jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Shahram Dindarlou | Gustavo M. Castelluccio
Source: International Journal of Plasticity (article, 2024-01-28)
Field: Materials Science — Materials Chemistry

Optimization of crystal plasticity parameters with proxy materials data for alloy single crystals Multiscale modelling approaches have demonstrated ample value in understanding, predicting, and engineering materials response. While increasing computational power has aided in modelling atomic behaviour from first principles, mesoscale mechanisms such as intergranular failure or crack initiation still rely strongly on correlative models. Crystal Plasticity models have been extensively used to relate process-property-structure in metallic materials including mesoscale effects such as texture, microplasticity, and failure variability. However, models still suffer from low predictive power at the grain scale, which leads to poor damage prognosis outside the experimental calibration set. In addition to model form error, mesoscale uncertainty is dominated by an inadequate model parameterization that arises from calibration exclusively to macroscopic experimental data. This work explores parameter uncertainty in Crystal Plasticity models and proposes a hybrid physic-based and numerical optimization approach to identify parameters associated to mesoscale strengthening in FCC metals and alloys. The strength and novelty of the approach rely on calibrating parameters independently using single-crystal and polycrystal stress–strain curves. We further demonstrate that multiple materials can be incorporated into a single optimization algorithm to robustly quantify mesoscale material-invariant parameters. These values are then used to blindly predict the response of single- and poly-crystals engineering alloys. As a result, our approach mitigates modelling uncertainty by augmenting the data for calibration with single crystal experiments from different materials with similar dislocation structures (i.e., proxy materials). The results provide the basis for a robust parameterization of crystal plasticity models that can predict single- and poly-crystal responses for engineering alloys even in the absence of direct experimental data. Materials science ; Crystal plasticity ; Alloy ; Plasticity ; Proxy (statistics) ; Composite material ; Computer science Titanium Alloys Microstructure and Properties ; Advanced materials and composites ; Microstructure and Mechanical Properties of Steels
Authors: Cornelia Breitkopf
Source: Thermo (article, 2024-02-13)
Field: Materials Science — Materials Chemistry

Theoretical Characterization of Thermal Conductivities for Polymers—A Review Polymer thermal conductivities play an important role for their potential use in industrial applications. Therefore, great efforts have been made to investigate fundamental structure–property relationships to understand and predict thermal conductivities for polymers and their composites. The review summarizes selected well-proven microscopic theoretical approaches to calculate thermal conductivities such as EMD, NEMD, EMT, and BTE, and cites examples to focus on different qualitative aspects of recent polymer theoretical research. Examples other than polymer materials are given as supplemental information to support the general discussion of heat transport phenomena in solid materials. Characterization (materials science) ; Polymer ; Materials science ; Thermal ; Thermal conductivity ; Polymer science ; Chemical engineering ; Thermodynamics ; Composite material ; Nanotechnology ; Physics ; Engineering Thermal properties of materials ; Composite Material Mechanics ; Polymer crystallization and properties
Authors: Janez Lapajne | Ana Vojnović | Andrej Vončina | Uroš Žibrat
Source: Plants (article, 2024-07-11)
Field: Chemistry — Analytical Chemistry

Enhancing Water-Deficient Potato Plant Identification: Assessing Realistic Performance of Attention-Based Deep Neural Networks and Hyperspectral Imaging for Agricultural Applications Hyperspectral imaging has emerged as a pivotal technology in agricultural research, offering a powerful means to non-invasively monitor stress factors, such as drought, in crops like potato plants. In this context, the integration of attention-based deep learning models presents a promising avenue for enhancing the efficiency of stress detection, by enabling the identification of meaningful spectral channels. This study assesses the performance of deep learning models on two potato plant cultivars exposed to water-deficient conditions. It explores how various sampling strategies and biases impact the classification metrics by using a dual-sensor hyperspectral imaging systems (VNIR -Visible and Near-Infrared and SWIR-Short-Wave Infrared). Moreover, it focuses on pinpointing crucial wavelengths within the concatenated images indicative of water-deficient conditions. The proposed deep learning model yields encouraging results. In the context of binary classification, it achieved an area under the receiver operating characteristic curve (AUC-ROC-Area Under the Receiver Operating Characteristic Curve) of 0.74 (95% CI: 0.70, 0.78) and 0.64 (95% CI: 0.56, 0.69) for the KIS Krka and KIS Savinja varieties, respectively. Moreover, the corresponding F1 scores were 0.67 (95% CI: 0.64, 0.71) and 0.63 (95% CI: 0.56, 0.68). An evaluation of the performance of the datasets with deliberately introduced biases consistently demonstrated superior results in comparison to their non-biased equivalents. Notably, the ROC-AUC values exhibited significant improvements, registering a maximum increase of 10.8% for KIS Krka and 18.9% for KIS Savinja. The wavelengths of greatest significance were observed in the ranges of 475-580 nm, 660-730 nm, 940-970 nm, 1420-1510 nm, 1875-2040 nm, and 2350-2480 nm. These findings suggest that discerning between the two treatments is attainable, despite the absence of prominently manifested symptoms of drought stress in either cultivar through visual observation. The research outcomes carry significant implications for both precision agriculture and potato breeding. In precision agriculture, precise water monitoring enhances resource allocation, irrigation, yield, and loss prevention. Hyperspectral imaging holds potential to expedite drought-tolerant cultivar selection, thereby streamlining breeding for resilient potatoes adaptable to shifting climates. Hyperspectral imaging ; Identification (biology) ; Artificial neural network ; Agriculture ; Computer science ; Artificial intelligence ; Environmental science ; Agricultural engineering ; Biochemical engineering ; Engineering ; Biology ; Botany ; Ecology Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Advanced Chemical Sensor Technologies
Authors: Muhammad Syukri Sadimantara | Bambang Dwi Argo | Sucipto Sucipto | Dimas Firmanda Al Riza | Yusuf Hendrawan
Source: Journal of Robotics and Control (JRC) (article, 2024-01-10)
Field: Chemistry — Analytical Chemistry

