jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Nuno M. Fortunato | Xiaoqing Li | Stephan Schönecker | Ruiwen Xie | Andreas Taubel | Franziska Scheibel | Ingo Opahle | Oliver Gutfleisch | Hongbin Zhang
Source: Chemistry of Materials (article, 2024-07-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

High-Throughput Screening of All-<i>d</i>-Metal Heusler Alloys for Magnetocaloric Applications Due to their versatile composition and customizable properties, A<sub>2</sub>BC Heusler alloys have found applications in magnetic refrigeration, magnetic shape memory effects, permanent magnets, and spintronic devices. The discovery of all-<i>d</i>-metal Heusler alloys with improved mechanical properties compared to those containing main group elements presents an opportunity to engineer Heusler alloys for energy-related applications. Using high-throughput density-functional theory calculations, we screened magnetic all-<i>d</i>-metal Heusler compounds and identified 686 (meta)stable compounds. Our detailed analysis revealed that the inverse Heusler structure is preferred when the electronegativity difference between the A and B/C atoms is small, contrary to conventional Heusler alloys. Additionally, our calculations of Pugh ratios and Cauchy pressures demonstrated that ductile and metallic bonding are widespread in all-<i>d</i>-metal Heuslers, supporting their enhanced mechanical behavior. We identified 49 compounds with a double-well energy surface based on Bain path calculations and magnetic ground states, indicating their potential as candidates for magnetocaloric and shape memory applications. Furthermore, by calculating the free energies, we propose that 11 compounds exhibit structural phase transitions and suggest isostructural substitutions to enhance the magnetocaloric effect. Magnetic refrigeration ; Materials science ; Electronegativity ; Density functional theory ; Heusler compound ; Condensed matter physics ; Pseudoelasticity ; Spintronics ; Metal ; Isostructural ; Crystallography ; Ferromagnetism ; Magnetization ; Magnetic field ; Metallurgy ; Crystal structure ; Computational chemistry ; Microstructure ; Chemistry ; Physics Heusler alloys: electronic and magnetic properties ; Shape Memory Alloy Transformations ; Magnetic and transport properties of perovskites and related materials
Authors: Seyedeh Masoomeh Rahimi | Bahman Ramavandi | Mohammad Moslehi | Mahdi Rahiminia | Negin Nasseh
Source: Applied Water Science (article, 2024-12-19)
Field: Chemistry — Organic Chemistry

Application of CuFe2O4/CuS as a new green magnetic nanocomposite in adsorption of tetracycline from aqueous solutions: mathematical models of thermodynamics, isotherms, and kinetics In current study, a novel adsorbent of CuFe2O4/CuS magnetic nanocomposite (MNC) was constructed via a green approach for tetracycline (TC) removal. The leaf extract of the Alhagi pseudalhagi plant was employed as a green reductant agent. The features of the nanocomposite were characterized using XRD, FTIR, FESEM, TEM, BET, and VSM. Batch studies were conducted to assess the impact of parameters, including pH (3.0–9.0), adsorbent dosage (0.025–2 g/L), TC concentration (5–100 mg/L), and temperature (5–50 °C) on the TC adsorption efficiency. The antibiotic was fully removed at pH 7.0, nanocomposite dose of 1.5 g/L, time of 200 min, and TC content of 5 mg/L. Based on the thermodynamic study, the TC adsorption onto the CuFe2O4/CuS MNC occurred spontaneously and was primarily driven by physical interactions (physisorption). Positive values of ∆H° (enthalpy change) and ∆S° (entropy change) demonstrated that the adsorption process is naturally endothermic, and the degree of dispersion improves with rising temperature. Adsorption kinetics was well fitted by the pseudo-second-order model. The isotherm studies showed that TC can be removed by the adsorbent at a maximum of 31 mg/g. Overall, CuFe2O4/CuS MNC exhibited notable efficacy and cost-effectiveness (reusability: 5 times) for the TC adsorption from water. Kinetics ; Aqueous solution ; Adsorption ; Nanocomposite ; Industrial and production engineering ; Thermodynamics ; Chemistry ; Materials science ; Physical chemistry ; Physics ; Nanotechnology ; Engineering Nanomaterials for catalytic reactions ; Adsorption and biosorption for pollutant removal ; Water Quality Monitoring and Analysis
Authors: Bei Zhang | Jian Wang
Source: Organic Chemistry Frontiers (article, 2024-01-01)
Field: Chemistry — Inorganic Chemistry

