jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: David Heinrich Müller | Marieke Börger | Julia Thien | Hans‐Jürgen Koß
Source: Analytical and Bioanalytical Chemistry (article, 2023-11-20)
Field: Chemistry — Analytical Chemistry

The Good pH probe: non-invasive pH in-line monitoring using Good buffers and Raman spectroscopy In bioprocesses, the pH value is a critical process parameter that requires monitoring and control. For pH monitoring, potentiometric methods such as pH electrodes are state of the art. However, they are invasive and show measurement value drift. Spectroscopic pH monitoring is a non-invasive alternative to potentiometric methods avoiding this measurement value drift. In this study, we developed the Good pH probe, which is an approach for spectroscopic pH monitoring in bioprocesses with an effective working range between pH 6 and pH 8 that does not require the estimation of activity coefficients. The Good pH probe combines for the first time the Good buffer 3-(N-morpholino)propanesulfonic acid (MOPS) as pH indicator with Raman spectroscopy as spectroscopic technique, and Indirect Hard Modeling (IHM) for the spectral evaluation. During a detailed characterization, we proved that the Good pH probe is reversible, exhibits no temperature dependence between 15 and 40 °C, has low sensitivity to the ionic strength up to 1100 mM, and is applicable in more complex systems, in which other components significantly superimpose the spectral features of MOPS. Finally, the Good pH probe was successfully used for non-invasive pH in-line monitoring during an industrially relevant enzyme-catalyzed reaction with a root mean square error of prediction (RMSEP) of 0.04 pH levels. Thus, the Good pH probe extends the list of critical process parameters monitorable using Raman spectroscopy and IHM by the pH value. Raman spectroscopy ; Chemistry ; Potentiometric titration ; Analytical Chemistry (journal) ; Ionic strength ; pH indicator ; Accuracy and precision ; Spectroscopy ; pH meter ; Electrode ; Chromatography ; Aqueous solution ; Physical chemistry ; Optics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Analytical Chemistry and Sensors
Authors: Parvathy Chittur Subramanianprasad | Yihan Ma | Achintha Ihalage | Yang Hao
Source: Sensors (article, 2023-06-13)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Active Learning Optimisation of Binary Coded Metasurface Consisting of Wideband Meta-Atoms The design of a metasurface array consisting of different unit cells with the objective of minimizing its radar cross-section is a popular research topic. Currently, this is achieved by conventional optimisation algorithms such as genetic algorithm (GA) and particle swarm optimisation (PSO). One major concern of such algorithms is the extreme time complexity, which makes them computationally forbidden, particularly at large metasurface array size. Here, we apply a machine learning optimisation technique called active learning to significantly speed up the optimisation process while producing very similar results compared to GA. For a metasurface array of size 10 × 10 at a population size of 106, active learning took 65 min to find the optimal design compared to genetic algorithm, which took 13,260 min to return an almost similar optimal result. The active learning optimisation strategy produced an optimal design for a 60 × 60 metasurface array 24× faster than the approximately similar result generated by GA technique. Thus, this study concludes that active learning drastically reduces computational time for optimisation compared to genetic algorithm, particularly for a larger metasurface array. Active learning using an accurately trained surrogate model also contributes to further lowering of the computational time of the optimisation procedure. Genetic algorithm ; Particle swarm optimization ; Computer science ; Binary number ; Active learning (machine learning) ; Algorithm ; Computational complexity theory ; Population ; Artificial intelligence ; Machine learning ; Mathematics Metamaterials and Metasurfaces Applications ; Antenna Design and Analysis ; Antenna Design and Optimization
Authors: Mohamed Abdessamia Chakchouk | Pierre‐Richard Dahoo | Abdelkhalak El Hami | Azzedine Lakhlifi | Dalal Fadil | Wajih Gafsi | Mohamed Haddar
Source: The European Physical Journal D (article, 2023-10-01)
Field: Chemistry — Spectroscopy

