jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jialin Tang | Guotai Li | Qi Wang | Jiongzhi Zheng | Lin Cheng | Ruiqiang Guo
Source: International Journal of Heat and Mass Transfer (article, 2023-03-02)
Field: Materials Science — Materials Chemistry

Effect of four-phonon scattering on anisotropic thermal transport in bulk hexagonal boron nitride by machine learning interatomic potential Thermal conductivity ; Phonon ; Materials science ; Condensed matter physics ; Anisotropy ; Scattering ; Phonon scattering ; van der Waals force ; Boron nitride ; Density functional theory ; Thermoelectric materials ; Isotropy ; Nanotechnology ; Physics ; Optics ; Composite material ; Computational chemistry ; Chemistry Thermal properties of materials ; Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices
Authors: Francesca Romana Bertani | Arianna Mencattini | Lucia Gambacorta | Adele De Ninno | Luca Businaro | Michele Solfrizzo | Annamaria Gerardino | Eugenio Martinelli
Source: Journal of Food Composition and Analysis (article, 2023-11-23)
Field: Chemistry — Analytical Chemistry

Aflatoxins detection in almonds via fluorescence imaging and deep neural network approach Aflatoxin ; Artificial intelligence ; Contamination ; Pattern recognition (psychology) ; Computer science ; Support vector machine ; RGB color model ; Artificial neural network ; ENCODE ; Food science ; Chemistry ; Biology Spectroscopy and Chemometric Analyses ; Mycotoxins in Agriculture and Food ; Identification and Quantification in Food
Authors: Meng Shi | Peng Wang | Yang Zhang | Youlei Tu | Jie Zhang | Jiang Li | Shaoyun Guo
Source: Composites Science and Technology (article, 2023-05-15)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Two-layer structural method: An efficient strategy inspired by machine learning for nanocomposites with magnetic particles for electromagnetic interference shielding Electromagnetic shielding ; Materials science ; EMI ; Electromagnetic interference ; Composite material ; Composite number ; Nanocomposite ; Electrical conductor ; Particle (ecology) ; Layer (electronics) ; Electromagnetic radiation ; Computer science ; Optics ; Telecommunications Electromagnetic wave absorption materials ; Advanced Antenna and Metasurface Technologies
Authors: Joseph P. Heindel | Kristina M. Herman | Sotiris S. Xantheas
Source: Annual Review of Physical Chemistry (review, 2023-04-24)
Field: Materials Science — Materials Chemistry

Many-Body Effects in Aqueous Systems: Synergies Between Interaction Analysis Techniques and Force Field Development Interaction analysis techniques, including the many-body expansion (MBE), symmetry-adapted perturbation theory, and energy decomposition analysis, allow for an intuitive understanding of complex molecular interactions. We review these methods by first providing a historical context for the study of many-body interactions and discussing how nonadditivities emerge from Hamiltonians containing strictly pairwise-additive interactions. We then elaborate on the synergy between these interaction analysis techniques and the development of advanced force fields aimed at accurately reproducing the Born-Oppenheimer potential energy surface. In particular, we focus on ab initio-based force fields that aim to explicitly reproduce many-body terms and are fitted to high-level electronic structure results. These force fields generally incorporate many-body effects through (<i>a</i>) parameterization of distributed multipoles, (<i>b</i>) explicit fitting of the MBE, (<i>c</i>) inclusion of many-atom features in a neural network, and (<i>d</i>) coarse-graining of many-body terms into an effective two-body term. We also discuss the emerging use of the MBE to improve the accuracy and speed of ab initio molecular dynamics. Force field (fiction) ; Ab initio ; Statistical physics ; Computer science ; Interaction energy ; Context (archaeology) ; Pairwise comparison ; Physics ; Artificial intelligence ; Quantum mechanics ; Molecule Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Spectroscopy and Quantum Chemical Studies
Authors: Guangwu Zhang | Dong Shi-lin | Chao Yang | Dan Han | Gongming Xin | Xinyu Wang
Source: Applied Physics Letters (article, 2023-07-31)
Field: Materials Science — Materials Chemistry