The Classification of Aflatoxin Contamination Level in Cocoa Beans using Fluorescence Imaging and Deep learning Aflatoxin contamination in cacao is a significant problem in terms of trade losses and health effects. This calls for the need for a non-invasive, precise, and effective detection strategy. This research contribution is to determine the best deep-learning model to classify the aflatoxin contamination level in cocoa beans based on fluorescence images and deep learning to improve performance in the classification. The process involved inoculating and incubating Aspergillus flavus (6mL/100g) to obtain aflatoxin-contaminated cocoa beans for 7 days during the incubation period. Liquid Mass Chromatography (LCMS) was used to quantify the aflatoxin in order to categorize the images into different levels including “free of aflatoxin”, “contaminated below the limit”, and “contaminated above the limit”. 300 images were acquired through a mini studio equipped with UV lamps. The aflatoxin level was classified using several pre-trained CNN approaches which has high accuracy such as GoogLeNet, SqueezeNet, AlexNet, and ResNet50. The sensitivity analysis showed that the highest classification accuracy was found in the GoogLeNet model with optimizer: Adam and learning rate: 0.0001 by 96.42%. The model was tested using a testing dataset and obtain accuracy of 96% based on the confusion matrix. The findings indicate that combining CNN with fluorescence images improved the ability to classify the amount of aflatoxin contamination in cacao beans. This method has the potential to be more accurate and economical than the current approach, which could be adapted to reduce aflatoxin's negative effects on food safety and cacao trade losses. Aflatoxin ; Contamination ; Environmental science ; Food science ; Environmental chemistry ; Chemistry ; Biology ; Ecology Spectroscopy and Chemometric Analyses
Authors: Ladan Baziar | Leila Emami | Zahra Rezaei | Aida Solhjoo | Amirhossein Sakhteman | Soghra Khabnadideh
Source: Scientific Reports (article, 2024-11-03)
Field: Chemistry — Organic Chemistry

Design, synthesis, biological evaluation and computational studies of 4-Aminopiperidine-3, 4-dihyroquinazoline-2-uracil derivatives as promising antidiabetic agents A novel series of 4-aminopiperidin-3,4-dihyroquinazoline-2-uracil derivatives (9a-9 L) were logically designed and synthesized as potent DPP4 inhibitors as antidiabetic agents. Chemical structure of all new compounds were confirmed by different spectroscopic methods. The designed compounds were evaluated using a MAK 203 kit as DPP4 inhibitors in comparison with Sitagliptin. The biological evaluation revealed that compound 9i bearing chloro substitution on phenyl moiety of 6-bromo quinazoline ring had promising inhibitory activity with IC<sub>50</sub> = 9.25 ± 0.57 µM. The toxicity test of all compounds confirmed safety profile of them. Kinetic studies showed that compound 9i exhibited a competitive-type inhibition with a K<sub>i</sub> value of 12.01 µM. Computational approach supported the rationality of our design strategy, as 9i represented appropriate binding interactions with the active sites of DPP4 target. MD simulation outputs validated the stability of ligand 9i at DPP4 active site. Also, Density functional theory (DFT) including HOMO-LUMO energies, ESP map, thermochemical parameters, and theoretical IR spectrum was employed to study the reactivity descriptors of 9i and 9a as the most and least potent compounds respectively. Based on the DFT study, compound 9i was softer and, as a result, more reactive than 9a. Taken together, our results showed the potential of 4-aminopiperidin-3,4-dihyroquinazoline-2-uracil derivatives as promising candidates for developing some novel DPP4 inhibitors for managing of type 2 diabetes. Uracil ; Computer science ; Combinatorial chemistry ; Computational biology ; Chemistry ; Bioinformatics ; Biochemistry ; Biology ; DNA Quinazolinone synthesis and applications ; Computational Drug Discovery Methods ; Click Chemistry and Applications
Authors: Hongxiang Chen | Jiantao Fu | Shuxian Huang | Yiding Qiu | Enhui Zhao | Shiyu Li | Jianeng Huang | Pinqiang Dai | Hengzhong Fan | Bing Xiao
Source: Advanced Materials (article, 2024-03-29)
Field: Materials Science — Materials Chemistry

