jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Rebecca L. Pinals | Nicholas Ouassil | Jackson Travis Del Bonis-O’Donnell | Jeffrey W. Wang | Markita P. Landry
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2021-06-20)
Field: Chemistry — Spectroscopy

Supervised Learning Model Predicts Protein Adsorption to Carbon Nanotubes Engineered nanoparticles are advantageous for numerous biotechnology applications, including biomolecular sensing and delivery. However, testing the compatibility and function of nanotechnologies in biological systems requires a heuristic approach, where unpredictable biofouling via protein corona formation often prevents effective implementation. Moreover, rational design of biomolecule-nanoparticle conjugates requires prior knowledge of such interactions or extensive experimental testing. Toward better applying engineered nanoparticles in biological systems, herein, we develop a random forest classifier (RFC) trained with proteomic mass spectrometry data that identifies proteins that adsorb to nanoparticles, based solely on the protein's amino acid sequence. We model proteins that populate the corona of a single-walled carbon nanotube (SWCNT)-based optical nanosensor and study whether there is a relationship between the protein's amino acid-based properties and the protein's adsorption to SWCNTs. We optimize the classifier and characterize the classifier performance against other models. To evaluate the predictive power of our model, we apply the classifier to rapidly identify proteins with high binding affinity to SWCNTs, followed by experimental validation. We further determine protein features associated with increased likelihood of SWCNT binding: high content of solvent-exposed glycine residues and non-secondary structure-associated amino acids. Conversely, proteins with high content of leucine residues and beta-sheet-associated amino acids are less likely to form the SWCNT protein corona. The classifier presented herein provides a step toward undertaking the otherwise intractable problem of predicting protein-nanoparticle interactions, which is needed for more rapid and effective translation of nanobiotechnologies from in vitro synthesis to in vivo use. Amino acid ; Biomolecule ; Proteomics ; Nanoparticle ; Carbon nanotube ; Nanotechnology ; Classifier (UML) ; Materials science ; Computational biology ; Chemistry ; Computer science ; Artificial intelligence ; Biochemistry ; Biology Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Biosensors and Analytical Detection
Authors: David Farina | Sai Krishna Sirumalla | Emily Mazeau | Richard H. West
Source:  (preprint, 2021-07-22)
Field: Chemistry — Organic Chemistry

Extensive High-Accuracy Thermochemistry and Group Additivity Values for Halocarbon Combustion Modeling Standard enthalpies, entropies, and heat capacities are calculated for 16,813 halocarbons using an automated high-fidelity thermochemistry workflow. This workflow generates conformers at density functional tight binding (DFTB) level, optimizes geometries, calculates harmonic frequencies, and performs 1D hindered rotor scans at DFT level, and computes electronic energies at G4 level. The computed enthalpies of formation for 400 molecules show good agreement with literature references, but the majority of the calculated species have no reference in the literature. Thus, this work presents the most accurate thermochemistry for many halocarbons to date. This new data set is used to train an extensive ensemble of group additivity values and hydrogen bond increment groups within the Reaction Mechanism Generator (RMG) framework. On average, the new group values estimate standard enthalpies for halogenated hydrocarbons within 3 kcal/mol of their G4 values. A significant contribution towards automated mechanism generation of halocarbon combustion, this research provides thermochemical data for thousands of novel halogenated species and presents a self-consistent set of halogen group additivity values. Thermochemistry ; Additive function ; Standard enthalpy of formation ; Chemistry ; Combustion ; Halocarbon ; Computational chemistry ; Workflow ; Thermodynamics ; Physical chemistry ; Computer science ; Mathematics ; Physics ; Database ; Organic chemistry Chemical Thermodynamics and Molecular Structure ; Advanced Chemical Physics Studies ; Thermal and Kinetic Analysis
Authors: El Hassane Lahrar | Patrice Simon | Céline Merlet
Source: The Journal of Chemical Physics (preprint, 2021-10-20)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Carbon–carbon supercapacitors: Beyond the average pore size or how electrolyte confinement and inaccessible pores affect the capacitance Carbon-carbon supercapacitors are high power electrochemical energy storage systems, which store energy through reversible ion adsorption at the electrode-electrolyte interface. Due to the complex structure of the porous carbons used as electrodes, extracting structure-property relationships in these systems remains a challenge. In this work, we conduct molecular simulations of two model supercapacitors based on nanoporous electrodes with the same average pore size, a property often used when comparing porous materials, but different morphologies. We show that the carbon with the more ordered structure, and a well defined pore size, has a much higher capacitance than the carbon with the more disordered structure and a broader pore size distribution. We analyze the structure of the confined electrolyte and show that the ions adsorbed in the ordered carbon are present in larger quantities and are also more confined than for the disordered carbon. Both aspects favor a better charge separation and thus a larger capacitance. In addition, the disordered electrodes contain a significant amount of carbon atoms, which are never in contact with the electrolyte, carry a close to zero charge, and are thus not involved in the charge storage. The total quantities of adsorbed ions and degrees of confinement do not change much with the applied potential, and as such, this work opens the door to computationally tractable screening strategies. Supercapacitor ; Electrolyte ; Carbon fibers ; Materials science ; Capacitance ; Nanoporous ; Chemical physics ; Electrode ; Nanotechnology ; Chemical engineering ; Ion ; Porosity ; Adsorption ; Electrochemistry ; Energy storage ; Composite material ; Chemistry ; Thermodynamics ; Organic chemistry ; Physical chemistry ; Power (physics) ; Physics Supercapacitor Materials and Fabrication ; Advancements in Battery Materials ; Conducting polymers and applications
Authors: Yue Hu | Yongxing Shen | Hua Bao
Source: arXiv (Cornell University) (preprint, 2021-09-28)
Field: Materials Science — Materials Chemistry

