text considers the ideal gas and van der Waals equations of state in some detail. In addition to these, we summarize here several more-accurate equations of state. This appendix presents but a small fraction of such equations. A more comprehensive discussion of the quality of such equations is given in .
Note that all fluid equations of state reduce to the general ideal gas at low densities. For small deviations from ideal behavior, the virial expansion is the most reliable. However, it is not appropriate for predicting vapor–liquid equilibria. The cubic equations of state are straightforward to use and computationally simple. However, some sacrifice must be made for accuracy. Of these, the Peng–Robinson, Soave–Redlich–Kwong, and Schmidt–Wenzel equations are usually superior. However, all cubic equations are suspect near the critical region. For an excellent review of many such equations, see . If computational ease should be sacrificed for accuracy, the Benedict–Webb–Rubin and the Anderko–Pitzer equations are usually more accurate.
The parameters in the cubic PVT equations of state are usually determined from the critical properties of a fluid, and these equations are given. Critical values for a few substances are given in Table D.3 in Appendix D. More values can be found from the NIST web page. If the critical values of a substance are not known, they may be estimated from group methods on the basis of the chemical structure of the substance. These methods are reviewed in .
Thermodynamics is the combination of a structure plus an underlying governing equation. Before designing plays in basketball or volleyball, we first need to lay down the rules to the game – or the structure. Once the structure is in place, we can design an infinite variety of plays and ways that the game can run. Some of these plays will be more successful than others, but all of them should fit the rules. Of course, in sports you can sometimes get away with breaking the rules, but Mother Nature is not so lax. You might be able to convince your boss to fund construction of a perpetual-motion machine, but the machine will never work.
In this chapter we lay the foundation for the entire structure of thermodynamics. Remarkably, the structure is simple, yet powerfully predictive. The cost for such elegance and power, however, is that we must begin somewhat abstractly. We need to begin with two concepts: energy and entropy. While most of us feel comfortable and are familiar with energy, entropy might be new. However, entropy is no more abstract than energy – perhaps less so – and the approach we take allows us to become as skilled at manipulating the concept of entropy as we are at thinking about energy. Therefore, in order to gain these skills, we consider many examples where an underlying governing equation is specified. For example, we consider the fundamental relations that lead to the ideal-gas law, the van der Waals equation of state, and more sophisticated equations of state that interrelate pressure, volume, and temperature.
In this chapter, we make the connection between molecules and thermodynamics. Until this chapter, we have dealt only with macroscopic quantities, and the influence of molecular details has been discussed only qualitatively. Here, we make quantitative connections between molecules and much of the thermodynamics we have already seen. For example, we derive the fundamental relations for ideal gases (simple and general), molecular adsorption on solid surfaces, and the elasticity of polymer chains. The procedure is general, and may be used to derive fundamental relations for more complex systems, to relate macroscopic experimental results to molecular interactions, or to exploit computer simulations. Since statistical mechanics deals with more detailed information than does classical thermodynamics, we can also find how real systems fluctuate in time when at equilibrium. We will see that these fluctuations are important for all systems, and can even dominate the behavior of fluids near a critical point (or spinodal curve) or that of small systems.
This chapter is not essential for understanding most of the rest of the book. However, the remaining chapters will also have a few sections dealing with the statistical mechanics of mixtures and polymers, and the material in this chapter is essential for understanding those topics. Statistical mechanics is both beautiful and powerful, but often difficult to grasp the first time you see it. However, we believe that statistical mechanics, like thermodynamics, rewards the student who visits it repeatedly.
Reactions are an essential component of chemical engineering, and reaction engineering itself is a very broad discipline [43, 107]. However, before we study the time evolution of chemical reactions, it is important to know the final equilibrium state that a system can reach. The equilibrium state of a chemical reaction is determined by thermodynamic principles.
In this chapter we examine the thermodynamic principles that govern chemical reactions, and find methods for calculating the final composition of a mixture that results from chemical reactions. The methods consist of two general steps: determination of the equilibrium constant(s) from thermodynamic properties of the constituent chemical compounds; and determination of the chemical composition of the equilibrium mixture from the initial composition and the equilibrium constant(s). In general, finding this composition requires the use of models with good estimates for the activities (or fugacities) of the constituent species, as shown in the previous two chapters. The equilibrium constant depends only on the temperature and the species present, not on the composition.
We also consider how thermodynamic driving forces can influence reaction rates. However, few details are given in this book, since that is a topic best left for other courses and textbooks. Denaturation of DNA strands is discussed in Section 10.9, where denaturation is applied to polymerase chain reactions (PCR). PCR is a technique that can be used to amplify small amounts of genetic material. Finally, in the last section, Section 10.10, connections are made to statistical mechanics.
Now that we have a reasonably complete structure of thermodynamics, we can tackle more complicated problems. In the following section we introduce the concepts of local and global stability, and show how local stability puts restrictions on second-order derivatives. In Section 4.2 we see that application of local stability criteria to the proposed fundamental relations leads to predictions of spinodal curves, which indicate when substances will spontaneously change state. In Section 4.2.2 the application of global stability to a van der Waals fluid leads to predictions of vapor saturation curves and liquid saturation curves. These curves are sometimes called binodal curves (or coexistence curves), and can be predicted from PVT equations of state alone. We then show how thermodynamic diagrams useful for refrigeration, or power-cycle design, can be constructed from PVT relations in Section 4.4. Section 4.4.2 shows generically how one can make predictions of differences in thermodynamic quantities from any of the equations of state shown in Appendix B or from experimental data.
What happens if you take gaseous nitrogen and compress it, keeping the internal energy constant by removing heat? Eventually, the nitrogen begins to condense in the container, and you have a mixture of liquid and gaseous nitrogen. If you isolate this system and wait for a long time, then you see that the system is indeed at a stable equilibrium. From Section 2.8 we know that these two phases have the same temperature and pressure. How can that be? Why is some of the nitrogen happy to stay gaseous, while the rest saw fit to condense into a liquid? The two phases have the same temperature and pressure, yet they have different densities.