jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Daniel Conde-Torres | Pablo F. Garrido | Martín Calvelo | Ángel Piñeiro | Rebeca García‐Fandiño
Source: International Journal of Molecular Sciences (article, 2022-03-15)
Field: Materials Science — Biomaterials

Molecular Dynamics Simulations of Transmembrane Cyclic Peptide Nanotubes Using Classical Force Fields, Hydrogen Mass Repartitioning, and Hydrogen Isotope Exchange Methods: A Critical Comparison Self-assembled cyclic peptide nanotubes with alternating <i>D</i>- and <i>L</i>-amino acid residues in the sequence of each subunit have attracted a great deal of attention due to their potential for new nanotechnology and biomedical applications, mainly in the field of antimicrobial peptides. Molecular dynamics simulations can be used to characterize these systems with atomic resolution at different time scales, providing information that is difficult to obtain via wet lab experiments. However, the performance of classical force fields typically employed in the simulation of biomolecules has not yet been extensively tested with this kind of highly constrained peptide. Four different classical force fields (AMBER, CHARMM, OPLS, and GROMOS), using a nanotube formed by eight <i>D</i>,<i>L</i>-α-cyclic peptides inserted into a lipid bilayer as a model system, were employed here to fill this gap. Significant differences in the pseudo-cylindrical cavities formed by the nanotubes were observed, the most important being the diameter of the nanopores, the number and location of confined water molecules, and the density distribution of the solvent molecules. Furthermore, several modifications were performed on GROMOS54a7, aiming to explore acceleration strategies of the MD simulations. The hydrogen mass repartitioning (HMR) and hydrogen isotope exchange (HIE) methods were tested to slow down the fastest degrees of freedom. These approaches allowed a significant increase in the time step employed in the equation of the motion integration algorithm, from 2 fs up to 5-7 fs, with no serious changes in the structural and dynamical properties of the nanopores. Subtle differences with respect to the simulations with the unmodified force fields were observed in the concerted movements of the cyclic peptides, as well as in the lifetime of several H-bonds. All together, these results are expected to contribute to better understanding of the behavior of self-assembled cyclic peptide nanotubes, as well as to support the methods tested to speed up general MD simulations; additionally, they do provide a number of quantitative descriptors that are expected to be used as a reference to design new experiments intended to validate and complement computational studies of antimicrobial cyclic peptides. Nanopore ; Molecular dynamics ; Force field (fiction) ; Lipid bilayer ; Biomolecule ; Chemical physics ; Peptide ; Chemistry ; Molecule ; Nanotechnology ; Hydrogen bond ; Computational chemistry ; Materials science ; Membrane ; Physics Supramolecular Self-Assembly in Materials ; Antimicrobial Peptides and Activities ; Lipid Membrane Structure and Behavior
Authors: Sandro Spagnolo | Katharina Davoudian | Soha Ahmadi | Edmund Chan | Tibor Hianik | Michael Thompson
Source: Chemosensors (article, 2022-10-20)
Field: Materials Science — Surfaces, Coatings and Films

Thiol-Based Probe Linker with Antifouling Properties for Aptasensor Development Surfaces with antifouling properties are critical for optimizing biosensors to improve the selectivity and specificity of analyte detection in complex biological samples. This work describes the four-step synthesis of 3-dithiothreitol propanoic acid (DTTCOOH), a new antifouling thiol linker that (a) significantly reduces fouling of raw human serum samples and (b) binds amino receptors via its terminal carboxylic acid group. DTTCOOH was successfully functionalized on quartz crystal microbalance (QCM) discs and used to anchor penicillin-binding aptamers. Relative to bare and coated (11-mercaptoundecanoic acid (MUA) and 1-undecanethiol (UDT)) QCM crystals, DTTCOOH’s antifouling improved by approximately 75–86%. Following aptamer/ethanolamine extension, the modified DTTCOOH layer reduced serum fouling by approximately 95–97% compared to bare and coated (MUA, UDT) crystals. QCM with dissipation (QCM-D) monitoring, contact goniometry, and cyclic voltammetry techniques were used to compare the DTTCOOH surfaces with quartz crystals functionalized with hydrophobic and hydrophilic molecules. Quartz crystal microbalance ; Biofouling ; Aptamer ; Contact angle ; Biosensor ; Analyte ; Chemistry ; Linker ; Thiol ; Fouling ; Cyclic voltammetry ; Combinatorial chemistry ; Chromatography ; Materials science ; Organic chemistry ; Electrochemistry ; Adsorption ; Biochemistry Polymer Surface Interaction Studies ; Advanced biosensing and bioanalysis techniques ; Acoustic Wave Resonator Technologies
Authors: Jianping Li | Christos T. Maravelias | Reid C. Van Lehn
Source: Industrial & Engineering Chemistry Research (article, 2022-06-13)
Field: Chemistry — Spectroscopy