Optimized phonon band discretization scheme for efficiently solving the non-gray Boltzmann transport equation Phonon Boltzmann transport equation (BTE) is an important tool for studying the nanoscale thermal transport. Because phonons have a large spread in their properties, the non-gray (i.e. considering different phonon bands) phonon BTE is needed to accurately capture the nanoscale transport phenomena. However, BTE solvers generally require large computational cost. Non-gray modeling imposes significant additional complexity to the numerical simulations, which hinders the modeling of real nanoscale systems. In this work, we address this issue by a systematic investigation on the phonon band discretization scheme using real material properties of four representative materials, including silicon, gallium arsenide, diamond, and lead telluride. We find that the schemes used in previous studies require at least a few tens of bands to ensure the accuracy, which requires large computational costs. We then propose an improved band discretization scheme, in which we divide the mean free path domain into two subdomains, one on either side of the inflection point of the mean free path accumulated thermal conductivity and adopt the Gauss-Legendre quadrature for each subdomain. With this scheme, the solution of phonon BTE converges (error &lt; 1%) with less than 10 phonon bands for all these materials. The proposed scheme allows to significantly reduce the time and memory consumption of the numerical BTE solver, which is an important step towards large-scale phonon BTE simulation for real materials. Boltzmann equation ; Phonon ; Discretization ; Mean free path ; Solver ; Thermal conductivity ; Convection–diffusion equation ; Computer science ; Physics ; Statistical physics ; Mathematical optimization ; Computational physics ; Mathematics ; Scattering ; Condensed matter physics ; Mathematical analysis ; Optics ; Mechanics ; Quantum mechanics Thermal properties of materials ; Advanced Thermoelectric Materials and Devices ; Advancements in Semiconductor Devices and Circuit Design
Authors: Xinyi Du
Source: Scholarworks (University of Massachusetts Amherst) (article, 2021-04-21)
Field: Chemistry — Analytical Chemistry

