jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Pooja Chahal | Apoorv Kushwaha | T. J. Dhilip Kumar
Source: Monthly Notices of the Royal Astronomical Society (article, 2024-09-18)
Field: Chemistry — Spectroscopy

Inelastic scattering of PO+ by H2 at interstellar temperatures ABSTRACT Phosphorous species are of great interest in interstellar chemistry since they are the basic blocks for building life here on Earth. Modelling the abundance and environment of recently detected PO$^{+}$ under non-local thermodynamic equilibrium (LTE) requires rotational spectra of the molecule along with accurate collisional rates with the most abundant species, hydrogen and helium. A new 4D ab initio potential energy surface (PES) of PO$^{+}$ – H$_{2}$ collision is calculated using CCSD(T)/CBS(DTQ) methodology considering rigid rotor approximation. The region containing the minima of the PES is augmented using neural networks (NNs) model while very high potentials ($\gt 2500$ cm$^{-1}$) and asymptotic region have been approximated using Slater and R$^{-4}$ functions, respectively. The close coupling calculations have been performed using molscat software for both ortho and para-H$_{2}$. The rate coefficients have been reported for transitions $j-j^{\prime }=$$1-0$, $2-1$, $3-2$, and $5-4$ through which PO$^{+}$ has been experimentally detected in interstellar medium (ISM). The rate coefficients for even and odd transitions of PO$^{+}$ with para-H$_{2}$ are compared with that of helium and are found to be 1.1–2.0 times higher. For even transitions ($\Delta j = 2$), the ortho-H$_{2}$ rates are 10 per cent higher than para-H$_{2}$ rates. However, the trend reverses in the case of odd transitions ($\Delta j = 1$) when higher J transitions are considered at low temperatures. At higher temperatures, the ortho rates cross the para-H$_{2}$ rates and become larger than the latter. The new rate coefficients with both ortho and para-H$_{2}$ will enable accurate modelling of the PO$^{+}$ abundance in the ISM under non-LTE conditions. Physics ; Interstellar cloud ; Astrophysics ; Scattering ; Interstellar medium ; Inelastic scattering ; Astronomy ; Atomic physics ; Galaxy ; Optics Spectroscopy and Laser Applications ; Atmospheric Ozone and Climate ; Advanced Chemical Physics Studies
Authors: Jiale Liu | Zheng Guo | Hantian You | Changsheng Zhang | Luhua Lai
Source: Angewandte Chemie International Edition (article, 2024-09-19)
Field: Materials Science — Materials Chemistry

All‐Atom Protein Sequence Design Based on Geometric Deep Learning Designing sequences for specific protein backbones is a key step in creating new functional proteins. Here, we introduce GeoSeqBuilder, a deep learning framework that integrates protein sequence generation with side chain conformation prediction to produce the complete all-atom structures for designed sequences. GeoSeqBuilder uses spatial geometric features from protein backbones and explicitly includes three-body interactions of neighboring residues. GeoSeqBuilder achieves native residue type recovery rate of 51.6 %, comparable to ProteinMPNN and other leading methods, while accurately predicting side chain conformations. We first used GeoSeqBuilder to design sequences for thioredoxin and a hallucinated three-helical bundle protein. All the 15 tested sequences expressed as soluble monomeric proteins with high thermal stability, and the 2 high-resolution crystal structures solved closely match the designed models. The generated protein sequences exhibit low similarity (minimum 23 %) to the original sequences, with significantly altered hydrophobic cores. We further redesigned the hydrophobic core of glutathione peroxidase 4, and 3 of the 5 designs showed improved enzyme activity. Although further testing is needed, the high experimental success rate in our testing demonstrates that GeoSeqBuilder is a powerful tool for designing novel sequences for predefined protein structures with atomic details. GeoSeqBuilder is available at https://github.com/PKUliujl/GeoSeqBuilder. Protein design ; Protein engineering ; Sequence (biology) ; Computational biology ; Protein structure ; Computer science ; Protein sequencing ; Protein structure prediction ; Biological system ; Chemistry ; Bioinformatics ; Peptide sequence ; Artificial intelligence ; Biochemistry ; Biology ; Enzyme ; Gene Enzyme Structure and Function ; Microbial Natural Products and Biosynthesis ; Computational Drug Discovery Methods
Authors: Jonathan Tran | Simone Vassiliadis | Aaron Elkins | Noel O. I. Cogan | Simone Rochfort
Source: Sensors (article, 2024-08-06)
Field: Chemistry — Analytical Chemistry

Rapid In Situ Near-Infrared Assessment of Tetrahydrocannabinolic Acid in Cannabis Inflorescences before Harvest Using Machine Learning Cannabis is cultivated for therapeutic and recreational purposes where delta-9 tetrahydrocannabinol (THC) is a main target for its therapeutic effects. As the global cannabis industry and research into cannabinoids expands, more efficient and cost-effective analysis methods for determining cannabinoid concentrations will be beneficial to increase efficiencies and maximize productivity. The utilization of machine learning tools to develop near-infrared (NIR) spectroscopy-based prediction models, which have been validated from accurate and sensitive chemical analysis, such as gas chromatography (GC) or liquid chromatography mass spectroscopy (LCMS), is essential. Previous research on cannabinoid prediction models targeted decarboxylated cannabinoids, such as THC, rather than the naturally occurring precursor, tetrahydrocannabinolic acid (THCA), and utilize finely ground cannabis inflorescence. The current study focuses on building prediction models for THCA concentrations in whole cannabis inflorescences prior to harvest, by employing non-destructive screening techniques so cultivators may rapidly characterize high-performing cultivars for chemotype in real time, thus facilitating targeted optimization of crossbreeding efforts. Using NIR spectroscopy and LCMS to create prediction models we can differentiate between high-THCA and even ratio classes with 100% prediction accuracy. We have also developed prediction models for THCA concentration with a <i>R</i><sup>2</sup> = 0.78 with a prediction error average of 13%. This study demonstrates the viability of a portable handheld NIR device to predict THCA concentrations on whole cannabis samples before harvest, allowing the evaluation of cannabinoid profiles to be made earlier, therefore increasing high-throughput and rapid capabilities. Cannabinoid ; Cannabis ; Cannabinoid receptor ; Chemistry ; Artificial intelligence ; Computer science ; Machine learning ; Computational biology ; Biochemical engineering ; Chromatography ; Biology ; Engineering ; Biochemistry ; Medicine Spectroscopy and Chemometric Analyses ; Cannabis and Cannabinoid Research ; Coffee research and impacts
Authors: Omid Banapour Ghaffari | Bijan Eftekhari Yekta | Morteza Zakeri‐Nasrabadi
Source: Ceramics International (article, 2024-08-08)
Field: Materials Science — Ceramics and Composites

Designing high-performance ion-exchangeable glasses with multi-objective optimization and machine learning Traverse ; Ion ; Process (computing) ; Materials science ; Genetic algorithm ; Set (abstract data type) ; Ion exchange ; Computer science ; Inverse ; Task (project management) ; Glass transition ; Biological system ; Machine learning ; Nanotechnology ; Composite material ; Polymer ; Mathematics ; Physics ; Engineering ; Systems engineering Glass properties and applications ; Luminescence Properties of Advanced Materials ; Pigment Synthesis and Properties
Authors: Keita Yaginuma | Kensaku Matsunami | Laure Descamps | Alexander Ryckaert | Thomas De Beer
Source: International Journal of Pharmaceutics (article, 2024-07-14)
Field: Chemistry — Analytical Chemistry

Hybrid modeling of T-shaped partial least squares regression and transfer learning for formulation and manufacturing process development of new drug products Partial least squares regression ; Tableting ; Scale (ratio) ; Computer science ; Process (computing) ; Pharmaceutical manufacturing ; Machine learning ; Chemistry ; Chromatography Spectroscopy and Chemometric Analyses ; Crystallization and Solubility Studies ; Mineral Processing and Grinding
Authors: Jie Xu | Miao Xie | Xinhao Wu | Kunshu Xiao | Yaoyu Ding | Libing Bai | Cheng‐Geng Huang | Wei Huang
Source: Materials Research Express (article, 2024-01-01)
Field: Materials Science — Polymers and Plastics

Remaining useful life prediction towards cycling stability of organic electrochemical transistors Abstract Organic electrochemical transistors (OECTs) show abundant potential in biosensors, artificial neuromorphic systems, brain-machine interfaces, etc With the fast development of novel functional materials and new device structures, OECTs with high transconductance (g m &gt; mS) and good cycling stabilities (&gt; 10,000 cycles) have been developed. While stability characterization is always time-consuming, to accelerate the development and commercialization of OECTs, tools for stability prediction are urgently needed. In this paper, OECTs with good cycling stabilities are realized by minimizing the gate voltage amplitude during cycling, while a remaining useful life (RUL) prediction framework for OECTs is proposed. Specifically, OECTs based on p(g2T-T) show tremendously enhanced stability which exhibits only 46.1% on-current (I ON ) and 33.2% peak g m decreases after 80,000 cycles (53 min). Then, RUL prediction is proposed based on the run-to-failure (RtF) aging tests (cycling stability test of OECTs). By selecting two aging parameters (I ON and peak g m ) as health indicators (HI), a novel multi-scale feature fusion (MFF) method for RUL prediction is proposed, which consists of a long short-term memory (LSTM) neural network based multi-scale feature generator (MFG) module for feature extraction and an attention-based feature fusion (AFF) module for feature fusion. Consequently, richer effective information is utilized to improve the prediction performance, where the experimental results show the superiority of the proposed framework on multiple OECTs in RUL prediction tasks. Therefore, by introducing such a powerful framework for the evaluation of the lifetime of OECTs, further optimization of materials, devices, and integrated systems relevant to OECTs will be stimulated. Moreover, this tool can also be extended to other relevant bioelectronics. Neuromorphic engineering ; Transconductance ; Stability (learning theory) ; Cycling ; Computer science ; Feature (linguistics) ; Artificial intelligence ; Artificial neural network ; Scale (ratio) ; Voltage ; Pattern recognition (psychology) ; Materials science ; Transistor ; Machine learning ; Engineering ; Electrical engineering ; Physics Conducting polymers and applications ; Advanced Memory and Neural Computing ; Organic Electronics and Photovoltaics
Authors: Yan-Yu Xie | Xiao-Dong Li | Huidong Zhang | Xiuying Liu | Junfei Wang
Source: International Journal of Hydrogen Energy (article, 2024-09-14)
Field: Chemistry — Inorganic Chemistry

Evaluation and screening of multivariate metal-organic frameworks for hydrogen storage Hydrogen storage ; Multivariate statistics ; Metal-organic framework ; Environmental science ; Hydrogen ; Multivariate analysis ; Environmental chemistry ; Chemistry ; Computer science ; Organic chemistry ; Machine learning ; Adsorption Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Hydrogen Storage and Materials
Authors: Verra Mellyana | I Wayan Budiastra | Irmansyah | Yohanes Aris Purwanto
Source: International Journal on Advanced Science Engineering and Information Technology (article, 2024-04-28)
Field: Chemistry — Analytical Chemistry

Electrical Properties for Non-destructive Determination of Free Fatty Acid and Moisture Content in Oil Palm Fruit FFA content is one of the essential qualities of oil palm fruit. FFA content exceeding 5% is considered unsuitable for human consumption. Commonly, FFA content is determined by chemical methods in the laboratory, but it is destructive, time-consuming, and costly. Several non-destructive methods have been investigated, but a maturity prediction approach has been primarily used with no direct relation to FFA content. Thus, there is a pressing need for a non-destructive framework to assess the FFA levels in the oil palm fruit directly. An attempt has been explored using a non-destructive palm fruit quality assessment that relied on electrical properties (impedance, admittance, resistance, and capacitance) in the frequency range from 50 Hz to 5 MHz was investigated to predict FFA and moisture content directly. Two statistical analyses were employed: stepwise multiple linear regressions (MLR) and artificial neural networks (ANN) to calibrate and validate electrical properties with FFA and moisture content. The best-performing models ANN showcased significant results: r= 0.96, R2=0.92, SEC at 0.86%, SEP at 0.97%, CV at 19.45%, consistency at 88.54, and RPD at 3.43 for FFA prediction, and r= 0.99, R2=0.98, SEC at 3.09 %, SEP at 3.46 %, CV at 5.44 %, consistency at 89.08 and RPD at 7.02 for predicting moisture content. In modeling oil palm quality determination, applying the ANN method significantly improved model performances, demonstrating its efficacy in predicting non-destructively both FFA levels based on admittance and moisture content based on impedance. Water content ; Palm oil ; Food science ; Palm ; Fatty acid ; Horticulture ; Oil mill ; Chemistry ; Biology ; Organic chemistry ; Engineering ; Physics ; Geotechnical engineering Spectroscopy and Chemometric Analyses ; Coconut Research and Applications ; Advanced Chemical Sensor Technologies
Authors: Kui Xu | Liyun Fan | Jinwei Sun | Haibo Huo | Zejun Jiang | Chongchong Shen | Yunpeng Wei
Source: Applied Thermal Engineering (article, 2024-11-28)
Field: Materials Science — Materials Chemistry

Multi-criteria sensitivity study and optimization of a two-stage preheated SOFC system considering thermal characteristics Sensitivity (control systems) ; Thermal ; Stage (stratigraphy) ; Materials science ; System optimization ; Process engineering ; Mechanical engineering ; Engineering ; Mathematical optimization ; Thermodynamics ; Mathematics ; Geology ; Electronic engineering Advancements in Solid Oxide Fuel Cells ; Fuel Cells and Related Materials ; Electrocatalysts for Energy Conversion
Authors: Shilin Wen | Jingang Huang | Weishuai Li | Mengke Wu | Felix Steyskal | Jianfang Meng | Xiaobin Xu | Pingzhi Hou | Junhong Tang
Source: Chemosphere (article, 2024-02-17)
Field: Chemistry — Analytical Chemistry

Henna plant biomass enhanced azo dye removal: Operating performance, microbial community and machine learning modeling Lawsone ; Lawsonia inermis ; Sulfanilic acid ; Fermentation ; Chemistry ; Redox ; Reducing agent ; Organic chemistry Dye analysis and toxicity ; Enzyme-mediated dye degradation ; Chromium effects and bioremediation
Authors: Júlia Públio Rabello | Paulo Eduardo da Silva Cavalcante | Jaci Leme | Vinícius Aragão Tejo Dias | Fernanda Angela Correia Barrence | Luis Giovani de Oliveira Guardalini | Thaissa Consoni Bernardino | Robson Nunes | Iago H.A.S. Barros | Aldo Tonso | Soraia Attie Calil Jorge | Eutímio Gustavo Fernández Núñez
Source: Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy (article, 2024-09-24)
Field: Chemistry — Analytical Chemistry

Chemometrics and analytical blank on the at-line monitoring of Zika-VLP production using near-infrared spectroscopy Chemistry ; Chemometrics ; Blank ; Infrared spectroscopy ; Analytical Chemistry (journal) ; Near-infrared spectroscopy ; Spectroscopy ; Optics ; Chromatography ; Organic chemistry Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis
Authors: Dmitriy D. Matyushin | Anastasia Yu. Sholokhova
Source: Journal of Chromatography A (article, 2024-08-02)
Field: Chemistry — Spectroscopy

Large-scale statistical study of the dependence of retention index on heating rate in temperature-programmed gas chromatography Kovats retention index ; Chemistry ; Gas chromatography ; Analytical Chemistry (journal) ; Chromatography ; Correlation coefficient ; Siloxane ; Stationary phase ; Thermodynamics ; Organic chemistry ; Statistics ; Polymer Analytical Chemistry and Chromatography ; Advanced Chemical Sensor Technologies ; Chemical Thermodynamics and Molecular Structure
Authors: H. Gong | Wencheng Fang | Jianhao Tan | Xiaoxia Huang | C. Wang | Yiming Xu | Zhigang Zhao
Source: Physical Review Accelerators and Beams (article, 2024-04-15)
Field: Materials Science — Surfaces, Coatings and Films

Design of a dual-mode transverse deflecting structure using neural network and multiobjective algorithms Shanghai Synchrotron Radiation Facility/Shanghai Soft X-ray FEL Facility is currently developing an advanced variable polarization transverse deflecting structure TTDS (two-mode transverse deflecting structure) using a dual-mode rf structure concept. Driven by two different rf power sources, this novel TDS works using both the <a:math xmlns:a="http://www.w3.org/1998/Math/MathML" display="inline"><a:msub><a:mi>HEM</a:mi><a:mn>11</a:mn></a:msub></a:math> and <c:math xmlns:c="http://www.w3.org/1998/Math/MathML" display="inline"><c:msub><c:mi>HEM</c:mi><c:mn>12</c:mn></c:msub></c:math> modes for simultaneous vertical and horizontal deflections, consequently, it can provide a time-varying polarization of the electrical field at ultrafast speeds. It is capable of producing circular and elliptical polarizations as well as flexible vector combinations through amplitude and phase modulation from a low-level rf system. The work presented in this paper is focused on the analysis and design of the variable polarization TDS, consisting of dual-mode cells and two dual-mode couplers. Designing and optimizing for dual-mode design and optimization is complex; consequently, an advanced optimization procedure based on neural networks and multiobjective algorithms has been developed. This improves the accuracy and efficiency of the rf structure design process. Through iterations, the dual-mode cells in the final design are optimized for high impedance and other rf performance criteria for both the <e:math xmlns:e="http://www.w3.org/1998/Math/MathML" display="inline"><e:msub><e:mi>HEM</e:mi><e:mn>11</e:mn></e:msub></e:math> and <g:math xmlns:g="http://www.w3.org/1998/Math/MathML" display="inline"><g:msub><g:mi>HEM</g:mi><g:mn>12</g:mn></g:msub></g:math> modes. The two couplers for rf power input and output are also optimized. Based on the optimized design and rf sensitivity analysis, the mechanical design has been completed and is now ready for manufacture. Published by the American Physical Society 2024 Artificial neural network ; Dual mode ; Mode (computer interface) ; Transverse plane ; Dual (grammatical number) ; Algorithm ; Computer science ; Artificial intelligence ; Engineering ; Electronic engineering ; Structural engineering Optical Coatings and Gratings ; Laser and Thermal Forming Techniques ; Advanced Surface Polishing Techniques
Authors: Yiding Qiu | Ziang Jing | Haoliang Liu | Huaxuan He | Kai Wu | Yonghong Cheng | Bing Xiao
Source: Nanoscale (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Fast access of the lattice thermal conductivity and phonon quasiparticle spectra of Mo<sub>2</sub>TiC<sub>2</sub>T<sub>2</sub> (T = –O and –F) and Janus Mo<sub>2</sub>TiC<sub>2</sub>OF MXenes from machine learning potentials The presence of strong anharmonic effects in surface functionalized MXenes greatly challenges the use of harmonic lattice dynamics calculations to predict their phonon spectra and lattice thermal conductivity at finite temperatures. Herein, we demonstrate the workflow for training and validating machine learning potentials in terms of moment tensor potential (MTP) for MXenes including Mo<sub>2</sub>TiC<sub>2</sub>, Mo<sub>2</sub>TiC<sub>2</sub>O<sub>2</sub>, Mo<sub>2</sub>TiC<sub>2</sub>F<sub>2</sub> and Janus-Mo<sub>2</sub>TiC<sub>2</sub>OF monolayers. Then, the MTPs of MXenes are successfully combined with the harmonic lattice dynamics calculations to obtain the temperature renormalized phonon spectra, three-phonon scattering rates, phonon relaxation times and lattice thermal conductivity at finite temperatures. Furthermore, combining MTPs with classic molecular dynamics simulations at finite temperatures directly enables the calculation of phonon quasi-particle spectral energy density with a full inclusion of all anharmonic effects in MXenes. Our current results indicate that anharmonic effects are found to be relatively weak in Mo<sub>2</sub>TiC<sub>2</sub> and Mo<sub>2</sub>TiC<sub>2</sub>O<sub>2</sub> monolayers, whereas the phonon quasi-particle spectral energy densities largely resemble those of harmonic or renormalized lattice dynamics calculations. Significant broadening of spectral energy density at finite temperature is predicted for Mo<sub>2</sub>TiC<sub>2</sub>F<sub>2</sub> and Janus-Mo<sub>2</sub>TiC<sub>2</sub>OF monolayers, implying strong anharmonic effects in those MXenes. Our work paves a new way for fast and reliable calculation of the phonon scattering process and lattice thermal conductivity of MXenes within MTPs trained from first-principles molecular dynamics simulations in the future. Quasiparticle ; Janus ; Phonon ; Thermal conductivity ; Condensed matter physics ; Materials science ; Spectral line ; Lattice (music) ; Physics ; Nanotechnology ; Superconductivity ; Quantum mechanics MXene and MAX Phase Materials ; 2D Materials and Applications ; Advanced Memory and Neural Computing
Authors: Andrea Schincaglia | Luisa Pasti | Alberto Cavazzini | Giorgia Purcaro | Marco Beccaria
Source: Food Chemistry (article, 2024-07-31)
Field: Chemistry — Spectroscopy

Optimization of headspace high-capacity tool coupled to two-dimensional gas chromatography–mass spectrometry for mapping the volatile organic compounds of raw pistachios. A proof-of-concept on the classification ability by geographic origin Normalization (sociology) ; Mass spectrometry ; Gas chromatography–mass spectrometry ; Chemistry ; Gas chromatography ; Volatile organic compound ; Chromatography ; Kovats retention index ; Two-dimensional gas ; Random forest ; Preprocessor ; Mass spectrum ; Principal component analysis ; Analytical Chemistry (journal) ; Artificial intelligence ; Computer science ; Organic chemistry Analytical Chemistry and Chromatography ; Advanced Chemical Sensor Technologies ; Spectroscopy and Chemometric Analyses