Recent advances in metal-free catalytic enantioselective higher-order cycloadditions In this review, we briefly summarize the recent research progress in enantioselective higher-order cycloadditions, focusing on the preparation of medium-sized and multicyclic chiral molecules by metal-free catalysis. Enantioselective synthesis ; Chemistry ; Catalysis ; Order (exchange) ; Combinatorial chemistry ; Metal ; Nanotechnology ; Organic chemistry ; Business Asymmetric Hydrogenation and Catalysis ; Asymmetric Synthesis and Catalysis ; Cyclopropane Reaction Mechanisms
Authors: Belmina Šarić Medić | Nikolina Tomić | Nefeli Lаgopati | Maria Gazouli | Lejla Pojskić
Source: Molecules (review, 2024-11-25)
Field: Materials Science — Materials Chemistry

Advances in Metal and Metal Oxide Nanomaterials for Topical Antimicrobial Applications: Insights and Future Perspectives Nanotechnology has seen significant growth in the past few decades, with the use of nanomaterials reaching a wide scale. Given that antimicrobial resistance is peaking, nanotechnology holds distinct potential in this area. This review discusses recent applications of metal and metal oxide nanoparticles as antibacterial, antifungal, and antiviral agents, particularly focusing on their topical applications and their role in chronic wound therapy. We explore their use in various forms, including coated, encapsulated, and incorporated in hydrogels or as complexes, proposing them as topical antimicrobials with promising properties. Some studies have shown that metal and metal oxide nanoparticles can exhibit cytotoxic and genotoxic effects, while others have found no such properties. These effects depend on factors such as nanoparticle size, shape, concentration, and other characteristics. It is essential to establish the dose or concentration associated with potential toxic effects and to investigate the severity of these effects to determine a threshold below which metal or metal oxide nanoparticles will not produce negative outcomes. Therefore, further research should focus on safety assessments, ensuring that metal and metal oxide nanoparticles can be safely used as therapeutics in biomedical sciences. Nanotechnology ; Nanomaterials ; Antimicrobial ; Oxide ; Nanoparticle ; Metal ; Antifungal ; Materials science ; Chemistry ; Metallurgy ; Biology ; Microbiology ; Organic chemistry Nanoparticles: synthesis and applications ; Wound Healing and Treatments ; Essential Oils and Antimicrobial Activity
Authors: Zongbin Jia | Liang Cheng | Long Zhang | Sanzhong Luo
Source: Nature Communications (article, 2024-05-14)
Field: Chemistry — Organic Chemistry

Asymmetric C–H Dehydrogenative Alkenylation via a Photo-induced Chiral α‑Imino Radical Intermediate The direct alkenylation with simple alkenes stands out as the most ideal yet challenging strategy for obtaining high-valued desaturated alkanes. Here we present a direct asymmetric dehydrogenative α-C(sp<sup>3</sup>)-H alkenylation of carbonyls based on synergistic photoredox-cobalt-chiral primary amine catalysis under visible light. The ternary catalytic system enables the direct coupling of β-keto-carbonyls and alkenes through a cooperative radical addition-dehydrogenation process involving a chiral α-imino radical and Co(II)-metalloradical intermediate. A catalytic H-transfer process involving nitrobenzene is engaged to quench in situ generated cobalt hydride species, ensuring a chemoselective alkenylation in good yields and high enantioselectivities. Dehydrogenation ; Catalysis ; Chemistry ; Nitrobenzene ; Cobalt ; Combinatorial chemistry ; Ternary operation ; Photochemistry ; Amine gas treating ; Hydride ; Enantioselective synthesis ; Medicinal chemistry ; Organic chemistry ; Hydrogen Catalytic C–H Functionalization Methods ; Radical Photochemical Reactions ; Sulfur-Based Synthesis Techniques
Authors: Virender Kumar | Alessandro Pellis | Reinhard Wimmer | Vladimir N. Popok | Jesper de Claville Christiansen | Cristiano Varrone
Source: ACS Sustainable Chemistry & Engineering (article, 2024-06-20)
Field: Materials Science — Biomaterials