Revisiting four-phonon scattering in WS2 monolayer with machine learning potential Recently, the importance of the higher-order anharmonic effect on the thermal transport has been demonstrated in a few two-dimensional (2D) materials. As a member of 2D materials, the WS2 monolayer possesses excellent properties, but its higher-order phonon anharmonic effect is still unclear. As an emerging tool for atomistic simulations, the machine learning Gaussian approximation potential (GAP) has good computational speed and accuracy. In this work, the effect of the four-phonon scattering on the thermal transport properties of the WS2 monolayer is systematically investigated by combining a well-trained GAP with the Boltzmann transport equation. We find that the in-plane thermal conductivity of the WS2 monolayer decreases by 34.68% at 300 K compared to the results without considering the four-phonon scattering, which mainly originates from the strong low-frequency redistribution scattering process. The temperature-induced phonon renormalization in WS2 monolayer is investigated using the temperature-dependent effective potential method. The results show that phonon modes slightly soften with increasing temperature, and the temperature effect weakens the four-phonon scattering while hardly affect the three-phonon scattering. Our work reveals that previous studies considering only the three-phonon scattering and the temperature effect are insufficient, and the four-phonon interaction must be introduced to accurately describe the thermal transport properties of the WS2 monolayer. Phonon ; Condensed matter physics ; Scattering ; Phonon scattering ; Monolayer ; Anharmonicity ; Thermal conductivity ; Boltzmann equation ; Materials science ; Physics ; Nanotechnology ; Thermodynamics ; Optics Thermal properties of materials ; Machine Learning in Materials Science ; 2D Materials and Applications
Authors: Agustami Sitorus | Suluh Pambudi | Wutthiphong Boodnon | Ravipat Lapcharoensuk
Source: Analytical Letters (article, 2023-04-30)
Field: Chemistry — Analytical Chemistry

Near-Infrared Spectroscopy with Machine Learning for Classifying and Quantifying Nutmeg Adulteration Near-infrared spectroscopy (NIRS) provides broadbands, overtones, and combinations of organic-bond vibrations and has been used to characterize agricultural and food products. The adulteration of grated nutmeg with cinnamon is extremely profitable and difficult to detect; to prevent retail fraud, it is vital to differentiate between these materials. This study proposes a model for classifying the adulteration of nutmeg with cinnamon and predicting the level of adulteration. NIR spectra were characterized with six machine learning (ML) algorithms, namely, the principal component-multilayer perceptron (PC-MLP), principal component-linear discriminant analysis (PC-LDA), partial least squares regression (PLSR), support vector machine (SVM), random forest (RF), and decision tree (DT) methods. PC-MLP provided 100% accuracy in calibration and prediction in distinguishing nutmeg from cinnamon. In addition, this approach showed excellent performance in predicting the adulteration ratio of nutmeg and cinnamon with a high coefficient of determination of prediction (R2pred) value of 0.9969, low root mean square error of prediction (RMSEP) value of 0.5728%, and high ratio of prediction to deviation (RPD) value of 17.9605. Therefore, this study indicates the potential of integrating NIR spectroscopy with PC-MLP to classify and quantify the adulteration of nutmeg. Nutmeg ; Principal component analysis ; Linear discriminant analysis ; Partial least squares regression ; Support vector machine ; Chemistry ; Pattern recognition (psychology) ; Chemometrics ; Artificial intelligence ; Near-infrared spectroscopy ; Coefficient of determination ; Multilayer perceptron ; Statistics ; Machine learning ; Mathematics ; Chromatography ; Food science ; Artificial neural network ; Computer science Spectroscopy and Chemometric Analyses ; Phytochemicals and Antioxidant Activities ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Noura El‐Ahmady El‐Naggar | Nashwa H. Rabei | Mohamed F. El‐Mansy | Omar T. Elmessiry | Mostafa K. El-Sherbeny | Mohanad E. El-Saidy | Mohamed T. Sarhan | Manar G. Helal
Source: Scientific Reports (article, 2023-08-04)
Field: Materials Science — Materials Chemistry

