jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Sophie R. Ullrich | H Fuchs | Charlotte Ashworth-Güth
Source: Frontiers in Microbiology (article, 2024-02-07)
Field: Chemistry — Electrochemistry

Electrochemical and structural characterization of recombinant respiratory proteins of the acidophilic iron oxidizer Ferrovum sp. PN-J47-F6 suggests adaptations to the acidic pH at protein level The tendency of the periplasmic redox proteins in acidophiles to have more positive redox potentials (<i>E<sub>m</sub></i>) than their homologous counterparts in neutrophiles suggests an adaptation to acidic pH at protein level, since thermodynamics of electron transfer processes are also affected by acidic pH. Since this conclusion is mainly based on the electrochemical characterization of redox proteins from extreme acidophiles of the genus <i>Acidithiobacillus</i>, we aimed to characterize three recombinant redox proteins of the more moderate acidophile <i>Ferrovum</i> sp. PN-J47-F6. We applied protein film voltammetry and linear sweep voltammetry coupled to UV/Vis spectroscopy to characterize the redox behavior of HiPIP-41, CytC-18, and CytC-78, respectively. The <i>E<sub>m</sub></i>-values of HiPIP-41 (571 ± 16 mV), CytC-18 (276 ± 8 mV, 416 ± 2 mV), and CytC-78 (308 ± 7 mV, 399 ± 7 mV) were indeed more positive than those of homologous redox proteins in neutrophiles. Moreover, our findings suggest that the adaptation of redox proteins with respect to their <i>E<sub>m</sub></i> occurs more gradually in response to the pH, since there are also differences between moderate and more extreme acidophiles. In order to address structure function correlations in these redox proteins with respect to structural features affecting the <i>E<sub>m</sub></i>, we conducted a comparative structural analysis of the <i>Ferrovum</i>-derived redox proteins and homologs of <i>Acidithiobacillus</i> spp. and neutrophilic proteobacteria. Hydrophobic contacts in the redox cofactor binding pockets resulting in a low solvent accessibility appear to be the major factor contributing to the more positive <i>E<sub>m</sub>-values</i> in acidophile-derived redox proteins. While additional cysteines in HiPIPs of acidophiles might increase the effective shielding of the [4Fe-4S]-cofactor, the tight shielding of the heme centers in acidophile-derived cytochromes is achieved by a drastic increase in hydrophobic contacts (<i>A.f</i>. Cyc<sub>41</sub>), and by a larger fraction of aromatic residues in the binding pockets (CytC-18, CytC-78). Redox ; Electron transfer ; Chemistry ; Biochemistry ; Biophysics ; Cyclic voltammetry ; Biology ; Electrochemistry ; Inorganic chemistry ; Photochemistry Electrochemical Analysis and Applications ; Metal Extraction and Bioleaching ; Metal-Catalyzed Oxygenation Mechanisms
Authors: Aditya Singh | David T. Limmer
Source: arXiv (Cornell University) (preprint, 2024-02-08)
Field: Chemistry — Physical and Theoretical Chemistry

Splitting probabilities as optimal controllers of rare reactive events The committor constitutes the primary quantity of interest within chemical kinetics as it is understood to encode the ideal reaction coordinate for a rare reactive event. We show the generative utility of the committor, in that it can be used explicitly to produce a reactive trajectory ensemble that exhibits numerically exact statistics as that of the original transition path ensemble. This is done by relating a time-dependent analogue of the committor that solves a generalized bridge problem, to the splitting probability that solves a boundary value problem under a bistable assumption. By invoking stochastic optimal control and spectral theory, we derive a general form for the optimal controller of a bridge process that connects two metastable states expressed in terms of the splitting probability. This formalism offers an alternative perspective into the role of the committor and its gradients, in that they encode forcefields that guarantee reactivity, generating trajectories that are statistically identical to the way that a system would react autonomously. Rare events ; Computer science ; Control theory (sociology) ; Control (management) ; Mathematics ; Artificial intelligence ; Statistics History and advancements in chemistry
Authors: Л. М. Мосула | В. С. Мосула
Source: Medical Science of Ukraine (MSU) (article, 2024-12-30)
Field: Chemistry — Organic Chemistry