Adaptive Conformer Sampling for Property Prediction Using the Conductor-like Screening Model for Real Solvents The valorization of lignocellulose-derived bioproducts requires effective separation from excessive water. Liquid–liquid extraction is a promising low-energy separation technology, but effective extraction requires solvent selection based on the thermodynamic properties of the bioproduct and solvent components. We propose a computational framework for predicting such properties by developing an adaptive conformer selection approach for use with COSMO-RS (conductor-like screening model for real solvents) calculations. In this framework, molecular dynamics simulations are used to generate many molecular structures (conformers) at representative temperatures in varying solvent environments. Conformers are then clustered based on structural metrics in a low-dimensional space and selected using a mixed-integer quadratic programming problem to iteratively insert a sampled conformer. At each iteration, we determine bioproduct properties using COSMO-RS. We demonstrate the capability of the proposed framework on representative bioproducts to show convergence of the adaptive sampling toward experimentally measured properties with fewer calculations than required by random conformer sampling, enabling the improved screening of solvent systems for liquid-phase separation. Conformational isomerism ; COSMO-RS ; Sampling (signal processing) ; Adaptive sampling ; Bioproducts ; Materials science ; Solvent ; Computer science ; Statistical physics ; Thermodynamics ; Biological system ; Mathematical optimization ; Chemistry ; Molecule ; Mathematics ; Ionic liquid ; Physics ; Organic chemistry ; Monte Carlo method Analytical Chemistry and Chromatography ; Crystallization and Solubility Studies ; Protein Structure and Dynamics
Authors: Jiawei Guo | Cheng Chen | Chen Chen | Enguang Zuo | Bingyu Dong | Xiaoyi Lv | Wudao Yang
Source: Scientific Reports (article, 2022-05-13)
Field: Chemistry — Analytical Chemistry

Near-infrared spectroscopy combined with pattern recognition algorithms to quickly classify raisins With the development of commodity economy, the emergence of fake and shoddy raisin has seriously harmed the interests of consumers and enterprises. To deal with this problem, a classification method combining near-infrared spectroscopy and pattern recognition algorithms were proposed for adulterated raisins. In this study, the experiment was performed by three kinds of raisins in Xinjiang (Hongxiangfei, Manaiti, Munage). After collecting and normalizing the spectral data, we compared the spectra of three kinds of raisins. Next the principal component analysis (PCA) was preformed to compress the dimension of the spectral data, and then classification models including support vector machine (SVM), multiscale fusion convolutional neural network (MCNN) and improved AlexNet were established to identify raisins. The accuracy of SVM, MCNN, and improved AlexNet is 100%, 92.83%, and 97.78% respectively. This study proves that near-infrared spectroscopy combined with pattern recognition is feasible for the raisin inspection. Principal component analysis ; Support vector machine ; Pattern recognition (psychology) ; Convolutional neural network ; Computer science ; Artificial intelligence ; Dimension (graph theory) ; Mathematics Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Meat and Animal Product Quality
Authors: Víctor Martin-Dominguez | Paula I. Aleman Cabrera | Laslo Eidt | Ulf Pruesse | Anja Kuenz | Miguel Ladero | Victoria E. Santos
Source: Fermentation (article, 2022-01-30)
Field: Materials Science — Biomaterials