DEVELOPMENT OF HEADSPACE ANALYSIS OF LIVING AND POSTHARVEST FRESH PRODUCE USING SURFACE-ENHANCED RAMAN SPECTROSCOPY (SERS) The increasing market demand for fresh produce promotes a keen interest in developing a rapid, sensitive and reliable method for monitoring plant health and determining the shelf-life of postharvest produce. The objective of this study is to explore the capability of Surface-enhanced Raman spectroscopy (SERS) in these applications. SERS integrates Raman spectroscopy which measures molecular vibrations and nanotechnology which enhances the weak Raman signals. Herein, we developed two SERS methods based on a surface detection approach using nanoparticles solution and a headspace detection approach using gold nanoparticles (AuNPs) fibers, to detect biochemical changes during postharvest storage of arugula leaves. Compared with surface detection, the headspace detection revealed significant spectral changes during the storage, particularly in the shifts around 500, 950 and 1030 cm-1. These changes analyzed using principal component analysis (PCA) to establish a prediction model for shelf-life determination. Through analyzing reference standard compounds, we identified the dimethyl disulfide (DMDS), 1-propanethiol and methanethiol (MT) were most likely to account for the signature spectra of headspace arugula at the late storage period due to the activities of spoilage bacteria. The headspace detection method was also applied to monitor the stress responses of living basil to abiotic stresses (pesticide/salinity). However, the volatile analysis of the basil plants response to abiotic stresses (pesticide/salinity) showed indistinctive results. In conclusion, the headspace detection based on SERS provides a new strategy for quality monitoring of fresh produce in the food industry. Postharvest ; Surface-enhanced Raman spectroscopy ; Raman spectroscopy ; Chemistry ; Spectroscopy ; Analytical Chemistry (journal) ; Materials science ; Chromatography ; Horticulture ; Optics ; Biology ; Raman scattering Spectroscopy and Chemometric Analyses ; Date Palm Research Studies ; Edible Oils Quality and Analysis
Authors: Matteo Sanviti | Ángel Alegría | Daniel E. Martínez‐Tong
Source: ChemRxiv (preprint, 2021-11-23)
Field: Materials Science — Polymers and Plastics

Fabrication and Nanoscale Properties of PEDOT:PSS Conducting Polymer Nanospheres Electrically conducting nanospheres of poly-(3,4-ethylenedioxythiophene):poly(styrene sulfonate) (PEDOT:PSS) with tailored size were prepared by a solvent displacement technique. To fabricate the nanostructures, dried PEDOT:PSS was dissolved in ethylene glycol (EG) and the solution was precipitated in deionized water. The proposed fabrication route allowed to obtain a water-based dispersion of PEDOT:PSS nanospheres with good optical properties. To determine the physical properties of the nanospheres, we followed a nanoscale approach, using Atomic Force Microscopy. Our nanoscale mechanical and electrical investigations showed that the nanospheres preserved good physical properties, compared to the commercial product. Moreover, the local studies indicated that the confinement imposed by the spherical shape and the treatment with EG lead to a different arrangement of the PSS and PEDOT phases, responsible for the good electrical conductivity of the nanostructures. PEDOT:PSS ; Materials science ; Ethylene glycol ; Fabrication ; Nanoscopic scale ; Nanostructure ; Nanotechnology ; Conductive polymer ; Polymer ; Chemical engineering ; Styrene ; Nanoparticle ; Dispersion (optics) ; Polymer chemistry ; Copolymer ; Composite material ; Optics Conducting polymers and applications ; Fuel Cells and Related Materials ; Advanced Sensor and Energy Harvesting Materials
Authors: Rizka Wildani | Usman Ahmad | Mohamad Rafi | Slamet Ari Dwi Ratnanto
Source: Advances in Food Science Sustainable Agriculture and Agroindustrial Engineering (article, 2021-12-01)
Field: Chemistry — Inorganic Chemistry