IR spectroscopic observation windows and analysis for environmental issues: application to CO2 Molecule ; Context (archaeology) ; Chemistry ; Infrared ; Infrared spectroscopy ; Chemical physics ; Atomic physics ; Computational chemistry ; Physics ; Quantum mechanics ; Organic chemistry Spectroscopy and Laser Applications ; Atmospheric Ozone and Climate ; Atmospheric and Environmental Gas Dynamics
Authors: Yujie Huang | Jun Lan | Chao Wu | Ruixue Zhang | Hongning Zheng | Shilong Fan | Fei Xu
Source: Biophysical Journal (article, 2023-05-23)
Field: Materials Science — Biomaterials

Stability of collagen heterotrimer with same charge pattern and different charged residue identities Salt (chemistry) ; Salt bridge ; Chemistry ; Molecular dynamics ; Crystallography ; Circular dichroism ; Folding (DSP implementation) ; Chemical physics ; Biophysics ; Nanotechnology ; Materials science ; Computational chemistry ; Biochemistry ; Biology ; Mutant ; Physical chemistry Collagen: Extraction and Characterization ; Marine Biology and Environmental Chemistry ; Calcium Carbonate Crystallization and Inhibition
Authors: X. Xin | Junjie Zhou | Quan He | Yangfeng Peng | Yongming Wei | Hongliang Zhao | Tianzhong Tong
Source: Chirality (article, 2023-12-06)
Field: Chemistry — Physical and Theoretical Chemistry

Resolution of <i>trans</i>‐1,2‐cyclohexanedicarboxylic acid containing two carboxylic groups by forming diastereomeric salts based on feeding molar ratio control To investigate the thermodynamic and molecular self-assembly mechanism of trans-1,2-cyclohexane dicarboxylic acid containing two carboxylic acid groups in the chiral resolution process, (S)-phenylethylamine was used as the chiral resolving agent. Two stoichiometric salts were formed when the raw materials were fed at different molar ratios: cyclohexane dicarboxylate monophenylethylamine salt and cyclohexane dicarboxylate diphenylethylamine salt. When the molar ratio of the (S)-phenylethylamine to trans-1,2-cyclohexane dicarboxylic acid was less than 3:1, trans-(1S,2S)-cyclohexane dicarboxylic acid was obtained with 97 e.e% purity. But when the molar ratio exceeded 3:1, the product was the racemic trans-(1,2)-cyclohexane dicarboxylic acid. In addition, single crystal structures of more soluble mono-salt, less soluble mono-salt, and less soluble di-salt were obtained. The weak intermolecular interactions and the way of the molecules packing in the crystals were analyzed. The hydrogen bond was stronger in the less soluble salt than that in the more soluble salt. And a "lock-and-key" structure in the hydrophobic layers makes it more tightly packed through the van der Waals interaction, which is responsible for the stability of less soluble salts. Chemistry ; Cyclohexane ; Diastereomer ; Salt (chemistry) ; Dicarboxylic acid ; Carboxylic acid ; Hydrogen bond ; Organic chemistry ; Polymer chemistry ; Molecule Crystallography and molecular interactions ; Crystallization and Solubility Studies ; Analytical Chemistry and Chromatography
Authors: Maksim N. Kosenko | G. S. Onkhonova | Ivan M. Susloparov | А. Б. Рыжиков
Source: Biophysical Reviews (review, 2023-09-29)
Field: Materials Science — Materials Chemistry

SARS-CoV-2 proteins structural studies using synchrotron radiation Structural biology ; Computational biology ; Biomolecule ; Protein structure ; Synchrotron radiation ; Biology ; Biophysics ; Chemistry ; Physics ; Cell biology ; Biochemistry Enzyme Structure and Function ; Protein Structure and Dynamics ; RNA and protein synthesis mechanisms
Authors: A.D. Brown | Jonathan Beaumont | David B. Thomas | Julian C. Shillcock | Matthew Naylor | Graeme M. Bragg | Mark Vousden | Simon W. Moore | Shane Fleming
Source: ACM Transactions on Parallel Computing (article, 2023-02-20)
Field: Materials Science — Materials Chemistry

