Chemistry labs can become a dangerous environment for students as the lab exercises involve hazardous chemicals, glassware, and equipment. Approximately one hundred thousand students take chemistry laboratory classes annually in Nepal. We conducted a survey on chemical lab safety issues across Nepal. In this paper, we assess the safety policy and equipment, protocols and procedures followed, and waste disposal in chemistry teaching labs. Significant population of the respondents believed that there is no monitoring of the lab safety in their lab (p<0.001). Even though many labs do not allow food and beverages inside lab and have first aid kits, they lack some basic safety equipment. There is no institutional mechanism to dispose lab waste and chemical waste is disposed haphazardly. Majority of the respondents believed that the safety training should be a part of educational training (p = 0.001) and they would benefit from short course and/or workshop on lab safety (p<0.001).
A major concern among universities around the world is that female students face gender bias, discrimination and related barriers in male-dominated STEM fields. To investigate this concern, we conducted a novel large-scale experiment of interactions between female and male students in one of the most important gateway courses for the Sciences and a course in which students interact one-on-one extensively throughout the term. Over the past four years, at a large public research university, we randomly paired every student enrolled in an introductory Chemistry lab (3,902 students and total N = 5,537). Using precise estimates from the experiment, we provide novel evidence that female students are not negatively affected academically by male partners. When assigned a male partner, female students do not receive lower scores or grades, and they are no more likely to drop the course or not continue in Chemistry or a STEM field. We also find that academically weaker female students are not negatively affected by male students and that female students are not negatively affected when paired with academically stronger male students. Although previous studies have documented that female students self-report experiencing gender bias from male peers in STEM, importantly, we do not find evidence that female students are negatively affected by male peers in intensive, long-term pairwise interactions in their course grades or future STEM course taking. The findings provide hopeful news for future trends in female representation in STEM fields.
One of the largest river systems in South Africa, the Olifants River, has experienced significant changes in water quality due to anthropogenic activities. Since 2005, there have been various “outbreaks” of the inflammatory disease pansteatitis in several vertebrate species. Large-scale pansteatitis-related mortality events have decimated the crocodile population at Lake Loskop and decreased the population at Kruger National Park. Most pansteatitis-related diagnoses within the region are conducted post-mortem by either gross pathology or histology. The application of a non-lethal approach to assess the prevalence and pervasiveness of pansteatitis in the Olifants River region would be of great importance for the development of a management plan for this disease. In this study, several plasma-based biomarkers accurately classified pansteatitis in Mozambique tilapia (Oreochromis mossambicus) collected from Lake Loskop using a commercially available benchtop blood chemistry analyzer combined with data interpretation via artificial neural network analysis. According to the model, four blood chemistry parameters (calcium, sodium, total protein and albumin), in combination with total length, diagnose pansteatitis to a predictive accuracy of 92 percent. In addition, several morphometric traits (total length, age, weight) were also associated with pansteatitis. On-going research will focus on further evaluating the use of blood chemistry to classify pansteatitis across different species, trophic levels, and within different sites along the Olifants River.
Interdisciplinary scientific collaboration promotes the innovative development of scientific research. Photocatalytic hydrogen evolution (PHE) is a typical interdisciplinary subject. This study aims to explore the characteristics of discipline interaction and the temporal evolution in the field. Bibliometric analysis could be used to understand the stage of research in a particular subject. In this work, the publications on the topic in Web of Science (WoS) platform from 1999 to 2020 were selected. On the basis of social network theory, the characteristics of interdisciplinary were revealed from three perspectives. First, the disciplinary interaction network is constructed through disciplinary co-occurrence to detect the characteristics of interaction structure among different disciplines. Then the node centrality index is employed to explore the influence of disciplines in the interactive network by using network centrality analysis. Moreover, the dynamic of discipline interaction evolution is studied using blockmodeling analysis. In the field of PHE, the number of disciplines and the intensity of interaction among different subjects gradually increased in the past 20 years. Chemistry and Material Sciences are the core discipline, and they play an important role in the network. The whole network is divided into different discipline groups. The scale of the discipline group is becoming large, and the disciplinary interaction is becoming more complex. The obtained results are helpful for guiding scholars to carry out interdisciplinary interaction. The methods of detecting interdisciplinary interactive relationship could provide paths for interdisciplinary research in other fields.