Efficient Depolymerization of Poly(ethylene 2,5-furanoate) Using Polyester Hydrolases Poly(ethylene 2,5-furanoate) (PEF) is considered to be the next-generation green polyester and is hailed as a rising star among novel plastics. It is biobased, is nontoxic, and has comparable or improved properties compared to polyethylene terephthalate (PET). Biobased PEF offers lower life-cycle greenhouse gas emissions than PET. However, with its industrial production starting soon, relatively little is known about its actual recyclability. This work reports on the near complete depolymerization of PEF using two efficient PET hydrolases, FastPETase and leaf compost-cutinase (LCC), at loadings 4.5-17 times lower than previously reported. FastPETase and LCC exhibited maximum depolymerization of PEF, measured by weight loss and 2,5-furandicarboxylic acid (FDCA) production, using potassium phosphate-NaOH buffer at 50 and 65 °C, respectively. The 98% depolymerization of 13 g L<sup>-1</sup> PEF film was achieved by three additions of the LCC in 72 h, while 78% weight loss was obtained using FastPETase in controlled conditions. Nonetheless, 92% weight loss was obtained with FastPETase when using only 6 g L<sup>-1</sup> PEF. The main reaction products were identified as FDCA, ethylene glycol, and mono(2-hydroxyethyl)-furanoate. LCC performed better than FastPETase, in terms of both FDCA release and weight loss. The effect of crystallinity was evident on the enzymes' performance, as only 4% to 7% weight loss of crystalline PEF (32%) was recorded. Microscopy studies of the treated PEF films provided information on the surface erosion processes and revealed higher resistance of the crystalline phase, explaining the low level of depolymerization. The study presents important insights into the enzymatic hydrolysis of biobased PEF material and paves the path toward more viable applications within biopolymer waste recycling. Depolymerization ; Polyester ; Ethylene ; Polymer science ; Polymer chemistry ; Chemistry ; Organic chemistry ; Materials science ; Chemical engineering ; Catalysis biodegradable polymer synthesis and properties ; Catalysis for Biomass Conversion ; Carbon dioxide utilization in catalysis
Authors: Guangdi Feng | Yifei Liu | Qiuxiang Zhu | Zhenyu Feng | Shijun Luo | Chuanjie Qin | Luqiu Chen | Yu Xu | Haonan Wang | Muhammad Zubair | Ke Qu | Chang Yang | Shenglan Hao | Fangyu Yue | Chun‐Gang Duan | Junhao Chu | Bobo Tian
Source: Nature Communications (article, 2024-11-09)
Field: Materials Science — Materials Chemistry

Giant tunnel electroresistance through a Van der Waals junction by external ferroelectric polarization The burgeoning interest in two-dimensional semiconductors stems from their potential as ultrathin platforms for next-generation transistors. Nonetheless, there persist formidable challenges in fully obtaining high-performance complementary logic components and the underlying mechanisms for the polarity modulation of transistors are not yet fully understood. Here, we exploit both ferroelectric domain-based nonvolatile modulation of Fermi level in transitional metal dichalcogenides (MoS<sub>2</sub>) and quantum tunneling through nanoscale hexagonal boron nitride (h-BN). Our prototype devices, termed as vertical tunneling ferroelectric field-effect transistor, utilizes a Van der Waals MoS<sub>2</sub>/h-BN/metal tunnel junction as the channel. The Fermi level of MoS<sub>2</sub> is bipolarly tuned by ferroelectric domains and sensitively detected by the direct quantum tunneling strength across the junction, demonstrating an impressive electroresistance ratio of up to 10<sup>9</sup> in the vertical tunneling ferroelectric field-effect transistor. It consumes only 0.16 fJ of energy to open a ratio window exceeding 10<sup>4</sup>. This work not only validates the effectiveness of tailored tunnel barriers in manipulating electronic flow but also highlights a new avenue for the design flexibility and functional versatility of advanced ferroelectric memory technology. van der Waals force ; Ferroelectricity ; Condensed matter physics ; Materials science ; Polarization (electrochemistry) ; Physics ; Optoelectronics ; Chemistry ; Quantum mechanics ; Dielectric ; Molecule ; Physical chemistry 2D Materials and Applications ; Perovskite Materials and Applications ; Graphene research and applications
Authors: Ruibin Bai | Zhou Jun-hui | Siman Wang | Yue Zhang | Tiegui Nan | Bin Yang | Chu Zhang | Jian Yang
Source: Foods (article, 2024-02-04)
Field: Chemistry — Analytical Chemistry