POETS: An Event-driven Approach to Dissipative Particle Dynamics HPC clusters have become ever more expensive, both in terms of capital cost and energy consumption; some estimates suggest that competitive installations at the end of the next decade will require their own power station. One way around this looming problem is to design bespoke computing engines, but while the performance benefits are good, the design costs are huge and cannot easily be amortized. Partially Ordered Event Triggered System (POETS)—the focus of this article—seeks to exploit a middle way: The architecture is tuned to a specific algorithmic pattern but, within that constraint, is fully programmable. POETS software is quasi-imperative: The user defines a set of sequential event handlers, defines the topology of a (typically large) concurrent ensemble of these, and lets them interact. The “solution” may be exfiltrated from the emergent behaviour of the ensemble. In this article, we describe (briefly) the architecture, and an example computational chemistry application, dissipative particle dynamics (DPD). The DPD algorithm is traditionally implemented using parallel computational techniques, but we re-cast it as a concurrent compute problem that is then ideally suited to POETS. Our prototype system is realised on a cluster of 48 FPGAs providing 50K concurrent hardware threads, and we report performance speedups of over two orders of magnitude better than a single thread baseline comparator and scaling behaviour that is almost constant. The results are validated against a “conventional” implementation. Computer science ; Exploit ; Parallel computing ; Thread (computing) ; Distributed computing ; Discrete event simulation ; Event (particle physics) ; Architecture ; Simulation ; Programming language Block Copolymer Self-Assembly ; Machine Learning in Materials Science ; Markov Chains and Monte Carlo Methods
Authors: Alessandra Carbone | Aurélien Decelle | Lorenzo Rosset | Beatriz Seoane
Source: arXiv (Cornell University) (preprint, 2023-07-13)
Field: Materials Science — Materials Chemistry

Fast and Functional Structured Data Generators Rooted in Out-of-Equilibrium Physics In this study, we address the challenge of using energy-based models to produce high-quality, label-specific data in complex structured datasets, such as population genetics, RNA or protein sequences data. Traditional training methods encounter difficulties due to inefficient Markov chain Monte Carlo mixing, which affects the diversity of synthetic data and increases generation times. To address these issues, we use a novel training algorithm that exploits non-equilibrium effects. This approach, applied on the Restricted Boltzmann Machine, improves the model's ability to correctly classify samples and generate high-quality synthetic data in only a few sampling steps. The effectiveness of this method is demonstrated by its successful application to four different types of data: handwritten digits, mutations of human genomes classified by continental origin, functionally characterized sequences of an enzyme protein family, and homologous RNA sequences from specific taxonomies. Markov chain Monte Carlo ; Computer science ; Markov chain ; Exploit ; Population ; Hidden Markov model ; Mixing (physics) ; Sampling (signal processing) ; Artificial intelligence ; Machine learning ; Computational biology ; Data mining ; Biology ; Bayesian probability ; Physics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Nanopore and Nanochannel Transport Studies
Authors: Krishnaraj Kundavu | S. Mondal | Amrita Bhattacharya
Source: Materials Advances (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Machine learning the vibrational free energy of perovskites The Helmholtz (vibrational) free energy of ABX 3 perovskites is modelled as a 3rd-order polynomial of temperature. Then, highly accurate machine-learning models are trained to predict the stability and phase transition in competing phases of the perovskites. Helmholtz free energy ; ABX test ; Energy (signal processing) ; Phase transition ; Stability (learning theory) ; Phase (matter) ; Order (exchange) ; Materials science ; Statistical physics ; Thermodynamics ; Condensed matter physics ; Computer science ; Physics ; Machine learning ; Mathematics ; Quantum mechanics ; Statistics Machine Learning in Materials Science ; Perovskite Materials and Applications ; Electronic and Structural Properties of Oxides
Authors: Shrinivas Deshpande | Udaykumar Nidoni | Sharanagouda Hiregoudar | K. T. Ramappa | Devanand Maski | Nagaraj Naik
Source: Current Science (article, 2023-03-25)
Field: Chemistry — Analytical Chemistry