Effect of hydrogen peroxide treatment on the concentration of volatile compound in coriander seeds oil Coriander seeds essential oil have shown some remarkable biological properties and health benefits. The coriander seeds used in Indonesia are imported and also treated with hydrogen peroxide before reaching consumers. Hydrogen peroxide is known to be a strong oxidizer, but so far, there has been no information that explains its effects on the essential oil composition and concentration in coriander seed. This study aims to determine the effect of using hydrogen peroxide and the drying method on the composition of essential oils in coriander seeds. Yield and volatile oil compounds were measured, and the results were compared between the hydrogen peroxide concentration (0,35 and 50% ) and drying method (mechanical drying (50 ℃) and room temperature drying). GC-MS analysis detected linalool as the most common volatile constituent in all treatments. The highest concentration of linalool compounds (70.16%) was found in seeds without hydrogen peroxide (0%) treatment combined with mechanical drying, followed by without hydrogen peroxide (0% H2O2) combined with room drying (23.74%), then by 35% hydrogen peroxide combined with room drying (18.71%), 35% hydrogen peroxidecombined with mechanical drying (18.84%), 50% hydrogen peroxide combined with room drying (22.18%) and by 50% hydrogen peroxide combined with mechanical drying (15.45%). Therefore, the yield was clearly affected only by hydrogen peroxide treatment where no hydrogen peroxide treatment gave the highest yield. The drying method did not have any significant effect on yield. Hydrogen peroxide ; Linalool ; Chemistry ; Yield (engineering) ; Peroxide ; Essential oil ; Nuclear chemistry ; Food science ; Organic chemistry ; Materials science ; Metallurgy Coconut Research and Applications ; Natural Products and Applications ; Medicinal Plant Research
Authors: Jin Wang | Youjun Jiang | Li Li | Chao Yang | Ke Li | Xueping Lan | Yuchong Zhang | Jinying Chen
Source: Journal of Biomedical Research & Environmental Sciences (article, 2021-08-01)
Field: Chemistry — Analytical Chemistry

How to Guarantee Food Safety via Grain Storage? An Approach to Improve Management Effectiveness by Machine Learning Algorithms The purpose of grain storage management is to dynamically analyze the quality change of the reserved grains, adopt scientific and effective management methods to delay the speed of the quality deterioration, and reduce the loss rate during storage. At present, the supervision of the grain quality in the reserve mainly depends on the periodic measurements of the quality of the grains and the milled products. The data obtained by the above approach is accurate and reliable, but the workload is too large while the frequency is high. The obtained conclusions are also limited to the studied area and not applicable to be extended into other scenarios. Therefore, there is an urgent need of a general method that can quickly predict the quality of grains given different species, regions and storage periods based on historical data. In this study, we introduced Back-Propagation (BP) neural network algorithm and support vector machine algorithm into the quality prediction of the reserved grains. We used quality index, temperature and humidity data to build both an intertemporal prediction model and a synchronous prediction model. The results show that the BP neural network based on the storage characters from the first three periods can accurately predict the key storage characters intertemporally. The support vector machine can provide precise predictions of the key storage characters synchronously. The average predictive error for each of wheat, rice and corn is less than 15%, while the one for soybean is about 20%, all of which can meet the practical demands. In conclusion, the machine learning algorithms are helpful to improve the management effectiveness of grain storage. Computer science ; Quality (philosophy) ; Key (lock) ; Workload ; Artificial neural network ; Support vector machine ; Algorithm ; Data mining ; Computer data storage ; Machine learning ; Artificial intelligence Spectroscopy and Chemometric Analyses ; GABA and Rice Research ; Smart Agriculture and AI
Authors: Cui Yang | Cheng Liu | Yanming Cheng | Mahmoud Al Shurafa | Il-Kyoo Lee | Niu Jing
Source: Mathematical Problems in Engineering (article, 2021-12-23)
Field: Materials Science — Electronic, Optical and Magnetic Materials