Identification and Classification of Coix seed Storage Years Based on Hyperspectral Imaging Technology Combined with Deep Learning Developing a fast and non-destructive methodology to identify the storage years of <i>Coix seed</i> is important in safeguarding consumer well-being. This study employed the utilization of hyperspectral imaging (HSI) in conjunction with conventional machine learning techniques such as support vector machines (SVM), k-nearest neighbors (KNN), random forest (RF), extreme gradient boosting (XGBoost), as well as the deep learning method of residual neural network (ResNet), to establish identification models for <i>Coix seed</i> samples from different storage years. Under the fusion-based modeling approach, the model's classification accuracy surpasses that of visible to near infrared (VNIR) and short-wave infrared (SWIR) spectral modeling individually. The classification accuracy of the ResNet model and SVM exceeds that of other conventional machine learning models (KNN, RF, and XGBoost). Redundant variables were further diminished through competitive adaptive reweighted sampling feature wavelength screening, which had less impact on the model's accuracy. Upon validating the model's performance using an external validation set, the ResNet model yielded more satisfactory outcomes, exhibiting recognition accuracy exceeding 85%. In conclusion, the comprehensive results demonstrate that the integration of deep learning with HSI techniques effectively distinguishes <i>Coix seed</i> samples from different storage years. Hyperspectral imaging ; Artificial intelligence ; Support vector machine ; Random forest ; Computer science ; Pattern recognition (psychology) ; Coix ; Extreme learning machine ; VNIR ; Machine learning ; Deep learning ; Artificial neural network ; Remote sensing ; Geography Spectroscopy and Chemometric Analyses ; Remote Sensing and Land Use ; Water Quality Monitoring and Analysis
Authors: Ying Zhi | Yao Jiang | Diwen Ke | Xianlei Hu | Liu Xiang-hua
Source: Materials (article, 2024-03-16)
Field: Materials Science — Materials Chemistry

Review on Cellular Automata for Microstructure Simulation of Metallic Materials The cellular automata (CA) method has played an important role in the research and development of metallic materials. CA can interpret the microstructure changes of materials and obtain more abundant, accurate and intuitive information of microstructure evolution than conventional methods. CA can visually represent the process of grain formation, growth, development and change to us in a graphical way, which can assist us in analysis, thinking and solving problems. In the last five years, the application of CA in materials research has been rapidly developed, and CA has begun to occupy an increasingly important position in the simulation research of metallic materials. After introducing the advantages and limitations of CA compared to other widely used simulation methods, the purpose of this paper is to review the recent application progress on the microstructure simulation of metallic materials using CA, such as solidification, recrystallization, phase transformation and carbide precipitation occurring during forming and heat treatment. Specifically, recent research advances on microstructure simulation by CA in the fields of additive manufacturing, welding, asymmetrical rolling, corrosion prevention, etc., are also elaborated in this paper. Furthermore, this paper points out the future work direction of CA simulation in the research of metallic materials, especially in the simulation of the crystal structure, the prediction of mechanical properties, CA simulation software and rule systems, etc. These are expected to attract wide attention of researchers in the field of metallic materials and promote the development of CA in materials research. Cellular automaton ; Microstructure ; Materials science ; Recrystallization (geology) ; Mechanical engineering ; Computer science ; Process engineering ; Metallurgy ; Engineering ; Geology ; Artificial intelligence Microstructure and mechanical properties ; Solidification and crystal growth phenomena ; Metallurgy and Material Forming
Authors: Syed Danish Ali | Sameen Raut | Joseph Dahlen | Laurence R. Schimleck | Richard Bergman | Zhou Zhang | Vahid Nasir
Source: Sensors (article, 2024-03-21)
Field: Chemistry — Organic Chemistry