Performance of Advanced Machine Learning Models in the Prediction of Amylose Content in Rice Using Internet of Things-Based Colorimetric Sensor Rice ageing is a complicated process that is difficult to examine methodically.Several physicochemical properties of rice change with age as a function of moisture content and storage temperature.Among these qualities, amylose content is the most important and numerous metrics depend on it.Several sensors, Internet of Things, Information and Communication Technology, artificial intelligence and machine learning (ML) approaches are being used in technological interventions to tackle this problem.In the present study, seven advanced ML models were evaluated to classify the different concentrations of amylose using light-intensity data obtained by the novel colorimetric amylose sensor.From the performance of the evaluated ML models, it was observed that for the light intensity dataset obtained from the sensor, higher and similar model parameters and an accuracy value of 0.77 were observed for both artificial neural network (ANN) and k-nearest neighbour (KNN) algorithms, followed by accuracy values of 0.75, 0.74, 0.65, 0.61 and 0.61 respectively, for the decision tree, random forest, AdaBoost, logistic regression and support vector machine algorithms.Thus ANN and KNN are promising in predicting the different classes of amylose in rice. Amylose ; Content (measure theory) ; Internet of Things ; Computer science ; The Internet ; Predictive modelling ; Machine learning ; Artificial intelligence ; Chemistry ; Mathematics ; World Wide Web ; Food science ; Starch Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI
Authors: Xin Yang | Xianglin Pei | Jiajie Xu | Zhipeng Yang | Wei Gong | Jincheng Zhong
Source: e-Polymers (article, 2023-01-01)
Field: Materials Science — Polymers and Plastics

Influence of temperature distribution on the foaming quality of foamed polypropylene composites Abstract The foamed polypropylene (PP) composites were prepared by injection molding process. Fourier’s law and software were used to calculate and simulate the internal temperature distribution of PP composites, respectively, and the influence of the temperature distribution on the foaming quality of foamed PP composites was further analyzed. The result showed that the calculative and simulated results of temperature distribution in different thermal transfer directions had great reproducibility. In different isothermal planes, the temperature from the nozzle to the dynamic mold gradually decreased. The isothermal plane with a temperature of 370.36 K had a better foaming quality, average diameter of cell and cell density were 28.46 µm and 3.7 × 10 10 cells·cm −3 , respectively. In different regions of the same isothermal plane, the temperature gradually decreased from the center to the edge. The foaming quality in the region (c) at a temperature of 335.86 K was ideal, and the average diameter of cell and the cell density were 26.5 µm and 2.39 × 10 10 cells·cm −3 , respectively. This work could provide prediction for improving the foaming quality of foamed polyolefin composites. Materials science ; Composite material ; Polypropylene ; Isothermal process ; Molding (decorative) ; Mold ; Nozzle Polymer Foaming and Composites ; Polymer crystallization and properties ; Epoxy Resin Curing Processes
Authors: Egor S. Bulavko | Marina A. Pak | Dmitry N. Ivankov
Source: Biomolecules (article, 2023-08-20)
Field: Chemistry — Inorganic Chemistry

In Silico Simulations Reveal Molecular Mechanism of Uranyl Ion Toxicity towards DNA-Binding Domain of PARP-1 Protein The molecular toxicity of the uranyl ion (UO<sub>2</sub><sup>2+</sup>) in living cells is primarily determined by its high affinity to both native and potential metal-binding sites that commonly occur in the structure of biomolecules. Recent advances in computational and experimental research have shed light on the structural properties and functional impacts of uranyl binding to proteins, organic ligands, nucleic acids, and their complexes. In the present work, we report the results of the computational investigation of the uranyl-mediated loss of DNA-binding activity of PARP-1, a eukaryotic enzyme that participates in DNA repair, cell differentiation, and the induction of inflammation. The latest experimental studies have shown that the uranyl ion directly interacts with its DNA-binding subdomains, zinc fingers Zn1 and Zn2, and alters their tertiary structure. Here, we propose an atomistic mechanism underlying this process and compute the free energy change along the suggested pathway. Our Quantum Mechanics/Molecular Mechanics (QM/MM) simulations of the Zn2-UO<sub>2</sub><sup>2+</sup> complex indicate that the uranyl ion replaces zinc in its native binding site. However, the resulting state is destroyed due to the spontaneous internal hydrolysis of the U-Cys162 coordination bond. Despite the enthalpy of hydrolysis being +2.8 kcal/mol, the overall reaction free energy change is -0.6 kcal/mol, which is attributed to the loss of domain's native tertiary structure originally maintained by a zinc ion. The subsequent reorganization of the binding site includes the association of the uranyl ion with the Glu190/Asp191 acidic cluster and significant perturbations in the domain's tertiary structure driven by a further decrease in the free energy by 6.8 kcal/mol. The disruption of the DNA-binding interface revealed in our study is consistent with previous experimental findings and explains the loss of PARP-like zinc fingers' affinity for nucleic acids. Uranyl ; Chemistry ; DNA ; Binding energy ; Molecular dynamics ; Molecular mechanics ; Binding site ; Biophysics ; Crystallography ; Computational chemistry ; Ion ; Biochemistry ; Organic chemistry ; Biology Radioactive element chemistry and processing
Authors: Srdjan Perišić | Katarina Kalevski | Aleksandar Grujić | Dragutin Nedeljković | Jasna Stajić-Trošić | Vesna Radojević
Source: Polymers (article, 2023-12-14)
Field: Materials Science — Polymers and Plastics