Artificial neural network approach for prediction of AuNPs biosynthesis by Streptomyces flavolimosus, characterization, antitumor potency in-vitro and in-vivo against Ehrlich ascites carcinoma Gold nanoparticles (AuNPs) have emerged as promising and versatile nanoparticles for cancer therapy and are widely used in drug and gene delivery, biomedical imaging, diagnosis, and biosensors. The current study describes a biological-based strategy for AuNPs biosynthesis using the cell-free supernatant of Streptomyces flavolimosus. The biosynthesized AuNPs have an absorption peak at 530-535 nm. The TEM images indicate that AuNPs were spherical and ranged in size from 4 to 20 nm. The surface capping molecules of AuNPs are negatively charged, having a Zeta potential of - 10.9 mV. FTIR analysis revealed that the AuNPs surface composition contains a variety of functional groups as -OH, C-H, N-, C=O, NH<sub>3</sub><sup>+</sup>, amine hydrochloride, amide group of proteins, C-C and C-N. The bioprocess variables affecting AuNPs biosynthesis were optimized by using the central composite design (CCD) in order to maximize the AuNPs biosynthesis. The maximum yield of AuNPs (866.29 µg AuNPs/mL) was obtained using temperature (35 °C), incubation period (4 days), HAuCl<sub>4</sub> concentration (1000 µg/mL) and initial pH level 6. Comparison was made between the fitness of CCD versus Artificial neural network (ANN) approach based on their prediction and the corresponding experimental results. AuNPs biosynthesis values predicted by ANN exhibit a more reasonable agreement with the experimental result. The anticancer activities of AuNPs were assessed under both in vitro and in vivo conditions. The results revealed a significant inhibitory effect on the proliferation of the MCF-7 and Hela carcinoma cell lines treated with AuNPs with IC<sub>50</sub> value of 13.4 ± 0.44 μg/mL and 13.8 ± 0.45 μg/mL for MCF-7 and Hela cells; respectively. Further, AuNPs showed potential inhibitory effect against tumor growth in tumor-bearing mice models. AuNPs significantly reduced the tumor volume, tumor weight, and decreased number of viable tumor cells in EAC bearing mice. In vivo ; Colloidal gold ; Biosynthesis ; HeLa ; Zeta potential ; In vitro ; Nuclear chemistry ; Chemistry ; Nanoparticle ; Biochemistry ; Biophysics ; Combinatorial chemistry ; Materials science ; Biology ; Nanotechnology ; Biotechnology ; Gene Nanoparticles: synthesis and applications ; Graphene and Nanomaterials Applications ; Nanoparticle-Based Drug Delivery
Authors: Mafalda Reis Pereira | Filipe Neves dos Santos | Fernando Tavares | Mário Cunha
Source: Frontiers in Plant Science (article, 2023-08-16)
Field: Chemistry — Analytical Chemistry