IN SILICO SEARCH FOR BIOLOGICALLY ACTIVE CHEMICAL COMPOUNDS AMONG 5-ARYLIDENE SUBSTITUTED DERIVATIVES OF RHODANINE WITH BENZOTHIAZOLE MOIETY IN THE MOLECULES Background. Knowledge about biological targets is important for effective treatment of diseases. The development of computer technology has led the emergence of a number of web tools that can help in this at the initial stages of drug development. Based on the known pharmacological potential of benzotiazole-substituted rhodanines and using modern virtual screening tools, we have carried out a targeted search for biologically active molecules among 5-arylidene substituted rhodanines with a benzothiazole moiety in molecules. The effect of various aryl substituents on the inhibitory activity of series compounds to therapeutic targets has been studied. Aim: to evaluate the potential pharmacological activity 5-arylidene derivatives of N-(4-oxo-2-thioxothiazolidin-3-yl)-2-(2-oxobenzo[d]thiazol-3(2H)-yl) acetamide using a virtual computer program. Material and methods. A series of 5-arylidene derivatives of 2- thioxothiazolidin-4-one (rhodanine) with 2-oxobenzothiazole moeity at the position 3 of the core heterocycle was investigated using the freely available program SuperPred 3.0. Virtual screening of derivatived was performed to identify potential biologically active compounds. Results. Derivatives with different arylidene substituents at the position 5 of the core heterocycle demonstrated a high probability of interaction with multiple targets. The common predicted targets for the compounds with the highest binding scores and model accuracy are Aldose reductase, Transcription intermediary factor 1-alpha and Cathepsin D. The highest binding scores with targets are attributed to compounds 3 (97,49% interaction with Transcription intermediary factor 1-alpha), 5 (98,42% interaction with Aldose reductase) і 9 (98,39% interaction with Cathepsin D). The prediction accuracy for these target models is sufficiently high at 95,56%, 92,38%, and 98,95%, respectively. Conclusion. Among the broad spectrum of probable types of biological activity of the investigated compounds, antitumor activity is predominant. We consider compound 8 to be the most promising derivative with a probable effect on tumor targets. The “structute – activity” analysis revealed an important role of 5-ene substituent of the core rhodanine scaffold in ensuring affinity for these biotargents. The effect of 5-arylidene derivatives on the corresponding protein molecules is higher than predicted for the core compound. The date obtained require further experimental confirmation. Benzothiazole ; Rhodanine ; Moiety ; In silico ; Chemistry ; Molecule ; Combinatorial chemistry ; Stereochemistry ; Organic chemistry ; Biochemistry Synthesis and biological activity ; Synthesis and Characterization of Heterocyclic Compounds ; Computational Drug Discovery Methods
Authors: S. Solomon | S T Tonga | Amos Danlami Agbu | Ibrahim Isah | Jamu Benson Yerima | A T Hirhyel
Source: International Journal of Recent Engineering Science (article, 2024-08-30)
Field: Materials Science — Materials Chemistry

Electronic Properties of Uranium Oxide: A Standard DFT and DFT+U Investigation This paper uses the first principle approach to investigate the electronic properties of uranium oxide. First, the structure was optimized, and the converged parameters were used for the rest of the calculations. For this article, the density functional theory as implemented on the quantum espresso code was, the ultrasoft Perdew Burke Erzhenhorf (PBE) pseudo potential was used, and the 4.0 eV Hubbard parameter was applied to the 5f orbital of the uranium atom in the bulk uranium oxide. The results show that the Hubbard correction applied to the 5f orbital of uranium accounting for the effect of strong electron-electron correlation has improved the electronic properties of uranium oxide by shifting the 5f of uranium orbital away from the Fermi level and increasing the probability of electronic transition from the valence to the conduction band. Finally, the result suggested that Hubbard corrected uranium oxide, which is the best system to use for future calculations, particularly in applications relating to nuclear fuel. Uranium ; Uranium oxide ; Density functional theory ; Materials science ; Oxide ; Chemistry ; Computational chemistry ; Metallurgy Nuclear Materials and Properties ; Nuclear reactor physics and engineering ; Nuclear Physics and Applications
Authors: Benedict Nnachi Alum
Source: INOSR APPLIED SCIENCES (article, 2024-07-12)
Field: Chemistry — Inorganic Chemistry

