jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Erin Traynor | Fraser J. Scott | Patrick Thomson
Source: Chemistry Teacher International (article, 2024-10-30)
Field: Chemistry — Physical and Theoretical Chemistry

Responses of teachers in Scotland to the reintroduction of the practical project in the advanced higher chemistry curriculum Abstract Many pupils who study chemistry in Scotland and intend to progress to university undertake a final capstone practical research project as part of their Advanced Higher chemistry qualification. This project work was suspended for several years due to COVID-19, then reintroduced in 2023/24. We surveyed 47 Scottish teachers during this first project year using a mixed-methods survey, and found that resource availability had the biggest impact on projects, with over half of respondents reporting that they asked pupils to buy materials and a third committing their own money. Technician support was seen as critical to success, but project selection was heavily curtailed by the availability of existing equipment or chemicals, even as teachers made good use of available professional development and other forms of support. We finally make recommendations around the resourcing of project work and teacher/technician CPD provision. Curriculum ; Mathematics education ; Medical education ; Chemistry ; Psychology ; Engineering ; Pedagogy ; Medicine Various Chemistry Research Topics ; Science Education and Pedagogy ; Innovative Teaching Methods
Authors: Julien Tranchida | F. Nicaud | Benjamin Beeler | Émeric Bourasseau
Source: The Journal of Chemical Physics (article, 2024-06-05)
Field: Materials Science — Materials Chemistry

Thermophysical properties and unexpected viscosity of liquid (U, Zr): An atomistic investigation In this study, we performed a numerical investigation of the thermophysical properties of liquid (U, Zr) mixtures, which are particularly relevant in the context of hypothetical nuclear accidents and the formation of in-vessel coriums. To do so, atomistic simulations leveraging classical molecular dynamics and an interatomic potential developed for solid (U, Zr) structures are performed. Our methodology is first validated by comparing the predictions of our model for the melting temperature and the structure factors to experimental, phase diagram, and ab initio data. We then use the approach to evaluate the temperature and composition dependence of four fundamental properties in the context of coriums: density, heat capacity, compressibility, and viscosity. Systematic comparisons to the existing experimental data are performed and discussed. In particular, the viscosity of liquid (U, Zr) mixtures is investigated by comparing diffusion calculations and the Stokes-Einstein formula as well as the results obtained with the Green-Kubo methodology, empirical predictions, and experimental data. Notably, the viscosity of the mixtures is predicted to be significantly higher than that of the single-element liquids, which is unexpected and could have crucial consequences on the early stages of the formation and flow of in-vessel corium. Thermodynamics ; Viscosity ; Context (archaeology) ; Compressibility ; Materials science ; Molecular dynamics ; Diffusion ; Experimental data ; Phase diagram ; Chemistry ; Phase (matter) ; Physics ; Computational chemistry Nuclear Materials and Properties ; Thermodynamic and Structural Properties of Metals and Alloys ; Fusion materials and technologies
Authors: Yu Pan | Xun Yu | Jihua Dong | Yonghang Zhao | Shuanming Li | Xiuliang Jin
Source: Frontiers in Plant Science (article, 2024-05-14)
Field: Chemistry — Analytical Chemistry

Classification of field wheat varieties based on a lightweight G-PPW-VGG11 model This validates the model's feasibility for practical agricultural wheat variety classification, establishing a foundation for intelligent management. For future research, the trained model and complete dataset are made publicly available. Field (mathematics) ; Biology ; Mathematics Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI
Authors: Jacqueline S. Ortiz-Letechipía | Carlos E. Galván-Tejada | Jorge I. Galván-Tejada | Manuel A. Soto-Murillo | Erika Acosta-Cruz | Hamurabi Gamboa-Rosales | José María Celaya Padilla | Huizilopoztli Luna-García
Source: PeerJ (article, 2024-01-09)
Field: Chemistry — Analytical Chemistry