Production of Fumaric Acid by Rhizopus arrhizus NRRL 1526: A Simple Production Medium and the Kinetic Modelling of the Bioprocess Fumaric acid is a promising monomer to obtain biomass-based polyesters and polyamides, and it is mainly produced by fungi of the Rhizopus genus in medium to high titters. The use of glucose, a main component of starchy and cellulosic food waste, as carbon source, together with a low-nitrogen source concentration, is a promising route to reduce process costs. In this work, the effects of nitrogen and carbonate sources on Rhizopus arrhizus NRRL 1526 morphology and fumaric acid productivity were analysed, simplifying the traditional production broth composition. Moreover, a non-structured, non-segregated kinetic model was proposed and fitted to concentration data of all relevant components obtained in batches performed in triplicate with the selected production broth at 34 °C and 200 rpm in an orbital shaker. Rhizopus arrhizus ; Fumaric acid ; Bioprocess ; Biomass (ecology) ; Chemistry ; Food science ; Pulp and paper industry ; Organic chemistry ; Chemical engineering ; Biology biodegradable polymer synthesis and properties ; Biofuel production and bioconversion ; Microbial Metabolic Engineering and Bioproduction
Authors: Santiago Suarez | Djafar Chabane | Abdoul N’Diaye | Youcef Amirat | Omar Elkedim | Abdesslem Djerdir
Source: Energies (article, 2022-05-10)
Field: Materials Science — Materials Chemistry

Evaluation of the Performance Degradation of a Metal Hydride Tank in a Real Fuel Cell Electric Vehicle In a fuel cell electric vehicle (FCEV) powered by a metal hydride tank, the performance of the tank is an indicator of the overall health status, which is used to predict its behaviour and make appropriate energy management decisions. The aim of this paper is to investigate how to evaluate the effects of charge/discharge cycles on the performance of a commercial automotive metal hydride hydrogen storage system applied to a real FCEV. For this purpose, a mathematical model is proposed based on uncertain physical parameters that are identified using the stochastic particle swarm optimisation (PSO) algorithm combined with experimental measurements. The variation of these parameters allows an assessment of the degradation level of the tank’s performance on both the quantitative and qualitative aspects. Simulated results derived from the proposed model and experimental measurements were in good agreement, with a maximum relative error of less than 2%. The validated model was used to establish the correlations between the observed degradations in a hydride tank recovered from a real FCEV. The results obtained show that it is possible to predict tank degradations by developing laws of variation of these parameters as a function of the real conditions of the use of the FCEV (number of charging/discharging cycles, pressures, mass flow rates, temperatures). Hydride ; Automotive engineering ; Electric vehicle ; Engineering ; Automotive industry ; Nuclear engineering ; Hydrogen ; Chemistry ; Thermodynamics Hydrogen Storage and Materials ; Hybrid Renewable Energy Systems ; Ammonia Synthesis and Nitrogen Reduction
Authors: Giuseppe Lovarelli | Gaetano Calogero | Gianluca Fiori | Giuseppe Iannaccone
Source: Physical Review Applied (article, 2022-09-19)
Field: Materials Science — Materials Chemistry

Multiscale Pseudoatomistic Quantum Transport Modeling for van der Waals Heterostructures Several electronic and optoelectronic devices have been proposed in recent years based on vertical heterostructures of two-dimensional (2D) materials. The large number of combinations of available 2D materials and the even larger number of possible heterostructures require effective and predictive device-simulation methods, to inform and accelerate experimental research and to support the interpretation of experiments. Here, we propose a computationally effective and physically sound method to model electron transport in 2D van der Waals heterostructures, based on a multiscale approach and quasiatomistic Hamiltonians. The method uses ab initio simulations to extract the parameters of a simplified tight-binding Hamiltonian based on a uniform three-dimensional lattice geometry that enables device simulations using the nonequilibrium Green's function approach in a computationally effective way. We describe the application and limitations of the method and discuss the examples of two use cases of practical electronic devices based on 2D materials, such as a field-effect transistor and a floating-gate memory, composed of molybdenum disulphide, hexagonal boron nitride and graphene. Heterojunction ; Hexagonal boron nitride ; van der Waals force ; Graphene ; Hamiltonian (control theory) ; Materials science ; Ab initio ; Non-equilibrium thermodynamics ; Quantum ; Molybdenum disulfide ; Lattice (music) ; Statistical physics ; Computer science ; Condensed matter physics ; Nanotechnology ; Physics ; Quantum mechanics ; Optoelectronics ; Mathematics ; Mathematical optimization ; Molecule Graphene research and applications ; 2D Materials and Applications ; Thermal properties of materials
Authors: Omar H. Badie | Ahmed Basyony | Reham Samir
Source: Microorganisms (article, 2022-10-05)
Field: Materials Science — Materials Chemistry