Enhancing host-pathogen phenotyping dynamics: early detection of tomato bacterial diseases using hyperspectral point measurement and predictive modeling Early diagnosis of plant diseases is needed to promote sustainable plant protection strategies. Applied predictive modeling over hyperspectral spectroscopy (HS) data can be an effective, fast, cost-effective approach for improving plant disease diagnosis. This study aimed to investigate the potential of HS point-of-measurement (POM) data for in-situ, non-destructive diagnosis of tomato bacterial speck caused by <i>Pseudomonas syringae</i> pv. <i>tomato</i> (Pst), and bacterial spot, caused by <i>Xanthomonas euvesicatoria</i> (Xeu), on leaves (cv. cherry). Bacterial artificial infection was performed on tomato plants at the same phenological stage. A sensing system composed by a hyperspectral spectrometer, a transmission optical fiber bundle with a slitted probe and a white light source were used for spectral data acquisition, allowing the assessment of 3478 spectral points. An applied predictive classification model was developed, consisting of a normalizing pre-processing strategy allied with a Linear Discriminant Analysis (LDA) for reducing data dimensionality and a supervised machine learning algorithm (Support Vector Machine - SVM) for the classification task. The predicted model achieved classification accuracies of 100% and 74% for Pst and Xeu test set assessments, respectively, before symptom appearance. Model predictions were coherent with host-pathogen interactions mentioned in the literature (e.g., changes in photosynthetic pigment levels, production of bacterial-specific molecules, and activation of plants' defense mechanisms). Furthermore, these results were coherent with visual phenotyping inspection and PCR results. The reported outcomes support the application of spectral point measurements acquired <i>in-vivo</i> for plant disease diagnosis, aiming for more precise and eco-friendly phytosanitary approaches. Hyperspectral imaging ; Artificial intelligence ; Support vector machine ; Linear discriminant analysis ; Biological system ; Computer science ; Biology ; Pattern recognition (psychology) ; Computational biology ; Machine learning ; Biotechnology Spectroscopy and Chemometric Analyses ; Plant Pathogens and Fungal Diseases ; Plant Pathogenic Bacteria Studies
Authors: Alena Vishina | Olle Eriksson | Heike C. Herper
Source: Acta Materialia (article, 2023-09-20)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Stable and metastable rare-earth-free permanent magnets from a database of predicted crystal structures With the recent developments in crystal structure prediction, databases of new (not previously synthesized) materials are being created. One of these databases contains more than a million entries with the distance to the Convex Hull predicted by crystal-graph attention networks. Hence, stable and metastable materials can be extracted and then investigated for any desired properties. A high-throughput and data-mining approach we previously developed to search for rare-earth-free permanent magnets was applied to these compounds. As a result, four promising candidates for novel rare-earth-free permanent magnets were discovered with high magnetization, high uniaxial magnetocrystalline anisotropy, and high Curie temperature - Ta3ZnFe8, AlFe2, Co3Ni2, and Fe3Ge. The materials were investigated in more detail and all were verified to be dynamically stable. Materials science ; Magnet ; Metastability ; Magnetocrystalline anisotropy ; Curie temperature ; Rare earth ; Anisotropy ; Crystal (programming language) ; Magnetization ; Curie ; Crystal structure ; Ferromagnetism ; Condensed matter physics ; Magnetic anisotropy ; Crystallography ; Metallurgy ; Computer science ; Mechanical engineering ; Magnetic field ; Physics ; Optics Magnetic Properties of Alloys ; Magnetic and transport properties of perovskites and related materials ; Hydrogen Storage and Materials
Authors: Daniel Iheanacho Udunwa | Okechukwu Dominic Onukwuli | Mathew Chukwudi Menkiti | Valentine Chikaodili Anadebe | Maduabuchi Arinzechukwu Chidiebere
Source: Heliyon (article, 2023-07-01)
Field: Materials Science — Materials Chemistry

1-Butyl-3-methylimidazolium methane sulfonate ionic liquid corrosion inhibitor for mild steel alloy: Experimental, optimization and theoretical studies The current research reports the performance of 1-butyl-3-methylimidazolium methane sulfonate ([C<sub>4</sub>MIM][OMs](IL)) as effective corrosion inhibitor for mild steel in 1 M H<sub>2</sub>SO<sub>4</sub> electrolyte. For proper evaluation, weight loss, electrochemical study, theoretical modeling and optimization techniques were used. Weight loss and electrochemical methods shows that the inhibition performance of [C<sub>4</sub>MIM][OMs] on the metal surface strengthens as the concentration increases. Maximum inhibition efficiency of 85.71%, 92.5% and 91.1% at 0.8 g L<sup>-1</sup> concentration of [C<sub>4</sub>MIM][OMs] were obtained from the weight loss, polarization and impedance studies, respectively. In addition, response surface methodology (RSM) a statistical tool was used for modeling and optimization of the empirical data. The RSM model validates the empirical findings. Also, DFT/MD-simulation investigations evidenced that [C<sub>4</sub>MIM][OMs] forms a barrier film on the mild steel surface. The result shows that the synthesized [C<sub>4</sub>MIM][OMs] could open up opportunities in corrosion and materials protection for sustainability. C4mim ; Ionic liquid ; Corrosion ; Materials science ; Polarization (electrochemistry) ; Sulfonate ; Dielectric spectroscopy ; Response surface methodology ; Electrochemistry ; Electrolyte ; Chemical engineering ; Chemistry ; Metallurgy ; Physical chemistry ; Chromatography ; Organic chemistry ; Catalysis ; Electrode ; Engineering Corrosion Behavior and Inhibition ; Concrete Corrosion and Durability ; Concrete and Cement Materials Research
Authors: Gabriele Scandura | Sana Eid | Ali A. Alnajjar | Twinkle Paul | Georgios N. Karanikolos | Dinesh Shetty | Khalid M. Omer | Rami Alqerem | Alaa Juma | Huanting Wang | Hassan A. Arafat | Ludovic F. Dumée
Source: Materials Advances (article, 2023-01-01)
Field: Chemistry — Inorganic Chemistry