Realization of Fine‐Tuning the Lattice Thermal Conductivity and Anharmonicity in Layered Semiconductors via Entropy Engineering Entropy engineering is widely proven to be effective in achieving ultra-low thermal conductivity for well-performed thermoelectric and heat management applications. However, no strong correlation between entropy and lattice thermal conductivity is found until now, and the fine-tuning of thermal conductivity continuously via entropy-engineering in a wide entropy range is still lacking. Here, a series of high-entropy layered semiconductors, Ni<sub>1-</sub> <sub>x</sub>(Fe<sub>0.25</sub>Co<sub>0.25</sub>Mn<sub>0.25</sub>Zn<sub>0.25</sub>)<sub>x</sub>PS<sub>3</sub>, where 0 ≤ x < 1, with low mass/size disorder is designed. High-purity samples with mixing configuration entropy of metal atomic site in a wide range of 0-1.61R are achieved. Umklapp phonon-phonon scattering is found to be the dominating phonon scattering mechanism, as revealed by the linear T<sup>-1</sup> dependence of thermal conductivity. Meanwhile, fine tuning of the lattice thermal conductivity via continuous entropy engineering at metal atomic sites is achieved, in an almost linear dependence in middle-/high- entropy range. Moreover, the slope of the κ - T<sup>-1</sup> curve reduces with the increase in entropy, and a linear response of the reduced Grüneisen parameter is revealed. This work provides an entropy engineering strategy by choosing multiple metal elements with low mass/size disorder to achieve the fine tuning of the lattice thermal conductivity and the anharmonic effect. Thermal conductivity ; Anharmonicity ; Materials science ; Phonon ; Condensed matter physics ; Thermal conduction ; Semiconductor ; Thermoelectric materials ; Thermodynamics ; Phonon scattering ; Scattering ; Physics ; Optoelectronics ; Quantum mechanics ; Composite material Advanced Thermoelectric Materials and Devices ; Thermal properties of materials ; Thermal Radiation and Cooling Technologies
Authors: David N. Breslauer
Source: Advanced Functional Materials (article, 2024-08-27)
Field: Materials Science — Biomaterials

Current Progress on Scale‐Up and Commercialization of Microbially‐Produced Silk Abstract Over the past two decades, significant advancements have been made in the scalable production and commercialization of microbially‐produced recombinant protein polymers. This perspective presents the evolution from early research efforts to the development of market‐ready products, with a focus on recombinant silk‐like proteins. Initial attempts to synthesize spider silk proteins in microbial hosts faced challenges with solubility, stability, and yield. Recent advancements in synthetic biology, protein engineering, and bioprocess development have enabled the substantial progress on these challenges. Early commercial efforts highlight the complexities and high costs involved in silk production and more recent strategies have shifted toward processes with better scalability, techno‐economics, and product properties. Significant commercial progress has been made, with products launched in textiles and personal care. Although market penetration is limited so far, substantial groundwork is laid for future success. Key challenges remain, such as continued high production costs and the need for cost‐effective purification and fiber spinning techniques. However, the convergence of scientific, technological, and market developments –including a growing number of product launches – suggests that recombinant silk and protein polymers can soon become widespread sustainable materials across various industries. Commercialization ; Bioprocess ; Spider silk ; Biochemical engineering ; SILK ; Nanotechnology ; New product development ; Biotechnology ; Materials science ; Engineering ; Business ; Biology ; Marketing Silk-based biomaterials and applications ; Biochemical and Structural Characterization ; Antimicrobial Peptides and Activities
Authors: Runhu Xiao | Zhihao Guan | Dushuo Feng | Changsheng Song
Source: Journal of Applied Physics (article, 2024-01-22)
Field: Materials Science — Materials Chemistry