Carbonate chemistry variability is often poorly characterized in coastal regions and patterns of covariation with other biologically important variables such as temperature, oxygen concentration, and salinity are rarely evaluated. This absence of information hampers the design and interpretation of ocean acidification experiments that aim to characterize biological responses to future pCO2 levels relative to contemporary conditions. Here, we analyzed a large carbonate chemistry data set from Puget Sound, a fjord estuary on the U.S. west coast, and included measurements from three seasons (winter, summer, and fall). pCO2 exceeded the 2008–2011 mean atmospheric level (392 µatm) at all depths and seasons sampled except for the near-surface waters (< 10 m) in the summer. Further, undersaturated conditions with respect to the biogenic carbonate mineral aragonite were widespread (Ωar<1). We show that pCO2 values were relatively uniform throughout the water column and across regions in winter, enriched in subsurface waters in summer, and in the fall some values exceeded 2500 µatm in near-surface waters. Carbonate chemistry covaried to differing levels with temperature and oxygen depending primarily on season and secondarily on region. Salinity, which varied little (27 to 31), was weakly correlated with carbonate chemistry. We illustrate potential high-frequency changes in carbonate chemistry, temperature, and oxygen conditions experienced simultaneously by organisms in Puget Sound that undergo diel vertical migrations under present-day conditions. We used simple calculations to estimate future pCO2 and Ωar values experienced by diel vertical migrators based on an increase in atmospheric CO2. Given the potential for non-linear interactions between pCO2 and other abiotic variables on physiological and ecological processes, our results provide a basis for identifying control conditions in ocean acidification experiments for this region, but also highlight the wide range of carbonate chemistry conditions organisms may currently experience in this and similar coastal ecosystems.
Hybrid combinatorial chemistry strategies that use DNA as an information-carrying medium are proving to be powerful tools for molecular discovery. In order to extend these efforts, we present a highly parallel format for DNA-programmed chemical library synthesis. The new format uses a standard microwell plate footprint and is compatible with commercially available automation technology. It can accommodate a wide variety of combinatorial synthetic schemes with up to 384 different building blocks per chemical step. We demonstrate that fluidic routing of DNA populations in the highly parallel format occurs with excellent specificity, and that chemistry on DNA arrayed into 384 well plates proceeds robustly, two requirements for the high-fidelity translation and efficient in vitro evolution of small molecules.
In tertiary science education, students are encouraged to engage in discipline specific thinking, to learn their chosen subject. The challenge for educators is engaging all students equitably, despite their educational backgrounds and depth of discipline specific knowledge. Personalising learning in the context of large-scale tertiary courses can only be achieved by using digital technologies. In the context of chemistry education, this project has investigated how an adaptive learning technology can effectively and consistently engage students in discipline specific thinking, by personalising their learning pathway. Adaptive learning has been integrated into a foundational chemistry subject and through quantitative analysis there is empirical evidence to support the benefit adaptive learning has on outcomes, in both the short and long term. This study shows adaptive learning can equitably meet the needs for all students and can lead to improvements in educational behaviour beyond grades. The evidence supports adaptive learning as one critical tool for chemistry educators, and educators in other disciplines of science, to include in their suite of pedagogical strategies to meet the needs of all their students.
Thermodynamics dictates the structure and function of metabolism. Redox reactions drive cellular energy and material flow. Hence, accurately quantifying the thermodynamics of redox reactions should reveal design principles that shape cellular metabolism. However, only few redox potentials have been measured, and mostly with inconsistent experimental setups. Here, we develop a quantum chemistry approach to calculate redox potentials of biochemical reactions and demonstrate our method predicts experimentally measured potentials with unparalleled accuracy. We then calculate the potentials of all redox pairs that can be generated from biochemically relevant compounds and highlight fundamental trends in redox biochemistry. We further address the question of why NAD/NADP are used as primary electron carriers, demonstrating how their physiological potential range fits the reactions of central metabolism and minimizes the concentration of reactive carbonyls. The use of quantum chemistry can revolutionize our understanding of biochemical phenomena by enabling fast and accurate calculation of thermodynamic values. Author summary: Redox reactions define the energetic constraints within which life can exist. However, measurements of reduction potentials are scarce and unstandardized, and current prediction methods fall short of desired accuracy and coverage. Here, we harness quantum chemistry tools to enable the high-throughput prediction of reduction potentials with unparalleled accuracy. We calculate the reduction potentials of all redox pairs that can be generated using known biochemical compounds. This high-resolution dataset enables us to uncover global trends in metabolism, including the differences between and within oxidoreductase groups. We further demonstrate that the redox potential of NAD(P) optimally satisfies two constraints: reversibly reducing and oxidizing the vast majority of redox reactions in central metabolism while keeping the concentration of reactive carbonyl intermediates in check.