Utilization of Synthetic Near-Infrared Spectra via Generative Adversarial Network to Improve Wood Stiffness Prediction Near-infrared (NIR) spectroscopy is widely used as a nondestructive evaluation (NDE) tool for predicting wood properties. When deploying NIR models, one faces challenges in ensuring representative training data, which large datasets can mitigate but often at a significant cost. Machine learning and deep learning NIR models are at an even greater disadvantage because they typically require higher sample sizes for training. In this study, NIR spectra were collected to predict the modulus of elasticity (MOE) of southern pine lumber (training set = 573 samples, testing set = 145 samples). To account for the limited size of the training data, this study employed a generative adversarial network (GAN) to generate synthetic NIR spectra. The training dataset was fed into a GAN to generate 313, 573, and 1000 synthetic spectra. The original and enhanced datasets were used to train artificial neural networks (ANNs), convolutional neural networks (CNNs), and light gradient boosting machines (LGBMs) for MOE prediction. Overall, results showed that data augmentation using GAN improved the coefficient of determination (R<sup>2</sup>) by up to 7.02% and reduced the error of predictions by up to 4.29%. ANNs and CNNs benefited more from synthetic spectra than LGBMs, which only yielded slight improvement. All models showed optimal performance when 313 synthetic spectra were added to the original training data; further additions did not improve model performance because the quality of the datapoints generated by GAN beyond a certain threshold is poor, and one of the main reasons for this can be the size of the initial training data fed into the GAN. LGBMs showed superior performances than ANNs and CNNs on both the original and enhanced training datasets, which highlights the significance of selecting an appropriate machine learning or deep learning model for NIR spectral-data analysis. The results highlighted the positive impact of GAN on the predictive performance of models utilizing NIR spectroscopy as an NDE technique and monitoring tool for wood mechanical-property evaluation. Further studies should investigate the impact of the initial size of training data, the optimal number of generated synthetic spectra, and machine learning or deep learning models that could benefit more from data augmentation using GANs. Artificial neural network ; Computer science ; Near-infrared spectroscopy ; Artificial intelligence ; Pattern recognition (psychology) ; Training set ; Generative adversarial network ; Convolutional neural network ; Synthetic data ; Data set ; Machine learning ; Deep learning ; Optics Wood and Agarwood Research ; Wood Treatment and Properties ; Spectroscopy and Chemometric Analyses
Authors: Hang Zeng | Zhuoyan Lv | Xiaoyan Sun | Tong Yang | Wei Wu | Shipeng Dong | Liang Mao
Source: Environment & Health (review, 2024-03-19)
Field: Materials Science — Materials Chemistry

Predicting Bioaccumulation of Nanomaterials: Modeling Approaches with Challenges Understanding the bioaccumulation of nanomaterials (NMs) by organisms is essential in evaluating their potential ecotoxicity. However, the experimental determination of bioaccumulation is substantially challenging, which spawned the development of prediction approaches via establishing models for various NMs. Conventional modeling approaches, such as the biotic ligand model (BLM), partition coefficients, accumulation factor models, and quantitative structure-activity relationship (QSAR) models, were initially used in the application of NMs, aiming to provide a reliable quantitative dose in a resource-saving way. These methods, which are based on the uptake patterns of substances, probably lead to deviated results due to the different uptake behaviors of NMs. In this study, currently developed models to evaluate the bioaccumulation of NMs are critically reviewed, with their feasibilities and limitations being analyzed. In addition, the recently developed machine learning amended models have taken great efforts in realizing biological behaviors of NMs in organisms by providing <i>in silico</i> predictions. Though this data-driven approach has limitations in mechanism exploration, it may give different insights into the bioaccumulation model establishment and critical feature identification. Bioaccumulation ; Ecotoxicity ; In silico ; Quantitative structure–activity relationship ; Identification (biology) ; Biochemical engineering ; Computer science ; Environmental science ; Environmental chemistry ; Chemistry ; Biology ; Ecology ; Engineering ; Machine learning Nanoparticles: synthesis and applications ; Environmental Toxicology and Ecotoxicology ; Computational Drug Discovery Methods