Computer-Based Identification of Potential Druggable Targets in Multidrug-Resistant Acinetobacter baumannii: A Combined In Silico, In Vitro and In Vivo Study The remarkable rise in antimicrobial resistance is alarming for <i>Acinetobacter baumannii</i>, which necessitates effective strategies for the discovery of promising anti-acinetobacter agents. We used a subtractive proteomics approach to identify unique protein drug targets. Shortlisted targets passed through subtractive channels, including essentiality, non-homology to the human proteome, druggability, sub-cellular localization prediction and conservation. Sixty-eight drug targets were shortlisted; among these, glutamine synthetase, dihydrodipicolinate reductase, UDP-N-acetylglucosamine acyltransferase, aspartate 1-decarboxylase and bifunctional UDP-N-acetylglucosamine diphosphorylase/glucosamine-1-phosphate N-acetyltransferase were evaluated in vitro by determining the minimum inhibitory concentration (MIC) of candidate ligands, citric acid, dipicolinic acid, D-tartaric acid, malonic acid and 2-(N-morpholino)ethanesulfonic acid (MES), respectively, which ranged from 325 to 1500 μg/mL except for MES (25 mg/mL). The candidate ligands, citric acid, D-tartaric acid and malonic acid, showed good binding energy scores to their targets upon applying molecular docking, in addition to a significant reduction in <i>A. baumannii</i> microbial load in the wound infection mouse model. These ligands also exhibited good tolerability to human skin fibroblast. The significant increase in the MIC of malonic acid in β-alanine and pantothenate-supplemented media confirmed its selective inhibition to aspartate 1-decarboxylase. In conclusion, three out of sixty-eight potential <i>A. baumannii</i> drug targets were effectively inhibited in vitro and in vivo by promising ligands. Biochemistry ; Acinetobacter baumannii ; Biology ; Dipicolinic acid ; In vivo ; Druggability ; Acetyltransferase ; Chemistry ; Microbiology ; Bacteria Enzyme Structure and Function ; Antibiotic Resistance in Bacteria ; Antimicrobial Peptides and Activities
Authors: Dean Miller | Angela Natale | Tatiana K. McAnulty | Rachel D. Swope | Emily A. McNaughton | Aviauna Beckett | Hannah E. Snoke | Annalee M. Schmidt | John N. Alumasa | Shawn Xiong
Source: Journal of Chemical Education (article, 2022-06-16)
Field: Chemistry — Physical and Theoretical Chemistry

The Design and Implementation of an Interdisciplinary CURE as an Alternative Option for the General Chemistry Laboratory Course Course-based Undergraduate Research Experiences (CUREs) incorporate research opportunities into the existing curriculum often by providing alternatives or replacing the traditional cookbook-based laboratory courses. Over the past 50 years, CURE courses have been shown to benefit both students and faculty members alike. Despite the large number of available publications on CUREs, few focus on their implementation at the first-year level. This article reports the design and implementation of a new interdisciplinary CURE based on antibiotic discovery that combines General Chemistry, Biochemistry, and Microbiology concepts allowing self-enrolled first-year students to satisfy the requirements for General Chemistry Laboratory I. The applicability and success of this CURE course is demonstrated through the significance of student-generated experimental results, improvement in competence tests, and affective surveys. Curriculum ; Competence (human resources) ; Engineering ethics ; Undergraduate research ; Course (navigation) ; Mathematics education ; Medical education ; Computer science ; Chemistry ; Psychology ; Engineering ; Medicine ; Pedagogy Various Chemistry Research Topics ; Innovative Teaching Methods ; Problem and Project Based Learning
Authors: Raynardthan Pontoh | Vania Edita Rarisavitri | Christine Charen Yang | Maximilliam Febriand Putra | Daru Seto Bagus Anugrah
Source: Indonesian Journal of Chemistry (article, 2022-01-20)
Field: Materials Science — Biomaterials