Classification and selection of the main features for the identification of toxicity in<i>Agaricus</i>and<i>Lepiota</i>with machine learning algorithms The occurrence of fungi is cosmopolitan, and while some mushroom species are beneficial to human health, others can be toxic and cause illness problems. This study aimed to analyze the organoleptic, ecological, and morphological characteristics of a group of fungal specimens and identify the most significant features to develop models for fungal toxicity classification using genetic algorithms and LASSO regression. The results of the study indicated that odor, spore print color, and habitat were the most significant characteristics identified by the genetic algorithm GALGO. Meanwhile, odor, gill size, stalk shape, and twelve other features were the relevant characteristics identified by LASSO regression. The importance score of the odor variable was 99.99%, gill size obtained 73.7%, stalk shape scored 39.9%, and the remaining variables did not score higher than 18%. Logistic regression, k-nearest neighbor (KNN), and XG-Boost classification algorithms were used to develop models using the features selected by both GALGO and LASSO. The models were evaluated using sensitivity, specificity, and accuracy metrics. The models with the highest AUC values were XGBoost, with a maximum value of 0.99 using the features selected by LASSO, followed by KNN with a maximum value of 0.99. The GALGO selection resulted in a maximum AUC of 0.98 in KNN and XGBoost. The models developed in this study have the potential to aid in the accurate identification of toxic fungi, which can prevent health problems caused by their consumption. Lasso (programming language) ; Artificial intelligence ; Logistic regression ; Feature selection ; Identification (biology) ; Machine learning ; Regression ; Statistics ; Algorithm ; Computer science ; Pattern recognition (psychology) ; Biology ; Mathematics ; Ecology Spectroscopy and Chemometric Analyses
Authors: Subed Lamichhane | Mohammadamir Kavousi | Sheldon X.-D. Tan
Source:  (article, 2024-10-27)
Field: Materials Science — Electronic, Optical and Magnetic Materials

BPINN-EM: Fast Stochastic Analysis of Electromigration Damage using Bayesian Physics-Informed Neural Networks Electromigration (EM) induced aging and degradation in interconnect wires is inherently a stochastic process, with lifetime typically measured in terms of mean time to failure at both wire and circuit levels. However, existing approaches still incur high computing costs, as computing both means and variances is generally expensive. In this work, we propose a novel fast variational analysis framework to tackle the challenges of stochastic estimation of EM stress evolution in multi-segment interconnect wires. We utilize Bayesian networks in conjunction with the recently introduced hierarchical (two-step) physics-informed neural networks (PINN). The resulting method, termed BPINN-EM, enables rapid variational stress analysis of metal wires by leveraging the robust uncertainty quantification capability of Bayesian networks with expedited training over small dataset. Moreover, we devise BPINN-EM to incorporate Bayesian networks only in the first stage of the hierarchical PINN, thereby circumventing the need for sampling across the entire PINN level during training and significantly reducing training costs. Our results on several general multi-segment interconnect structure demonstrate that the proposed BPINN-EM approach is much more efficient than conventional baselines and state-of-the-art algorithms. Compared to a Monte Carlo simulator implemented in COMSOL, BPINN-EM offers a 240× speedup. Moreover, compared to the recently proposed EMSpice simulated by the Monte Carlo method, the new method provides more than an 85× speedup with almost no loss of accuracy. Electromigration ; Bayesian probability ; Artificial neural network ; Statistical physics ; Computer science ; Physics ; Artificial intelligence ; Quantum mechanics Copper Interconnects and Reliability ; Power Transformer Diagnostics and Insulation ; Model Reduction and Neural Networks
Authors: Mustafa Korkmaz
Source: Environmental Research and Technology (article, 2024-02-27)
Field: Chemistry — Analytical Chemistry

Factorial experimental design for removal of Indigo Carmine and Brilliant Yellow dyes from solutions by coagulation Textile and food industries produce huge amounts of wastewaters containing dye residues. When these wastewaters are discharged to receiving surface waters like as lakes and rivers, aesthetically unpleasant situations form. Therefore, these wastewaters should be treated. Wastewater treatment is sometimes an expensive operation and cheap methods should be developed. The removal of Indigo Carmine (I.C., Acid dye) and Brilliant Yellow (B.Y., Azo dye) from synthetically prepared solutions was studied by coagulation using iron chloride salt in a batch reactor at room temperature. As an experimental approach, two leveled factorial design with three factors was applied as a function of pH (4-12), iron chloride amount (0.1-0.4 g/500 mL) and dye concentration (100-200 mg/L). Low pHs supported to removal of these two dyes. The results showed that 100% I.C. dye removal and 90.5% B.Y. dye removal were achieved. The all parameters were statistically insignificant for both the dyes. Indigo Carmine and Brilliant Yellow dyes were removed from solutions successfully. The applied treatment method was evaluated as promising due to low sludge production, low cost, low coagulation duration and high performance. A time span of 5 minutes was found as enough for removals of both of the dyes. After treatment of I.C. and B.Y. dyes by coagulation, the coagulated dyes were determined as unreusable due to iron complex by these dyes. Flocculation was found to be ineffective. A continuous flow reactor was successfully adopted for these dyes. Indigo carmine ; Coagulation ; Factorial experiment ; Indigo ; Chemistry ; Pulp and paper industry ; Art ; Mathematics ; Nuclear chemistry ; Visual arts ; Statistics ; Engineering ; Psychology Dye analysis and toxicity ; Dyeing and Modifying Textile Fibers
Authors: Palak Agarwal | Bhumika Gaur | Sanjay R. Mathur
Source: Water Practice & Technology (article, 2024-09-25)
Field: Chemistry — Analytical Chemistry