The environmental and health impacts of chemical processes have been a growing concern, leading to the establishment of Green Chemistry principles. Introducing new metrics for the assessment of methods’ greenness is crucial to evaluate the exerted efforts to conserve the environment. In this work, we introduce a Modified Green Star Area (MoGSA) and software to assess the greenness of chemical reactions in laboratory settings. MoGSA refines the traditional Green Star Area Index (GSAI) by allowing users to selectively apply specific principles of Green Chemistry based on their relevance to the chemical process being evaluated. This approach addresses the limitations of GSAI, which often lacks clear boundaries between green and non-green practices and does not account for the varying applicability of the 12 Green Chemistry principles across different contexts. Through comparative case studies on catalytic stereoselective reduction of acetophenone, MoGSA demonstrates its utility in providing a more refined and flexible assessment, enhancing both educational and industrial applications of sustainable chemical practices. The software is available as an open source at https://bit.ly/MOGSA.
The important but difficult choice of vocational trajectory often takes place in college, beginning with majoring in a subject and taking relevant coursework. Of all possible disciplines, pre-medical studies are often not a formally defined major but pursued by a substantial proportion of the college population. Understanding students’ experiences with pre-med coursework is valuable and understudied, as most research on medical education focuses on the later medical school and residency. We examined the pattern and predictors of attrition at various milestones along the pre-med coursework track during college. Using a College Board dataset, we analyzed a sample of 15,442 students spanning 102 institutions who began their post-secondary education in years between 2006 and 2009. We examined whether students fulfilled the required coursework to remain eligible for medical schools at several milestones: 1) one semester of general chemistry, biology, physics, 2) two semesters of general chemistry, biology, physics, 3) one semester of organic chemistry, and 4) either the second semester of organic chemistry or one semester of biochemistry, and predictors of persistence at each milestone. Only 16.5% of students who intended to major in pre-med graduate college with the required coursework for medical schools. Attrition rates are highest initially but drop as students take more advanced courses. Predictors of persistence include academic preparedness before college (e.g., SAT scores, high school GPA) and college performance (e.g., grades in pre-med courses). Students who perform better academically both in high school and in college courses are more likely to remain eligible for medical school.
Quantifying the amount of carbon dioxide (CO2) in seawater is an essential component of ocean acidification research; however, equipment for measuring CO2 directly can be costly and involve complex, bulky apparatus. Consequently, other parameters of the carbonate system, such as pH and total alkalinity (AT), are often measured and used to calculate the partial pressure of CO2 (pCO2) in seawater, especially in biological CO2-manipulation studies, including large ecological experiments and those conducted at field sites. Here we compare four methods of pCO2 determination that have been used in biological ocean acidification experiments: 1) Versatile INstrument for the Determination of Total inorganic carbon and titration Alkalinity (VINDTA) measurement of dissolved inorganic carbon (CT) and AT, 2) spectrophotometric measurement of pHT and AT, 3) electrode measurement of pHNBS and AT, and 4) the direct measurement of CO2 using a portable CO2 equilibrator with a non-dispersive infrared (NDIR) gas analyser. In this study, we found these four methods can produce very similar pCO2 estimates, and the three methods often suited to field-based application (spectrophotometric pHT, electrode pHNBS and CO2 equilibrator) produced estimated measurement uncertainties of 3.5–4.6% for pCO2. Importantly, we are not advocating the replacement of established methods to measure seawater carbonate chemistry, particularly for high-accuracy quantification of carbonate parameters in seawater such as open ocean chemistry, for real-time measures of ocean change, nor for the measurement of small changes in seawater pCO2. However, for biological CO2-manipulation experiments measuring differences of over 100 μatm pCO2 among treatments, we find the four methods described here can produce similar results with careful use.