Effect of Moisture on the Mechanical Properties of Wood–Plastic Composites Hybridized with Metal Grid Layers Wood-plastic composites (WPCs) are some of the most common modern composite materials for interior and exterior design that combine natural waste wood properties and the molding possibility of a thermoplastic polymer binder. The addition of reinforcing elements, binding agents, pigments, and coatings, as well as changes to the microstructure and composition, can all affect the quality of WPCs for particular purposes. To improve the properties, hybrid composite panels of WPCs with 30 wt. % and 40 wt. % of wood content and reinforced with one or three metal grid layers were prepared sequentially by extrusion and hot pressure molding. The results show an average 20% higher moisture absorption for composites with higher wood content. A high impact test (HIT) revealed that the absorbed energy of deformation increased with the number of metal grid layers, regardless of the wood content, around two times for all samples before water immersion and around ten times after water absorption. Also, absorbed energy increases with raised wood content, which is most pronounced in three-metal-grid samples, from 21 J to 26 J (before swelling) and from 15 J to 24 J (after swelling). Flexural tests follow the trends observed by HIT, indicating around 65% higher strength for samples with three metal grid layers vs. samples without a metal grid before water immersion and around 80% higher strength for samples with three metal grid layers vs. samples without a grid after water absorption. The synthesis route, double reinforcing (wood and metal), applied methods of characterization, and optimization according to the obtained results provide a WPC with improved mechanical properties ready for an outdoor purpose. Materials science ; Composite material ; Wood-plastic composite ; Absorption of water ; Flexural strength ; Molding (decorative) ; Thermoplastic ; Composite number ; Extrusion ; Moisture ; Metal ; Swelling ; Microstructure ; Metallurgy Natural Fiber Reinforced Composites ; Wood Treatment and Properties ; Cellular and Composite Structures
Source: International Research Journal of Modernization in Engineering Technology and Science (article, 2023-01-15)
Field: Chemistry — Analytical Chemistry

PLANT DISEASE DETECTION USING DEEP LEARNING Agriculture has been a crucial occupation in India for centuries and continues to be a major source of livelihood.However, with the increasing threat of global warming and plant diseases, this occupation faces challenges.Plant diseases can lead to significant production and economic losses in agriculture.Identifying the specific disease can be a difficult task and requires expert knowledge.Symptoms such as colored spots or streaks on leaves are often visible signs of a disease.These diseases are commonly caused by microorganisms including fungi, bacteria, and viruses.Neural networks, specifically convolutional neural networks (CNNs), have been gaining attention as a potential tool in various fields, including agriculture.CNNs use mathematical functions to process numerical inputs and produce numerical outputs.The application of CNNs in agriculture could include identifying and quantifying plant diseases.Currently, disease identification is often done through visual inspection by experts, which can be time-consuming in large farms.Implementing CNNs in early detection and recognition of plant diseases could potentially improve the quality of agricultural products. Deep learning ; Artificial intelligence ; Disease ; Computer science ; Medicine ; Internal medicine Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI