jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Xiuying Zhu | Mengjie Li | Xiong‐Fei Zhang | Yijie Liu | Jianfeng Yao
Source: Advanced Materials Technologies (article, 2023-11-27)
Field: Materials Science — Electronic, Optical and Magnetic Materials

MXene Functionalized Wood Composite Films for Efficient Electromagnetic Interference Shielding and Pressure Sensing Abstract Designing ultra‐light, green, and flexible films for efficient electromagnetic interference (EMI) shielding is crucial yet challenging. Herein, laminated Ti 3 C 2 T x MXene nanosheets are prepared and impregnated into a delignified wood matrix to fabricate composite films. Through a facile drop‐casting and densification strategy, highly aligned cellulose fibers are used for regulating the stacking behavior of MXenes, and the MXenes are encapsulated and attached tightly to the cellulose chains through hydrogen bonds. A high MXene loading up to 32.5 wt.% can be realized, and the resultant MXene/wood film exhibits a tensile strength of 260.7 MPa. The EMI shielding effectiveness is calculated to be 55.5 dB (X‐band, 8.2–12.4 GHz), with a normalized surface‐specific shielding effectiveness of 2673.4 B cm 2 g −1 . Moreover, the superior electrical conductivity and structural robustness of the composite film give it pressure‐sensing properties and show high sensitivity for monitoring diverse human physiological movements. MXenes ; Materials science ; Electromagnetic shielding ; Composite number ; Composite material ; Stacking ; EMI ; Electromagnetic interference ; Ultimate tensile strength ; Polyimide ; Cellulose ; Nanotechnology ; Electronic engineering ; Chemical engineering ; Layer (electronics) Electromagnetic wave absorption materials ; MXene and MAX Phase Materials ; Advanced Sensor and Energy Harvesting Materials
Authors: Atreyee Banerjee | Hsiao‐Ping Hsu | Kurt Kremer | Oleksandra Kukharenko
Source: ACS Macro Letters (article, 2023-05-11)
Field: Materials Science — Materials Chemistry

Data-Driven Identification and Analysis of the Glass Transition in Polymer Melts Understanding the nature of glass transition, as well as the precise estimation of the glass transition temperature for polymeric materials, remains open questions in both experimental and theoretical polymer sciences. We propose a data-driven approach, which utilizes the high-resolution details accessible through the molecular dynamics simulation and considers the structural information on individual chains. It clearly identifies the glass transition temperature of polymer melts of weakly semiflexible chains. By combining principal component analysis and clustering, we identify the glass transition temperature in the asymptotic limit even from relatively short time trajectories, which just reach into the Rouse-like monomer displacement regime. We demonstrate that fluctuations captured by the principal component analysis reflect the change in a chain’s behavior: from conformational rearrangement above to small fluctuations below the glass transition temperature. Our approach is straightforward to apply and should be applicable to other polymeric glass-forming liquids. Glass transition ; Polymer ; Principal component analysis ; Materials science ; Chain (unit) ; Component (thermodynamics) ; Statistical physics ; Displacement (psychology) ; Molecular dynamics ; Monomer ; Chemical physics ; Cluster analysis ; Thermodynamics ; Chemistry ; Computer science ; Physics ; Composite material ; Computational chemistry Material Dynamics and Properties ; Phase Equilibria and Thermodynamics ; Rheology and Fluid Dynamics Studies
Authors: Mehri H. Pourasl | Ali Vahedi | H. Tajalli | Balal Khalilzadeh | Farzaneh Bayat
Source: Scientific Reports (article, 2023-04-26)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Liquid crystal-assisted optical biosensor for early-stage diagnosis of mammary glands using HER-2 Breast cancer (BC) is one of the most commonly diagnosed cancers and the second leading cause of cancer mortality among women around the world. The purpose of this study is to present a non-labeled liquid crystal (LC) biosensor, based on the inherent feature of nematic LCs, for the evaluation of BC using the human epidermal growth factor receptor-2 (HER-2) biomarker. The mechanism of this sensing is supported by surface modification with dimethyloctadecyl [3-(trimethoxysilyl) propyl] ammonium chloride (DMOAP) encouraging the long alkyl chains that induce a homeotropic orientation of the LC molecules at the interface. To enhance the binding efficacy of more HER-2 antibody (Ab) on LC aligning agents, a simple ultraviolet radiation-assisted method was also used to increase functional groups on the DMOAP coated slides, thereby improving binding affinity and efficiency onto HER-2 Abs. The designed biosensor makes use of the specific binding of HER-2 protein to HER-2 Ab and disruption of the orientation of LCs. This orientation change leads to a transition of the optical appearance from dark to birefringent, enabling the detection of HER-2. This novel biosensor exhibits a linear optical response to HER-2 concentration in the wide dynamic range of 10<sup>-6</sup>-10<sup>2</sup> ng/mL, with an ultra-low detection limit of 1 fg/mL. As a proof of concept, the designed LC biosensor was successfully investigated for the quantification of HER-2 protein in patients suffering from BC. Owing to the sensitivity, selectivity, and label-free detection, this biosensor may amplify the application of LC-based biosensors for the detection of most types of cancers. Homeotropic alignment ; Biosensor ; Detection limit ; Liquid crystal ; Materials science ; Chemistry ; Chromatography ; Nanotechnology ; Optoelectronics Liquid Crystal Research Advancements ; Advanced Biosensing Techniques and Applications ; Monoclonal and Polyclonal Antibodies Research
Authors: Xu Xiang | Luis Soriano‐Agueda | Xabier López | Eloy Ramos‐Cordoba | Eduard Matito
Source: Journal of Chemical Theory and Computation (article, 2023-12-29)
Field: Materials Science — Materials Chemistry

All-Purpose Measure of Electron Correlation for Multireference Diagnostics We present an analytical relationship between two natural orbital occupancy-based indices, <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mover><mml:mrow><mml:msub><mml:mi>I</mml:mi><mml:mrow><mml:mi>N</mml:mi><mml:mi>D</mml:mi></mml:mrow></mml:msub><mml:mspace></mml:mspace></mml:mrow><mml:mrow><mml:mo>¯</mml:mo></mml:mrow></mml:mover></mml:math> and <i>I</i><sub>ND</sub><sup>max</sup>, and two established electron correlation metrics: the leading term of a configuration interaction expansion, <i>c</i><sub>0</sub>, and the <i>D</i><sub>2</sub> diagnostic. Numerical validation revealed that <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mover><mml:mrow><mml:msub><mml:mi>I</mml:mi><mml:mrow><mml:mi>N</mml:mi><mml:mi>D</mml:mi></mml:mrow></mml:msub><mml:mspace></mml:mspace></mml:mrow><mml:mrow><mml:mo>¯</mml:mo></mml:mrow></mml:mover></mml:math> and <i>I</i><sub>ND</sub><sup>max</sup> can effectively substitute for <i>c</i><sub>0</sub> and <i>D</i><sub>2</sub>, respectively. These indices offer three distinct advantages: (i) they are universally applicable across all electronic structure methods, (ii) their interpretation is more intuitive, and (iii) they can be readily incorporated into the development of hybrid electronic structure methods. Additionally, we draw a distinction between correlation measures and correlation diagnostics, establishing MP2 and CCSD numerical thresholds for <i>I</i><sub>ND</sub><sup>max</sup>, which are to be used as a multireference diagnostic. Our findings further demonstrate that establishing thresholds for other electronic structure methods can be easily accomplished using small data sets. Correlation ; Computer science ; Measure (data warehouse) ; Electronic structure ; Electronic correlation ; Multireference configuration interaction ; Statistical physics ; Data mining ; Electron ; Physics ; Mathematics ; Configuration interaction ; Quantum mechanics ; Molecule Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Catalysis and Oxidation Reactions
Authors: Minarni Shiddiq | Feri Candra | Barri Anand | Mohammad Fisal Rabin
Source: TELKOMNIKA (Telecommunication Computing Electronics and Control) (article, 2023-10-01)
Field: Chemistry — Analytical Chemistry

Neural network with k-fold cross validation for oil palm fruit ripeness prediction The combination of hyperspectral imaging and artificial neural network (ANN) can predict fruit ripeness. This work investigated the application of hyperspectral imaging and ANN models with the k-fold cross-validation method for ripeness prediction of oil palm fresh fruit bunches (FFB) for inline sorting and grading machine vision. Crude palm oil (CPO) is an exporting commodity for countries such as Indonesia and Malaysia. Oil palm FFB ripeness determines the quality of CPO. The unique shapes and colors of FFBs need innovative methods to substitute tedious and cumbersome manual sorting and grading. The oil palm FFB samples used in this study were categorized previously based on color and loosed fruits. We applied the Savitzky-Golay (SG) smoothing filter and 7-fold cross-validation for hyperspectral datasets before being used for the ANN models and a confusion matrix to find the ANN model accuracies. We obtained 72 data points after SG filter and data selection from 523 data points. The prediction results showed an average accuracy of 79.48%, in which three folds with k of 2, 5, and 7 gave the highest accuracy of 90%. The results confirmed the potential use of hyperspectral imaging, with k-fold cross-validation and ANN models for ripeness prediction of oil palm FFBs. Ripeness ; Hyperspectral imaging ; Palm oil ; Artificial neural network ; Artificial intelligence ; Cross-validation ; Computer science ; Mathematics ; Pattern recognition (psychology) ; Environmental science ; Food science ; Chemistry ; Ripening ; Agroforestry Spectroscopy and Chemometric Analyses ; Oil Palm Production and Sustainability ; Date Palm Research Studies
Authors: M.C. Joshi | Edward Sisco
Source: Wiley Interdisciplinary Reviews Forensic Science (article, 2023-05-01)
Field: Chemistry — Spectroscopy

Forensic seized drug analysis: Current challenges and emerging analytical solutions Abstract Over the past decade, new challenges in seized drug analysis have pushed the limits of existing instrumentation and workflows. These challenges include increasing caseloads, complex samples containing previously unidentified substances, and evolving legal requirements. Traditional approaches to seized drug analysis—such as gas chromatography mass spectrometry, color tests, and Fourier transform infrared spectroscopy—can be insufficient for current challenges, motivating the community to explore new instrumentation or data analysis solutions. However, the road to validation, implementation, and adoption of new technologies and solutions can be arduous. In this overview, we discuss the major challenges facing the seized drug community that can be addressed by advances in analytical approaches and highlight some emerging techniques that have been or could be adopted by laboratories in the near‐term. We also discuss specific hurdles limiting the implementation and adoption of new techniques, and approaches to lower these barriers. This article is categorized under: Forensic Chemistry and Trace Evidence &gt; Controlled and Emerging Drug Compounds Forensic Chemistry and Trace Evidence &gt; Emerging Technologies and Methods Forensic Chemistry and Trace Evidence &gt; Presentation and Evaluation of Forensic Science Output Workflow ; Limiting ; Computer science ; Data science ; Instrumentation (computer programming) ; Emerging technologies ; TRACE (psycholinguistics) ; Presentation (obstetrics) ; Nanotechnology ; Risk analysis (engineering) ; Engineering ; Business ; Materials science ; Medicine ; Artificial intelligence Mass Spectrometry Techniques and Applications ; Forensic Toxicology and Drug Analysis ; Metabolomics and Mass Spectrometry Studies
Authors: Roberto Dallocchio | Alessandro Dessì | Barbara Sechi | Paola Peluso
Source: Molecules (review, 2023-11-03)
Field: Chemistry — Spectroscopy

Molecular Dynamics Simulations of Amylose- and Cellulose-Based Selectors and Related Enantioseparations in Liquid Phase Chromatography In the last few decades, theoretical and technical advancements in computer facilities and computational techniques have made molecular modeling a useful tool in liquid-phase enantioseparation science for exploring enantioselective recognition mechanisms underlying enantioseparations and for identifying selector-analyte noncovalent interactions that contribute to binding and recognition. Because of the dynamic nature of the chromatographic process, molecular dynamics (MD) simulations are particularly versatile in the visualization of the three-dimensional structure of analytes and selectors and in the unravelling of mechanisms at molecular levels. In this context, MD was also used to explore enantioseparation processes promoted by amylose and cellulose-based selectors, the most popular chiral selectors for liquid-phase enantioselective chromatography. This review presents a systematic analysis of the literature published in this field, with the aim of providing the reader with a comprehensive picture about the state of the art and what is still missing for modeling cellulose benzoates and the phenylcarbamates of amylose and cellulose and related enantioseparations with MD. Furthermore, advancements and outlooks, as well as drawbacks and pitfalls still affecting the applicability of MD in this field, are also discussed. The importance of integrating theoretical and experimental approaches is highlighted as an essential strategy for profiling mechanisms and noncovalent interaction patterns. Chemistry ; Non-covalent interactions ; Molecular dynamics ; Enantioselective synthesis ; Context (archaeology) ; Amylose ; Cellulose ; Computational chemistry ; Organic chemistry ; Molecule ; Hydrogen bond Analytical Chemistry and Chromatography ; Microfluidic and Capillary Electrophoresis Applications ; Molecular spectroscopy and chirality
Authors: Alessio Zaccone
Source: Physical review. E (article, 2023-10-02)
Field: Materials Science — Materials Chemistry

General theory of the viscosity of liquids and solids from nonaffine particle motions A microscopic formula for the viscosity of liquids and solids is derived rigorously from a first-principles (microscopically reversible) Hamiltonian for particle-bath atomistic motion. The derivation is done within the framework of nonaffine linear response theory. This formula may lead to a valid alternative to the Green-Kubo approach to describe the viscosity of condensed matter systems from molecular simulations without having to fit long-time tails. Furthermore, it provides a direct link between the viscosity, the vibrational density of states of the system, and the zero-frequency limit of the memory kernel. Finally, it provides a microscopic solution to Maxwell's interpolation problem of viscoelasticity by naturally recovering Newton's law of viscous flow and Hooke's law of elastic solids in two opposite limits. Physics ; Viscosity ; Viscoelasticity ; Classical mechanics ; Hamiltonian (control theory) ; Viscous liquid ; Kernel (algebra) ; Interpolation (computer graphics) ; Volume viscosity ; Limit (mathematics) ; Mechanics ; Motion (physics) ; Thermodynamics ; Mathematical analysis ; Mathematics Material Dynamics and Properties ; Advanced Physical and Chemical Molecular Interactions ; Spectroscopy and Quantum Chemical Studies
Authors: Wei Pan | Wenjing Liu | Xiujuan Huang
Source: Current Research in Food Science (article, 2023-12-09)
Field: Chemistry — Analytical Chemistry

Rapid identification of the geographical origin of Baimudan tea using a Multi-AdaBoost model integrated with Raman Spectroscopy The potential of Multi-AdaBoost in spectral analysis is substantial, particularly when combined with weak classifiers and trained to develop into a robust classifier. Given the variable quality of Baimudan tea sourced from diverse regions, the novel application of Raman spectroscopy in conjunction with the Multi-AdaBoost model to analyze the geographic origin of Baimudan tea was introduced. Initially, Raman spectra of Baimudan tea from four distinct origins in Fujian province were gathered, namely Fuan (FA), Fuding (FD), Zhenghe (ZH), and Songxi (SX). Decision Tree (DT) and Support Vector Machine (SVM) models were employed as fitting classifiers to construct the Multi-AdaBoost-DT and Multi-AdaBoost-SVM models. The results demonstrated that the Multi-AdaBoost-DT model exhibited significantly improved recognition rates for FA, FD, ZH, and SX origin compared to the DT model, with the average recognition rate increasing from 86.46% to 91.67%. In contrast, the recognition rates for FA and SX origin in the Multi-AdaBoost-SVM model remained unchanged, attributed to the model having reached a local optimum. The recognition rates of FD origin increased from 91.67% to 95.83%, a significant improvement, while those of ZH origin escalated from 83.33% to 87.50%. The average recognition rate increased from 92.71% to 94.79%. Additionally, Multi-AdaBoost-SVM and Multi-AdaBoost-DT enhanced the sensitivity and specificity of the discrimination outcomes. These results corroborated the effectiveness of the proposed Multi-AdaBoost-SVM model in identifying the geographical origin of Baimudan tea. Moreover, the Multi-AdaBoost model demonstrates potential in elevating the discrimination accuracy of weak classifiers, which bodes well for its application in food authentication and quality control. AdaBoost ; Artificial intelligence ; Support vector machine ; Pattern recognition (psychology) ; Classifier (UML) ; Machine learning ; Decision tree ; Computer science ; Mathematics Spectroscopy and Chemometric Analyses ; Traditional Chinese Medicine Analysis ; Metabolomics and Mass Spectrometry Studies
Authors: Jieqing Li | Yuanzhong Wang | Honggao Liu
Source: Frontiers in Microbiology (article, 2023-01-12)
Field: Chemistry — Analytical Chemistry

Application of spectral image processing with different dimensions combined with large-screen visualization in the identification of boletes species Boletes are favored by consumers because of their unique flavor, rich nutrition and delicious taste. However, the different nutritional values of each species lead to obvious price differences, so shoddy products appear on the market, which affects food safety. The aim of this study was to find a rapid and effective method for boletes species identification. In this paper, 1,707 samples of eight boletes species were selected as the research objects. The original Mid-Infrared (MIR) spectroscopy data were adopted for support vector machine (SVM) modeling. The 11,949 spectral images belong to seven data sets such as two-dimensional correlation spectroscopy (2DCOS) and three-dimensional correlation spectroscopy (3DCOS) were used to carry out Alexnet and Residual network (Resnet) modeling, thus we established 15 models for the identification of boletes species. The results show that the SVM method needs to process complex feature data, the time cost is more than 11 times of other models, and the accuracy is not high enough, so it is not recommended to be used in data processing with large sample size. From the perspective of datasets, synchronous 2DCOS and synchronous 3DCOS have the best modeling results, while one-dimensional (1D) MIR Spectrum dataset has the worst modeling results. After comprehensive analysis, the modeling effect of Resnet on the synchronous 2DCOS dataset is the best. Moreover, we use large-screen visualization technology to visually display the sample information of this research and obtain their distribution rules in terms of species and geographical location. This research shows that deep learning combined with 2DCOS and 3DCOS spectral images can effectively and accurately identify boletes species, which provides a reference for the identification of other fields, such as food and Chinese herbal medicine. Support vector machine ; Pattern recognition (psychology) ; Computer science ; Artificial intelligence ; Identification (biology) ; Visualization ; Sample (material) ; Feature (linguistics) ; Biological system ; Residual ; Process (computing) ; Data mining ; Mathematics ; Chemistry ; Biology ; Algorithm ; Ecology Spectroscopy and Chemometric Analyses ; Essential Oils and Antimicrobial Activity ; Identification and Quantification in Food
Authors: Tim Gould
Source: The Journal of Chemical Physics (article, 2023-02-08)
Field: Materials Science — Materials Chemistry

Toward routine Kohn–Sham inversion using the “Lieb-response” approach Kohn-Sham (KS) inversion, in which the effective KS mean-field potential is found for a given density, provides insights into the nature of exact density functional theory (DFT) that can be exploited for the development of density functional approximations. Unfortunately, despite significant and sustained progress in both theory and software libraries, KS inversion remains rather difficult in practice, especially in finite basis sets. The present work presents a KS inversion method, dubbed the "Lieb-response" approach, that naturally works with existing Fock-matrix DFT infrastructure in finite basis sets, is numerically efficient, and directly provides meaningful matrix and energy quantities for pure-state and ensemble systems. Some additional work yields potential. It thus enables the routine inversion of even difficult KS systems, as illustrated in a variety of problems within this work, and provides outputs that can be used for embedding schemes or machine learning of density functional approximations. The effect of finite basis sets on KS inversion is also analyzed and investigated. Inversion (geology) ; Kohn–Sham equations ; Density functional theory ; Embedding ; Computer science ; Density matrix ; Statistical physics ; Algorithm ; Mathematics ; Applied mathematics ; Physics ; Quantum mechanics ; Artificial intelligence ; Quantum ; Geology Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Spectroscopy and Quantum Chemical Studies