Ocean acidification (OA) has the potential to restructure ecosystems due to variation in species sensitivity to the projected changes in ocean carbon chemistry. Ecological models can be forced with scenarios of OA to help scientists, managers, and other stakeholders understand how ecosystems might change. We present a novel methodology for developing estimates of species sensitivity to OA that are regionally specific, and applied the method to the California Current ecosystem. To do so, we built a database of all published literature on the sensitivity of temperate species to decreased pH. This database contains 393 papers on 285 species and 89 multi-species groups from temperate waters around the world. Research on urchins and oysters and on adult life stages dominates the literature. Almost a third of the temperate species studied to date occur in the California Current. However, most laboratory experiments use control pH conditions that are too high to represent average current chemistry conditions in the portion of the California Current water column where the majority of the species live. We developed estimates of sensitivity to OA for functional groups in the ecosystem, which can represent single species or taxonomically diverse groups of hundreds of species. We based these estimates on the amount of available evidence derived from published studies on species sensitivity, how well this evidence could inform species sensitivity in the California Current ecosystem, and the agreement of the available evidence for a species/species group. This approach is similar to that taken by the Intergovernmental Panel on Climate Change to characterize certainty when summarizing scientific findings. Most functional groups (26 of 34) responded negatively to OA conditions, but when uncertainty in sensitivity was considered, only 11 groups had relationships that were consistently negative. Thus, incorporating certainty about the sensitivity of species and functional groups to OA is an important part of developing robust scenarios for ecosystem projections.
Academic researchers and many in industry often lack the financial resources available to scientists working in “big pharma.” High costs include those associated with high-throughput screening and chemical synthesis. In order to address these challenges, many researchers have in part turned to alternate methodologies. Virtual screening, for example, often substitutes for high-throughput screening, and click chemistry ensures that chemical synthesis is fast, cheap, and comparatively easy. Though both in silico screening and click chemistry seek to make drug discovery more feasible, it is not yet routine to couple these two methodologies. We here present a novel computer algorithm, called AutoClickChem, capable of performing many click-chemistry reactions in silico. AutoClickChem can be used to produce large combinatorial libraries of compound models for use in virtual screens. As the compounds of these libraries are constructed according to the reactions of click chemistry, they can be easily synthesized for subsequent testing in biochemical assays. Additionally, in silico modeling of click-chemistry products may prove useful in rational drug design and drug optimization. AutoClickChem is based on the pymolecule toolbox, a framework that may facilitate the development of future python-based programs that require the manipulation of molecular models. Both the pymolecule toolbox and AutoClickChem are released under the GNU General Public License version 3 and are available for download from http://autoclickchem.ucsd.edu.
Shannon’s theory of communication has been very successfully applied for the analysis of biological information. However, the theory neglects semantic and pragmatic aspects and thus cannot directly be applied to distinguish between (bio-) chemical systems able to process “meaningful” information from those that do not. Here, we present a formal method to assess a system’s semantic capacity by analyzing a reaction network’s capability to implement molecular codes. We analyzed models of chemical systems (martian atmosphere chemistry and various combustion chemistries), biochemical systems (gene expression, gene translation, and phosphorylation signaling cascades), an artificial chemistry, and random reaction networks. Our study suggests that different chemical systems posses different semantic capacities. No semantic capacity was found in the model of the martian atmosphere chemistry, the studied combustion chemistries, and highly connected random networks, i.e. with these chemistries molecular codes cannot be implemented. High semantic capacity was found in the studied biochemical systems and in random reaction networks where the number of second order reactions is twice the number of species. We conclude that our approach can be applied to evaluate the information processing capabilities of a chemical system and may thus be a useful tool to understand the origin and evolution of meaningful information, e.g. in the context of the origin of life.