Enhancing textile wastewater treatment for subsequent biological processes by integrating electrooxidation and electrocoagulation ABSTRACT The presence of refractory contaminants in textile wastewater is one of the major concerns while handling them with the biological processes at common effluent treatment. Electro-oxidation (EO) as a standalone process is an insufficient treatment method for the abolition of inorganic contaminants (carbon and non-carbon). By incorporating electrocoagulation (EC) as an associated treatment method after EO, removal of such contaminants becomes easy, which not only makes the treated wastewater fit for biological remediation but also reduces load on biological units. The removal of non-carbonic impurities was assessed in terms of improvement in the chemical oxygen demand (COD) post EC. L25 orthogonal array of experiments was obtained using the Taguchi method. From the S/N ratio plot, the optimal process combination was obtained as, EO with current density = 25 mA/cm2, electrolysis time = 50 min followed by EC with current density = 18 mA/cm2, speed of rotation = 50 rpm and electrolysis time = 40 min. The enhancement in COD and total organic carbon removal efficiencies after EC were 65.11 and 63.57%, respectively, over EO. The biodegradability index also improved from an initial value of 0.098–0.737 post-hybrid treatment. Inorganic carbon reduced from a value of 36.37 mg/L after EO to 0.1 mg/L post EC. Electrocoagulation ; Textile ; Wastewater ; Sewage treatment ; Environmental science ; Waste management ; Pulp and paper industry ; Environmental chemistry ; Environmental engineering ; Chemistry ; Materials science ; Engineering Dye analysis and toxicity
Authors: Valeria Marzano | Stefano Levi Mortera | Lorenza Putignani
Source: PROTEOMICS (review, 2024-12-30)
Field: Chemistry — Spectroscopy

Insights on Wet and Dry Workflows for Human Gut Metaproteomics The human gut microbiota (GM) is a community of microorganisms that resides in the gastrointestinal (GI) tract. Recognized as a critical element of human health, the functions of the GM extend beyond GI well-being to influence overall systemic health and susceptibility to disease. Among the other omic sciences, metaproteomics highlights additional facets that make it a highly valuable discipline in the study of GM. Indeed, it allows the protein inventory of complex microbial communities. Proteins with associated taxonomic membership and function are identified and quantified from their constituent peptides by liquid chromatography coupled to mass spectrometry analyses and by querying specific databases (DBs). The aim of this review was to compile comprehensive information on metaproteomic studies of the human GM, with a focus on the bacterial component, to assist newcomers in understanding the methods and types of research conducted in this field. The review outlines key steps in a metaproteomic-based study, such as protein extraction, DB selection, and bioinformatic workflow. The importance of standardization is emphasized. In addition, a list of previously published studies is provided as hints for researchers interested in investigating the role of GM in health and disease states. Metaproteomics ; Workflow ; Computational biology ; Metagenomics ; Biology ; Computer science ; Biochemistry Advanced Proteomics Techniques and Applications ; Gut microbiota and health ; Metabolomics and Mass Spectrometry Studies
Authors: Samiran Sen | Henrique Musseli Cezar | Morten Ledum | Xinmeng Li | Michele Cascella
Source: The Journal of Physical Chemistry B (article, 2024-11-14)
Field: Materials Science — Materials Chemistry