An Effective Harmonic Suppression Method Based on Linear Active Disturbance Rejection Control for Copper Electrowinning Rectifier Because the harmonics in the production process of copper electrowinning have an important impact on the electrical energy consumption, it is necessary to suppress the harmonics effectively. In this paper, a copper electrowinning rectifier with double inverse star circuit is selected as a study object in which a large number of harmonics mainly including the 5th, 7th, 11th, and 13th harmonics are generated and injected back into the power grid. The total harmonic distortion rate of the power grid is up to 29.19% before filtering. Therefore, a method combining the induction filtering method and the active filtering method is proposed to carry out comprehensive filtering. Simulation results demonstrate that the total harmonic distortion rate of the system decreases to 4.20%, which indicates that the proposed method can track the corresponding changes of harmonics when the load changes in real time and filter them out. In order to ensure and improve the effect of active filter, a current harmonic tracking control method based on linear active disturbance rejection control is proposed. Simulation results show that the total harmonic distortion rate decreases to 3.34%, which is also lower than that of hysteresis control. Compared with the conventional single filtering method, the new filtering method combining induction filtering with active filtering based on linear active disturbance rejection control in the copper electrowinning rectifier has obvious advantages. Harmonics ; Control theory (sociology) ; Total harmonic distortion ; Rectifier (neural networks) ; Harmonic ; Distortion (music) ; Active filter ; Computer science ; Filter (signal processing) ; Electronic engineering ; Engineering ; Voltage ; Acoustics ; Physics ; Control (management) ; Electrical engineering Magnetic Properties and Applications ; Electrodeposition and Electroless Coatings ; Advanced DC-DC Converters
Authors: Chao Zhang | Qi Xu | Zhi Ping Xu | Long Wang | Zewen Liu | Zhong Li | Xusheng Shao
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2021-08-27)
Field: Materials Science — Materials Chemistry

Optical Control of Invertebrate nAChR and Behaviors with Dithienylethene-Imidacloprid ABSTRACT Photopharmacology has changed established methods of studying receptor functions, allowing for increasing spatiotemporal resolution. However, no photopharmacological tools are available for the invertebrate nicotinic acetylcholine receptor (nAChR). Here, we report a photochromic ligand, dithienylethene-imidacloprid (DitIMI), targeting invertebrate nAChR. We demonstrated that DitIMI has low spontaneous in vivo and in vitro activity but can be photoisomerized to a highly active closed-form. This photoisomerization can further be translated to photomodulation of neuron membrane potential and behavioral responses of living mosquito larvae and American cockroaches. Furthermore, we discovered that DitIMI is a specific reporter for fluorescence polarization based high-throughput screening of nAChR ligands. Imidacloprid ; Nicotinic acetylcholine receptor ; Photoisomerization ; Chemistry ; In vivo ; Invertebrate ; Biophysics ; Cockroach ; Acetylcholine receptor ; Receptor ; Biology ; Biochemistry ; Pesticide ; Ecology Photochromic and Fluorescence Chemistry ; Neurobiology and Insect Physiology Research ; Photoreceptor and optogenetics research
Authors: Jian Song | Changbin Yu
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2021-04-21)
Field: Chemistry — Spectroscopy

Alpha-XIC: a deep neural network for scoring the coelution of peak groups improves peptide identification by data-independent acquisition mass spectrometry ABSTRACT Motivation The peptide-centric identification methodologies of data-independent acquisition (DIA) data mainly rely on scores for the mass spectrometric signals of targeted peptides. Among these scores, the coelution scores of peak groups constructed by the chromatograms of peptide fragment ions have a significant influence on the identification. Most of the existing coelution scores are achieved by artificially designing some functions in terms of the shape similarity, retention time shift of peak groups. However, these scores cannot characterize the coelution robustly when the peak group is in the circumstance of interference. Results On the basis that the neural network is more powerful to learn the implicit features of data robustly from a large number of samples, and thus minimizing the influence of data noise, in this work, we propose Alpha-XIC, a neural network-based model to score the coelution. By learning the characteristics of the coelution of peak groups derived from identified peptides, Alpha-XIC is capable of reporting robust coelution scores even for peak groups with interference. With this score appending to initial scores generated by the accompanying identification engine, the ensuing statistical validation tool can update the identification result and recover the misidentified peptides. In our evaluation of the HeLa dataset with gradient lengths ranging from 0.5h to 2h, Alpha-XIC delivered 16.7% ~ 49.1% improvements in the number of identified precursors at 1% FDR. Furthermore, Alpha-XIC was tested on LFQbench, a mixed-species dataset with known ratios, and increased the number of peptides and proteins fell within valid ratios by up to 16.6% and 13.8%, respectively, compared to the initial identification. Availability and Implementation Source code are available at www.github.com/YuAirLab/Alpha-XIC . Peptide ; Chemistry ; Identification (biology) ; Computational biology ; Biochemistry ; Biology Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Machine Learning in Bioinformatics