Chemical conjugation of polysaccharide to carrier proteins has been a successful strategy to generate potent vaccines against bacterial pathogens. We developed a similar approach for poorly immunogenic malaria protein antigens. Our lead candidates in clinical trials are the malaria transmission blocking vaccine antigens, Pfs25 and Pfs230D1, individually conjugated to the carrier protein Exoprotein A (EPA) through thioether chemistry. These conjugates form nanoparticles that show enhanced immunogenicity compared to unconjugated antigens. In this study, we examined the broad applicability of this technology as a vaccine development platform, by comparing the immunogenicity of conjugates prepared by four different chemistries using different malaria antigens (PfCSP, Pfs25 and Pfs230D1), and carriers such as EPA, TT and CRM197. Several conjugates were synthesized using thioether, amide, ADH and glutaraldehyde chemistries, characterized for average molecular weight and molecular weight distribution, and evaluated in mice for humoral immunogenicity. Conjugates made with the different chemistries, or with different carriers, showed no significant difference in immunogenicity towards the conjugated antigens. Since particle size can influence immunogenicity, we tested conjugates with different average size in the range of 16–73 nm diameter, and observed greater immunogenicity of smaller particles, with significant differences between 16 and 73 nm particles. These results demonstrate the multiple options with respect to carriers and chemistries that are available for protein-protein conjugate vaccine development.
Two independent surveys of PhD students in STEM fields at the University of California, Berkeley, indicate that underrepresented minorities (URMs) publish at significantly lower rates than non-URM males, placing the former at a significant disadvantage as they compete for postdoctoral and faculty positions. Differences as a function of gender reveal a similar, though less consistent, pattern. A conspicuous exception is Berkeley’s College of Chemistry, where publication rates are tightly clustered as a function of ethnicity and gender, and where PhD students experience a highly structured program that includes early and systematic involvement in research, as well as clear expectations for publishing. Social science research supports the hypothesis that this more structured environment hastens the successful induction of diverse groups into the high-performance STEM academic track.
As plant litter decomposes, its mass exponentially decreases until it reaches a non-zero asymptote. However, decomposition rates vary considerably among litter types as a function of their overall quality (i.e., carbon:nitrogen (C:N) ratio and litter chemistry). We investigated the effects of hairy vetch (HV: Vicia villosa Roth):cereal rye (RYE: Secale cereale L.) biomass proportions with or without broadcasted poultry manure on overall litter quality before and during decomposition. As HV biomass proportions increased from 0 to 100%, the relative susceptibility of HV:RYE mixtures to microbial decomposition increased due to: (i) decrease in the initial C:N ratio (87:1 to 10:1 in 2012 and 67:1 to 9:1 in 2013), (ii) increase in the non-structural labile carbohydrates (33 to 61% across years), and (iii) decrease in the structural holo-cellulose (59 to 33% across years) and lignin (8 to 6% across years) fractions. Broadcasted poultry manure decreased the overall initial quality of HV-dominated litters and increased the overall initial quality of RYE-dominated litters. Across all HV:RYE biomass proportions with or without poultry manure, chemical changes during litter decay were related to proportional mass loss. Therefore, the relative decrease in carbohydrates and the concomitant increase in holo-cellulose and lignin fractions were more pronounced for fast decomposing litter types, i.e., litters dominated by HV rather than RYE. While our results suggest possible convergence of litter C:N ratios, initial differences in litter chemistry neither converged nor diverged. Therefore, we conclude that the initial chemistry of litter before decomposition exerts a strong control on its chemical composition throughout the decay continuum.
Encouragement of students across all communities through scientific outreach programs is critical to engaging the next generation, exciting young minds to pursue careers in science and medicine. Herein, we present a uniquely structured and widely influential science outreach program. Founded in 2005, the Duke Chemistry Outreach (DCO) employs a pedagogical approach to outreach that aims to teach its audience a new scientific concept, while instilling a pure enjoyment of science. DCO has performed 583 events reaching over 70,000 participants throughout 2,270 hours, with the majority of events in Durham, the surrounding North Carolinian communities, and across 8 other states. The flexibility and diversity of this outreach program creates a framework amendable for others to adopt in both secondary and higher education settings. Across 14 years, 581 events, and reaching 70,000 audience members, the Duke Chemistry Outreach program has engaged the surrounding community through fun scientific demonstrations. This Community Page article provides examples and guidelines to encourage others to establish similar programs.