Phase Coexistence in Hamiltonian Hybrid Particle–Field Theory Using a Multi-Gaussian Approach This study introduces an implementation of multiple Gaussian filters within the Hamiltonian hybrid particle-field (HhPF) theory, aimed at capturing phase coexistence phenomena in mesoscopic molecular simulations. By employing a linear combination of two Gaussians, we demonstrate that HhPF can generate potentials with attractive and steric components analogous to Lennard-Jones (LJ) potentials, which are crucial for modeling phase coexistence. We compare the performance of this method with the multi-Gaussian core model (MGCM) in simulating liquid-gas coexistence for a single-component system across various densities and temperatures. Our results show that HhPF effectively captures detailed information on phase coexistence and interfacial phenomena, including microconfiguration transitions and increased interfacial fluctuations at higher temperatures. Notably, the phase boundaries obtained from HhPF simulations align more closely with those of LJ systems compared to the MGCM results. This work advances the hybrid particle-field methodology to address phase coexistence without requiring modifications to the equation of state or introducing additional interaction energy functional terms, offering a promising approach for mesoscale molecular simulations of complex systems. Hamiltonian (control theory) ; Gaussian ; Statistical physics ; Particle (ecology) ; Physics ; Mathematical physics ; Mathematics ; Geology ; Mathematical optimization ; Quantum mechanics Material Dynamics and Properties ; Spectroscopy and Quantum Chemical Studies ; Quantum, superfluid, helium dynamics
Authors: Kateryna Tkachenko | J.M. González-Sáiz | Ana Cristina Calvo | Christian Lunetta | Rosario Osta | C. Pizarro
Source: Biosensors (article, 2024-10-30)
Field: Chemistry — Physical and Theoretical Chemistry

Comparative Blood Profiling Based on ATR-FTIR Spectroscopy and Chemometrics for Differential Diagnosis of Patients with Amyotrophic Lateral Sclerosis—Pilot Study Amyotrophic lateral sclerosis (ALS) is a motor neurodegenerative disease characterized by poor prognosis. Currently, screening and diagnostic methods for ALS remain challenging, often leading to diagnosis at an advanced stage of the disease. This delay hinders the timely initiation of therapy, negatively impacting patient well-being. Additionally, misdiagnosis with other neurodegenerative disorders that present similar profiles often occurs. Therefore, there is an urgent need for a cost-effective, rapid, and user-friendly tool capable of predicting ALS onset. In this pilot study, we demonstrate that infrared spectroscopy, coupled with chemometric analysis, can effectively identify and predict disease profiles from blood samples drawn from ALS patients. The selected predictive spectral markers, which are used in various discriminant models, achieved an AUROC sensitivity of almost 80% for distinguishing ALS patients from controls. Furthermore, the differentiation of ALS at both the initial and advanced stages from other neurodegenerative disorders showed even higher AUROC values, with sensitivities of 87% (AUROC: 0.70-0.97). These findings highlight the elevated potential of ATR-FTIR spectroscopy for routine clinical screening and early diagnosis of ALS. Amyotrophic lateral sclerosis ; Chemometrics ; Fourier transform infrared spectroscopy ; Medicine ; Analytical Chemistry (journal) ; Chemistry ; Chromatography ; Internal medicine ; Physics thermodynamics and calorimetric analyses ; Cholinesterase and Neurodegenerative Diseases ; Amyotrophic Lateral Sclerosis Research
Authors: Niko Vlahakis | James M. Holton | Nicholas K. Sauter | Peter Ercius | Aaron S. Brewster | José A. Rodríguez
Source: Annual Review of Physical Chemistry (review, 2024-06-28)
Field: Materials Science — Materials Chemistry

3D Nanocrystallography and the Imperfect Molecular Lattice Crystallographic analysis relies on the scattering of quanta from arrays of atoms that populate a repeating lattice. While large crystals built of lattices that appear ideal are sought after by crystallographers, imperfections are the norm for molecular crystals. Additionally, advanced X-ray and electron diffraction techniques, used for crystallography, have opened the possibility of interrogating micro- and nanoscale crystals, with edges only millions or even thousands of molecules long. These crystals exist in a size regime that approximates the lower bounds for traditional models of crystal nonuniformity and imperfection. Accordingly, data generated by diffraction from both X-rays and electrons show increased complexity and are more challenging to conventionally model. New approaches in serial crystallography and spatially resolved electron diffraction mapping are changing this paradigm by better accounting for variability within and between crystals. The intersection of these methods presents an opportunity for a more comprehensive understanding of the structure and properties of nanocrystalline materials. Diffraction ; Electron diffraction ; Electron crystallography ; Nanocrystalline material ; Lattice (music) ; Scattering ; Electron ; Materials science ; Crystallography ; Crystal (programming language) ; X-ray crystallography ; Chemical physics ; Physics ; Condensed matter physics ; Nanotechnology ; Optics ; Computer science ; Chemistry ; Quantum mechanics X-ray Diffraction in Crystallography ; Enzyme Structure and Function ; X-ray Spectroscopy and Fluorescence Analysis
Authors: Saranya Workhwa | Thitirat Khanthong | Napatsorn Manmak | Anthony Keith Thompson | Sontisuk Teerachaichayut
Source: Horticulturae (article, 2024-03-29)
Field: Chemistry — Analytical Chemistry