Advances in Color Chemistry and their Impact on the Paint Industry The broad topic of colour chemistry covers the manufacturing, applications, and invention of pigments and dyes, which are essential to the paint and coating, textile, and electronics industries, among others. Recent advances in colour chemistry have had a significant impact on the paint business, particularly with regard to coating sustainability, usefulness, and durability. This review sought to address how improved pigments, green chemistry solutions, and new technology could revolutionise the paint business as a result of recent advances in colour chemistry. The use of nanotechnology in the creation of nanopigments which have superior optical properties and produce vibrant, long-lasting colours that are resistant to weather is one of the other novel aspects. These advancements take into account the growing customer interest in eco-friendly products in addition to adhering to the rules governing low-VOC formulations. Technology advancements have also improved product positioning and quality through colour matching and customisation choices. We conducted a comprehensive literature review of scientific databases and reports from academic and industrial sources to gain a systematic understanding of current and upcoming trends, issues, and developments in the field of colour chemistry and the paint industry. The importance of colour chemistry in advancing the coating industry across several industries, including automotive, architecture, and consumer electronics, is expected to be increased by future developments in colour chemistry research, which are anticipated to further advance the field of material science and application methods. Keywords: Colour chemistry, Nanopigments, Color matching, Functional coatings, Regulatory compliance Chemistry ; Nanotechnology ; Engineering physics ; Polymer science ; Engineering ; Materials science Pigment Synthesis and Properties ; Dyeing and Modifying Textile Fibers
Authors: Thomas Barrett | Marilyn L. Minus
Source: Journal of Chemical Theory and Computation (article, 2024-12-23)
Field: Materials Science — Polymers and Plastics

<i>Pothos</i>: A Python Package for Polymer Chain Orientation and Microstructure Evolution Monitoring In the pursuit of informing experimental techniques with in silico optimizations, we propose a pip deployable Python package, <i>pothos</i>, to easily determine polymer crystallites within molecular dynamic melts and the chain orientation parameters of atomistic and coarse-grained simulations. The package supports the commonly used ⟨<i>P</i><sub>2</sub>⟩, ⟨<i>P</i><sub>4</sub>⟩, and ⟨<i>P</i><sub>6</sub>⟩ order parameters based on the chain chord vector and utilizes a modified DBSCAN algorithm to determine crystalline regions. The results of analysis are written to text and LAMMPS dump files for visualization and analysis. Python (programming language) ; Computer science ; R package ; Visualization ; Computational science ; Data mining ; Programming language Polymer crystallization and properties ; Rheology and Fluid Dynamics Studies ; Machine Learning in Materials Science
Authors: Zhenbang Xiao | Yu Wang | Shunyu Liu | Huiqiong Wang | Mingli Song | Tongya Zheng
Source: arXiv (Cornell University) (preprint, 2024-03-22)
Field: Chemistry — Physical and Theoretical Chemistry