Density Functional Theory Study of Intermolecular Interactions between Amylum and Cellulose Amylum is one of the polysaccharides developed into biodegradable plastic bags. However, amylum-based plastics are easily damaged due to their low mechanical strength and hydrophilic properties. Cellulose is used as a support material in amylum-based plastics to increase strength and reduce water damage. This study investigated the molecular interactions between amylum and cellulose computationally. The minimum interaction energy of amylum and cellulose was calculated using in silico modeling using the Density Functional Theory (DFT) method. The B3LYP function and the basis set 6-31++g** were used in the calculations. Simultaneously, D3 Grimme dispersion correction was used as the effect of water solvent in the measures. The results obtained from this study were the interaction energy of amylum and cellulose of –29.8 kcal/mol. The HOMO-LUMO energy gap of the cellulose-amylum complex was lower than cellulose, indicating that the cellulose-amylum complex was more reactive and bonded to each other. Analysis of Natural Bond Orbital (NBO), Quantum Theory Atom in Molecule (QTAIM), Reduced Density Gradient (RDG), Non-covalent Interaction Index (NCI), and Intrinsic Bond Strength Index (IBSI) showed that the cellulose-amylum complex had weak to medium intermolecular bonds. The hydrogen bond at O61···H48 was the strongest in the complex. All data show that cellulose and amylum could interact through non-covalent bonds. Chemistry ; Cellulose ; Intermolecular force ; Starch ; Natural bond orbital ; Density functional theory ; Atoms in molecules ; Hydrogen bond ; Interaction energy ; Covalent bond ; Computational chemistry ; Molecule ; Polymer chemistry ; Organic chemistry Advanced Cellulose Research Studies ; Hydrogels: synthesis, properties, applications
Authors: Ameya D. Jagtap | George Em Karniadakis
Source: arXiv (Cornell University) (preprint, 2022-09-06)
Field: Materials Science — Materials Chemistry

How important are activation functions in regression and classification? A survey, performance comparison, and future directions Inspired by biological neurons, the activation functions play an essential part in the learning process of any artificial neural network commonly used in many real-world problems. Various activation functions have been proposed in the literature for classification as well as regression tasks. In this work, we survey the activation functions that have been employed in the past as well as the current state-of-the-art. In particular, we present various developments in activation functions over the years and the advantages as well as disadvantages or limitations of these activation functions. We also discuss classical (fixed) activation functions, including rectifier units, and adaptive activation functions. In addition to discussing the taxonomy of activation functions based on characterization, a taxonomy of activation functions based on applications is presented. To this end, the systematic comparison of various fixed and adaptive activation functions is performed for classification data sets such as the MNIST, CIFAR-10, and CIFAR- 100. In recent years, a physics-informed machine learning framework has emerged for solving problems related to scientific computations. For this purpose, we also discuss various requirements for activation functions that have been used in the physics-informed machine learning framework. Furthermore, various comparisons are made among different fixed and adaptive activation functions using various machine learning libraries such as TensorFlow, Pytorch, and JAX. MNIST database ; Activation function ; Artificial intelligence ; Artificial neural network ; Computer science ; Machine learning ; Taxonomy (biology) ; Biology Machine Learning in Materials Science ; Machine Learning and Data Classification ; Machine Learning in Bioinformatics
Authors: Xinxin Li | Wei Wang | Xuedan Ma
Source: The Journal of Physical Chemistry C (article, 2022-11-16)
Field: Materials Science — Materials Chemistry

Quantum Photon Sources in WSe<sub>2</sub> Monolayers Induced by Weakly Localized Strain Fields Quantum emitters in semiconductor transition metal dichalcogenide (TMD) monolayers hold great promise for many quantum optics applications due to the intriguing properties afforded by the host materials. The creation of localized excitonic states in two-dimensional semiconductors is also fundamentally interesting. Local strain engineering of TMD monolayers has been attested to be a viable approach for creating quantum emitters. However, despite the ubiquitous existence of local topography variations in the structures used to create strain gradients in the TMD monolayers, an understanding of their influence on the strain fields and exciton trapping is notably lacking, especially on the nanoscale. In this study, we investigate WSe2 monolayers deposited on the edges of as-fabricated trenches, which are deemed to induce 1D delocalized strain profiles in the monolayers, and observe optical signatures of weakly confined excitonic states supporting biexciton emission. Our numerical simulations of the strain distributions suggest that the quantum emitters originate from quasi-1D like localized strain profiles induced by local topography variations at the trench edges. These findings have strong implications toward the controlled creation of quantum emitters in TMD monolayers and their efficient coupling to photonic structures. Monolayer ; Delocalized electron ; Exciton ; Materials science ; Semiconductor ; Condensed matter physics ; Strain (injury) ; Photonics ; Quantum ; Trench ; Photon ; Optoelectronics ; Quantum well ; Coupling (piping) ; Nanotechnology ; Physics ; Laser ; Optics ; Quantum mechanics ; Layer (electronics) ; Composite material 2D Materials and Applications ; Graphene research and applications ; MXene and MAX Phase Materials