Detection of Hardening in Mangosteens Using near-Infrared Hyperspectral Imaging Mangosteens can develop a postharvest physiological disorder, called “hardening”, which affects their marketability and is not detectable using visual inspection. The hardening disorder of mangosteens was determined by firmness value using the texture analyzer. Near-infrared hyperspectral imaging (NIR-HSI) in the region of 935–1720 nm was tested as a possible rapid and non-destructive method to detect this disorder. The spectra from a region of interest of mangosteens were acquired and used for analysis. Calibration models for firmness of a similarly sized group and a mixed-size group were established using partial least squares regression (PLSR) and support vector machine regression (SVMR). Chemometric algorithms were investigated in order to determine the optimal conditions for establishing the models for firmness. The optimum model was obtained when the fruit were graded into similarly sized groups. Using partial least squares regression (PLSR), the correlation coefficient of prediction (Rp) was 0.87 and the root mean square error of prediction (RMSEP) was 6.25 N. The predictive images for firmness of the fruit were created by interpreting predicted firmness visualized as colors in every pixel. From the data, it was concluded that NIR-HSI can potentially be used to visualize hardening of individual mangosteens based on their predictive images. Hyperspectral imaging ; Partial least squares regression ; Correlation coefficient ; Support vector machine ; Pixel ; Regression ; Mathematics ; Artificial intelligence ; Mean squared error ; Hardening (computing) ; Near-infrared spectroscopy ; Coefficient of determination ; Biological system ; Pattern recognition (psychology) ; Postharvest ; Chemistry ; Statistics ; Computer science ; Horticulture ; Optics ; Physics ; Biology Spectroscopy and Chemometric Analyses ; Phytochemicals and Antioxidant Activities ; Leaf Properties and Growth Measurement
Authors: Natalia P. Rodina | Riddhiman Sarkar | Dimitrios Tsakalos | Saba Suladze | Zheng Niu | Bernd Reif
Source: Biomolecular NMR Assignments (article, 2024-08-09)
Field: Chemistry — Spectroscopy

Manual and automatic assignment of two different Aβ40 amyloid fibril polymorphs using MAS solid-state NMR spectroscopy Amyloid fibrils from Alzheimer's amyloid-beta peptides (Aβ) are found to be polymorphic. So far, 14 Aβ40 fibril structures have been determined. The mechanism of why one particular protein sequence adopts so many different three-dimensional structures is yet not understood. In this work, we describe the assignment of the NMR chemical shifts of two Alzheimer's disease fibril polymorphs, P1 and P2, which are formed by the amyloid-beta peptide Aβ40. The assignment is based on <sup>13</sup>C-detected 3D NCACX and NCOCX experiments MAS solid-state NMR experiments. The fibril samples are prepared using an extensive seeding protocol in the absence and presence of the small heat shock protein αB-crystallin. In addition to manual assignments, we obtain chemical shift assignments using the automation software ARTINA. We present an analysis of the secondary chemical shifts and a discussion on the differences between the manual and automated assignment strategies. Fibril ; Amyloid (mycology) ; Solid-state nuclear magnetic resonance ; Chemical shift ; Nuclear magnetic resonance spectroscopy ; Chemistry ; Amyloid fibril ; Peptide ; Heat shock protein ; Crystallography ; Biophysics ; Amyloid β ; Biochemistry ; Nuclear magnetic resonance ; Stereochemistry ; Biology ; Physical chemistry ; Physics Advanced NMR Techniques and Applications ; NMR spectroscopy and applications ; Protein Structure and Dynamics