Simple Graph Condensation The burdensome training costs on large-scale graphs have aroused significant interest in graph condensation, which involves tuning Graph Neural Networks (GNNs) on a small condensed graph for use on the large-scale original graph. Existing methods primarily focus on aligning key metrics between the condensed and original graphs, such as gradients, output distribution and trajectories of GNNs, yielding satisfactory performance on downstream tasks. However, these complex metrics necessitate intricate external parameters and can potentially disrupt the optimization process of the condensation graph, making the condensation process highly demanding and unstable. Motivated by the recent success of simplified models across various domains, we propose a simplified approach to metric alignment in graph condensation, aiming to reduce unnecessary complexity inherited from intricate metrics. We introduce the Simple Graph Condensation (SimGC) framework, which aligns the condensed graph with the original graph from the input layer to the prediction layer, guided by a pre-trained Simple Graph Convolution (SGC) model on the original graph. Importantly, SimGC eliminates external parameters and exclusively retains the target condensed graph during the condensation process. This straightforward yet effective strategy achieves a significant speedup of up to 10 times compared to existing graph condensation methods while performing on par with state-of-the-art baselines. Comprehensive experiments conducted on seven benchmark datasets demonstrate the effectiveness of SimGC in prediction accuracy, condensation time, and generalization capability. Our code is available at https://github.com/BangHonor/SimGC. Simple (philosophy) ; Condensation ; Graph ; Mathematics ; Computer science ; Combinatorics ; Physics ; Thermodynamics ; Philosophy ; Epistemology History and advancements in chemistry
Authors: Bo-Chen Su | Duc Huy Nguyen | Paul C.-P. Chao
Source: Research Square (preprint, 2024-02-19)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Predicting Frequency Deviation of a Crystal Oscillator based on Long Short-Term Memory Network and Transfer Learning Technique Abstract Crystal oscillators are fundamental to an extensive range of electronic systems, spanning computers, mobile phones, and automotive electronics. Their significance is accentuated in high-precision applications such as global positioning systems (GPS) and aerospace systems where the frequency-temperature characteristics and thermal hysteresis phenomena are of paramount importance. This study introduces a groundbreaking approach for predicting frequency deviations arising from thermal hysteresis using Long Short-Term Memory (LSTM) networks. Contrary to prior research which predominantly utilized cubic functions to model frequency-temperature characteristics and frequently overlooked thermal hysteresis, this investigation distinguishes itself by leveraging LSTM. The proposed methodology is aptly designed to model both time-dependent and temperature-dependent variations, consequently offering a heightened precision in predicting frequency deviations. By integrating transfer learning techniques, the model's adaptability to diverse databases is augmented, broadening its utility. Experimental evaluations with real-world data underscore the preeminence of the introduced method, registering a root mean square error (RMSE) of less than 0.05 ppm, more favorable than that by the traditional cubic functions and all the prior arts. Hysteresis ; Mean squared error ; Computer science ; Adaptability ; Root mean square ; Term (time) ; Transfer function ; Range (aeronautics) ; Transfer of learning ; Thermal ; Electronic engineering ; Artificial intelligence ; Engineering ; Electrical engineering ; Physics ; Mathematics ; Statistics ; Aerospace engineering ; Meteorology ; Condensed matter physics Magnetic Properties and Applications ; Heat Transfer and Boiling Studies ; Thermal properties of materials
Authors: T. LIPIŃSKI
Source: Materials research proceedings (article, 2024-10-14)
Field: Materials Science — Metals and Alloys

Corrosion the X2CrNiMoN25-7-4 Super Duplex Stainless Steel in 65% Nitric Acid at Boiling Temperature Abstract. Corrosion of corrosion-resistant steels is a natural phenomenon, but knowing its speed allows you to determine the durability of a structural element. The corrosion rate may depend on the surface condition, which is shaped during the manufacturing process. Thin steels are produced by cold forming, while thick steels are produced by hot forming. Therefore, they have a surface layer with different mechanical, physical and chemical properties. The paper discusses the issue of determining the corrosion rate for cold-rolled and hot-rolled steel. The tests were carried out on samples taken from sheets in the delivered condition. The samples were soaked in 65% nitric acid at boiling point. As a result of the research, it was found that steel produced in the cold rolling process has greater corrosion resistance, especially in the first period of corrosion. This is most likely due to the surface strengthening of the material as a result of cold forming. Boiling ; Nitric acid ; Duplex (building) ; Corrosion ; Metallurgy ; Materials science ; Boiling point ; Chemistry ; Chemical engineering ; Engineering Hydrogen embrittlement and corrosion behaviors in metals ; Metal and Thin Film Mechanics ; Corrosion Behavior and Inhibition
Authors: Nathan A. Mahynski | Vincent K. Shen
Source: Journal of Chemical Theory and Computation (article, 2024-02-22)
Field: Materials Science — Materials Chemistry

Programming Interfacial Porosity and Symmetry with Escherized Colloids We simultaneously designed the porosity and plane symmetry of self-assembling colloidal films by using isohedral tiles to determine the location and shape of enthalpically interacting surface patches on motifs being functionalized. The symmetries of both the tile and motif determine the plane symmetry group of the final assembly. Previous work has either ignored symmetry considerations altogether or accounted for only the tile's properties, applicable only when the motif is asymmetric; this approach provides a complete account and enables the design of symmetric colloids using this tile-based approach, which are often more practical to manufacture. We present the methodology, based on the type of the tile, and provide computational tools that enable the automatic classification of all tiles for a given motif and the optimization of the tile to fit the motif, sometimes referred to as "Escherization". Tile ; Porosity ; Motif (music) ; Symmetry (geometry) ; Homogeneous space ; Colloid ; Computer science ; Topology (electrical circuits) ; Geometry ; Materials science ; Mathematics ; Physics ; Combinatorics ; Engineering ; Composite material ; Chemical engineering Pickering emulsions and particle stabilization ; Photonic Crystals and Applications ; Advanced Materials and Mechanics
Authors: Ghalia Merzougui | Nadia Boulelouah | Abdelheq Mokhtari | Abderraouf Hebira
Source: Ingénierie des systèmes d information (article, 2024-02-27)
Field: Chemistry — Analytical Chemistry