Photo-responsive metal–organic frameworks – design strategies and emerging applications in photocatalysis and adsorption The emergence of light-responsive metal–organic frameworks represents an opportunity for stimuli responsive platform design for environmental applications. Metal-organic framework ; Photocatalysis ; Adsorption ; Nanotechnology ; Materials science ; Environmental science ; Chemistry ; Catalysis Metal-Organic Frameworks: Synthesis and Applications ; Nanoplatforms for cancer theranostics ; Advanced Photocatalysis Techniques
Authors: Mariya Edeleva | Kyann De Smit | Simon Debrie | Annabelle Verberckmoes | Yoshi W. Marien | Dagmar D’hooge
Source: Current Opinion in Green and Sustainable Chemistry (article, 2023-06-22)
Field: Materials Science — Polymers and Plastics

Molecular scale-driven upgrading of extrusion technology for sustainable polymer processing and recycling Our polymer material and processing industry heavily relies on single- and twin-screw extrusion technology. To facilitate a circular economy technological upgrades, bridging experimental characterization techniques and the predictive power of modeling and software tools are although indispensable. The current work highlights engineering challenges and solution strategies to make (reactive) extrusion technology more sustainable and reliable. Molecular scale-driven case studies are included dealing with (i) energy and residence time optimization, (ii) the enlargement of the pool of polymers to be processed or synthesized (e.g. biopolymers and more well-defined compatibilizers), and (iii) polymer recycling applications, both chemical and mechanical. These case studies consider linear, branched, as well as cross-linked polymers. Extrusion ; Polymer ; Process engineering ; Reactive extrusion ; Plastics extrusion ; Materials science ; Bridging (networking) ; Work (physics) ; Scale (ratio) ; Characterization (materials science) ; Manufacturing engineering ; Mechanical engineering ; Nanotechnology ; Computer science ; Engineering ; Composite material Polymer crystallization and properties ; biodegradable polymer synthesis and properties ; Advanced Polymer Synthesis and Characterization
Authors: Yaokang Lv | Juncheng Wang | Ding‐Wei Ji | Jianhang Li | Suaisuai Zhao | Yingjian Zhao | Zhiwei Cai | Xuehua He | Xiaofang Sun
Source: Frontiers in Energy Research (article, 2023-01-26)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Carbonaceous electrode materials for supercapacitor: Preparation and surface functionalization Supercapacitors became more and more important recently in the area of energy storage and conversion. Their large power deliveries abilities, high stability and environmental friendliness characteristics draw tremendous attention in high-power applications such as public transit networks. Carbonaceous materials with unique surface and electrochemical properties were widely used in supercapacitors as electrode materials. This review focuses on the developments in supercapacitor electrodes made from carbonaceous materials recently, their working principle and evaluation parameters were summarized briefly. The preparation methods and electrochemical properties of different carbonaceous materials were compared and classified. It was found that the surface situation (e.g., porous structure, hydrophilic) of carbonaceous materials strongly affect the electrochemical performances of supercapacitor. So far, active carbons would be the most applicable carbonaceous electrode materials owing to their good chemical stability and conductivity, extensive accessibility inexpensiveness. But their energy densities still fall behind practical demands. Both theoretical calculations and experimental studies show that surface modification and doping of carbonaceous materials can not only optimize their pore size, structure, conductivity and surface properties, but also can introduce extra pseudocapacitance into these materials. Considering global environmental pollution and energy shortage problems nowadays, we sincerely suggested that future work should focus on domestic, medical and industrial wastes residues derived carbonaceous materials and scaled production process such as reactors and exhaust gas treatment. Supercapacitor ; Materials science ; Pseudocapacitance ; Surface modification ; Nanotechnology ; Environmental pollution ; Energy storage ; Electrode ; Economic shortage ; Specific surface area ; Porosity ; Electrochemistry ; Chemical engineering ; Composite material ; Environmental science ; Power (physics) ; Chemistry ; Organic chemistry Supercapacitor Materials and Fabrication ; Electrocatalysts for Energy Conversion ; Advanced battery technologies research