The forests of central Europe have undergone remarkable transitions in the past 40 years as air quality has improved dramatically. Retrospective analysis of Norway spruce (Picea abies) tree rings in the Czech Republic shows that air pollution (e.g. SO2 concentrations, high acidic deposition to the forest canopy) plays a dominant role in driving forest health. Extensive soil acidification occurred in the highly polluted "Black Triangle" in Central Europe, and upper mineral soils are still acidified. In contrast, acidic atmospheric deposition declined by 80% and atmospheric SO2 concentration by 90% between the late 1980s and 2010s. In this study we oserved that annual tree ring width (TRW) declined in the 1970s and subsequently recovered in the 1990s, tracking SO2 concentrations closely. Furthermore, recovery of TRW was similar in unlimed and limed stands. Despite large increases in soil base saturation, as well as soil pH, as a result of repeated liming starting in 1981, TRW growth was similar in limed and unlimed plots. TRW recovery was interrupted in 1996 when highly acidic rime (originating from more pronounced decline of alkaline dust than SO2 from local power plants) injured the spruce canopy, but recovered soon to the pre-episode growth. Across the long-term site history, changes in soil chemistry (pH, base saturation, Bc/Al soil solution ratio) cannot explain observed changes in TRW at the two study sites where we tracked soil chemistry. Instead, statistically significant recovery in TRW is linked to the trajectory of annual SO2 concentrations or sulfur deposition at all three stands.
For undergraduate students, involvement in authentic research represents scholarship that is consistent with disciplinary quality standards and provides an integrative learning experience. In conjunction with performing research, the communication of the results via presentations or publications is a measure of the level of scientific engagement. The empirical study presented here uses generalized linear mixed models with hierarchical bootstrapping to examine the factors that impact the means of dissemination of undergraduate research results. Focusing on the research experiences in physics and chemistry of undergraduates at four Primarily Undergraduate Institutions (PUIs) from 2004–2013, statistical analysis indicates that the gender of the student does not impact the number and type of research products. However, in chemistry, the rank of the faculty advisor and the venue of the presentation do impact the number of research products by undergraduate student, whereas in physics, gender match between student and advisor has an effect on the number of undergraduate research products. This study provides a baseline for future studies of discipline-based bibliometrics and factors that affect the number of research products of undergraduate students.
Metabolic networks perform some of the most fundamental functions in living cells, including energy transduction and building block biosynthesis. While these are the best characterized networks in living systems, understanding their evolutionary history and complex wiring constitutes one of the most fascinating open questions in biology, intimately related to the enigma of life's origin itself. Is the evolution of metabolism subject to general principles, beyond the unpredictable accumulation of multiple historical accidents? Here we search for such principles by applying to an artificial chemical universe some of the methodologies developed for the study of genome scale models of cellular metabolism. In particular, we use metabolic flux constraint-based models to exhaustively search for artificial chemistry pathways that can optimally perform an array of elementary metabolic functions. Despite the simplicity of the model employed, we find that the ensuing pathways display a surprisingly rich set of properties, including the existence of autocatalytic cycles and hierarchical modules, the appearance of universally preferable metabolites and reactions, and a logarithmic trend of pathway length as a function of input/output molecule size. Some of these properties can be derived analytically, borrowing methods previously used in cryptography. In addition, by mapping biochemical networks onto a simplified carbon atom reaction backbone, we find that properties similar to those predicted for the artificial chemistry hold also for real metabolic networks. These findings suggest that optimality principles and arithmetic simplicity might lie beneath some aspects of biochemical complexity. Author Summary: Metabolism is the network of biochemical reactions that transforms available resources (“inputs”) into energy currency and building blocks (“outputs”). Different organisms have different assortments of metabolic pathways and input/output requirements, reflecting their adaptation to specific environments, and to specific strategies for reproduction and survival. Here we ask whether, beneath the intricate wiring of these networks, it is possible to discern signatures of optimal (i.e., shortest and maximally efficient) pathway architectures. A systematic search for such optimal pathways between all possible pairs of input and output molecules in real organic chemistry is computationally intractable. However, we can implement such a search in a simple artificial chemistry, which roughly resembles a single atom (e.g., carbon) version of real biochemistry. We find that optimal pathways in our idealized chemistry display a logarithmic dependence of pathway length on input/output molecule size. They also display recurring topologies, including autocatalytic cycles reminiscent of ancient and highly conserved cores of real biochemistry. Finally, across all optimal pathways, we identify universally important metabolites and reactions, as well as a characteristic distribution of reaction utilization. Similar features can be observed in real metabolic networks, suggesting that arithmetic simplicity may lie beneath some aspects of biochemical complexity.