Improving the Approval Process for Durum Wheat Grain Quality in Algeria Using Computer Vision and Machine Learning This research aimed to develop computer vision and machine learning models to improve durum wheat quality control in Algeria.Durum wheat is a vital cereal crop in Algeria used for many staple foods.Currently, quality control relies on manual evaluation which is too lengthy and tedious.To address this, models utilizing image processing and 200 image descriptors, including 102 texture features, 8 morphological features, and 90 colour features, were developed to automate classification of durum wheat species, varieties, and impurities.An optimized Support Vector Machine (SVM) model was implemented hyperparameters tuning.The models achieved exceptional performance, classifying durum wheat species with 99% accuracy, varieties with 95% accuracy, and impurities with 94% accuracy.This illustrates the significant potential of tailored computer vision and machine learning techniques to enable automated quality control.The models could be integrated into crop certification workflows, increasing productivity. Quality (philosophy) ; Agricultural engineering ; Process (computing) ; Grain quality ; Computer science ; Artificial intelligence ; Engineering ; Agronomy ; Biology Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI
Authors: Paulo F. Bedaque | Hersh Kumar | Andy Sheng
Source: arXiv (Cornell University) (preprint, 2024-10-22)
Field: Materials Science — Materials Chemistry

A Machine Learning Approach to Trapped Many-Fermion Systems We apply a variational Ansatz based on neural networks to the problem of spin-$1/2$ fermions in a harmonic trap interacting through a short distance potential. We showed that standard machine learning techniques lead to a quick convergence to the ground state, especially in weakly coupled cases. Higher couplings can be handled efficiently by increasing the strength of interactions during "training". Fermion ; Computer science ; Artificial intelligence ; Physics ; Particle physics Machine Learning in Materials Science ; Topological Materials and Phenomena ; Surface and Thin Film Phenomena
Authors: Adama Traore | Mahamadou Konare | Syed Tahir Ata-Ul-Karim | Sunusi Amin Abubakar | Seydou Traoré | Aiwang Duan | Ben Zhao
Source: World Journal of Advanced Research and Reviews (article, 2024-11-03)
Field: Chemistry — Analytical Chemistry

Comparative analysis of deep neural network and artificial neural network for predicting wheat water content using UAV multispectral and thermal imagers Water is a vital component for the growth of wheat and the quality of its grains. The measurement of plant water content (PWC) serves as a crucial indicator for assessing the water status of crops, thereby guiding effective irrigation management practices. Recent advancements in technology, particularly the use of multispectral and thermal imaging from unmanned aerial vehicles (UAVs), offer the capability to capture a comprehensive view of PWC variability across agricultural fields. This study aimed to create predictive models for PWC utilizing artificial neural networks (ANN), deep neural networks (DNN), and traditional stepwise regression techniques, all based on the analysis of multispectral and thermal imagery. To effectively evaluate the protein content in wheat, we combined high-resolution thermal and multispectral imaging techniques with machine learning approaches. This assessment was carried out through three distinct experiments, which included one conducted in a rainout shelter and two performed under rainfed conditions. The findings demonstrated that UAV-derived multispectral imagery, when coupled with machine learning models, can effectively predict wheat plant water content with remarkable precision. Notably by considering all the dataset, DNN model exhibited superior performance (R2=0.96, ENS=0.98, RMSE=1.37%, MAE=0.98%) compared to both the ANN (R2=0.95, ENS=0.95, RMSE=1.88%, MAE=1.46%,) and the stepwise regression model (SRM) (R2=0.67, ENS=0.51, RMSE=10.79%, MAE=9.03%). Across all machine learning approaches, both the DNN and ANN models significantly outperformed the stepwise regression model in predictive accuracy. The statistical outcomes derived from the calibration phase indicate that both the trained Artificial Neural Networks (ANN) and Deep Neural Networks (DNN) serve as effective instruments for accurate prediction of PWC. Notably, the DNN network demonstrates superior accuracy compared to ANN. Artificial neural network ; Multispectral image ; Artificial intelligence ; Computer science ; Environmental science Spectroscopy and Chemometric Analyses