Strain-tunable ferromagnetism and skyrmions in two-dimensional Janus Cr2XYTe6 (X, Y = Si, Ge, Sn, and X≠Y) monolayers By using first-principles calculations and micromagnetic simulations, we investigate the electronic structure, magnetic properties, and skyrmions in two-dimensional Janus Cr2XYTe6 (X,Y = Si, Ge, Sn, X ≠ Y) monolayers. Our findings reveal that the Cr2XYTe6 monolayers are ferromagnetic semiconductors with a high Curie temperature (Tc). The bandgap and Tc can be further increased by applying tensile strain. In addition, there is a transition from the ferromagnetic to the antiferromagnetic state at a compressive strain. Both Cr2SiSnTe6 and Cr2SiGeTe6 exhibit a large magnetic anisotropy energy, which are mainly associated with the significant spin–orbit coupling of the nonmagnetic Te atoms rather than that of the magnetic Cr atoms. Interestingly, the Cr2SiSnTe6 monolayer exhibits a significant Dzyaloshinskii–Moriya interaction of 1.12 meV, which facilitates the formation of chiral domain walls and skyrmions. Furthermore, under tensile strain, chiral DWs can be transformed into skyrmions if applying an external magnetic field. These findings suggest that Janus Cr2XYTe6 monolayers hold promise for spintronic nanodevice applications. Condensed matter physics ; Ferromagnetism ; Spintronics ; Materials science ; Skyrmion ; Monolayer ; Antiferromagnetism ; Curie temperature ; Janus ; Nanotechnology ; Physics 2D Materials and Applications ; MXene and MAX Phase Materials ; Heusler alloys: electronic and magnetic properties
Authors: Giuseppe Longobardi | Thomas L. Moore | Claudia Conte | Francesca Ungaro | Ronit Satchi‐Fainaro | Fabiana Quaglia
Source: Wiley Interdisciplinary Reviews Nanomedicine and Nanobiotechnology (review, 2024-09-01)
Field: Materials Science — Biomaterials

Polyester nanoparticles delivering chemotherapeutics: Learning from the past and looking to the future to enhance their clinical impact in tumor therapy Polymeric nanoparticles (NPs), specifically those comprised of biodegradable and biocompatible polyesters, have been heralded as a game-changing drug delivery platform. In fact, poly(α-hydroxy acids) such as polylactide (PLA), poly(lactide-co-glycolide) (PLGA), and poly(ε-caprolactone) (PCL) have been heavily researched in the past three decades as the material basis of polymeric NPs for drug delivery applications. As materials, these polymers have found success in resorbable sutures, biodegradable implants, and even monolithic, biodegradable platforms for sustained release of therapeutics (e.g., proteins and small molecules) and diagnostics. Few fields have gained more attention in drug delivery through polymeric NPs than cancer therapy. However, the clinical translational of polymeric nanomedicines for treating solid tumors has not been congruent with the fervor or funding in this particular field of research. Here, we attempt to provide a comprehensive snapshot of polyester NPs in the context of chemotherapeutic delivery. This includes a preliminary exploration of the polymeric nanomedicine in the cancer research space. We examine the various processes for producing polyester NPs, including methods for surface-functionalization, and related challenges. After a detailed overview of the multiple factors involved with the delivery of NPs to solid tumors, the crosstalk between particle design and interactions with biological systems is discussed. Finally, we report state-of-the-art approaches toward effective delivery of NPs to tumors, aiming at identifying new research areas and re-evaluating the reasons why some research avenues have underdelivered. We hope our effort will contribute to a better understanding of the gap to fill and delineate the future research work needed to bring polyester-based NPs closer to clinical application. This article is categorized under: Therapeutic Approaches and Drug Discovery > Nanomedicine for Oncologic Disease Nanotechnology Approaches to Biology > Nanoscale Systems in Biology Therapeutic Approaches and Drug Discovery > Emerging Technologies. Nanomedicine ; Nanotechnology ; Drug delivery ; Polyester ; Materials science ; Biocompatible material ; PLGA ; Biodegradable polymer ; Context (archaeology) ; Cancer therapy ; Surface modification ; Nanoparticle ; Polymer ; Biomedical engineering ; Cancer ; Medicine ; Chemistry Nanoparticle-Based Drug Delivery ; Nanoplatforms for cancer theranostics ; Graphene and Nanomaterials Applications
Authors: Anoop Kishore Vatti | Srikanth Divi | Poulumi Dey
Source: The Journal of Chemical Physics (article, 2024-03-07)
Field: Chemistry — Analytical Chemistry

Effectiveness of inhibitors to prevent asphaltene aggregation: Insights from atomistic and molecular simulations The technological landscape for industrial processes handling asphaltene is evolving at a rapid pace due to the increase in the extraction of heavy crude oil. The main underlying challenges in this regard are the flow assurance, the recovery of the spent solvent, and the sophisticated extractor setup required to develop the process to an industrial scale. The number of studies focused on the handling of the asphaltene at the atomic and molecular scales is growing enormously in order to identify new sustainable solvents for the effective extraction of asphaltene from heavy crude oil or oil-bearing sands. This Perspective focuses on the importance of density functional theory and molecular dynamics simulations to explore the broader range of asphaltene inhibitors, e.g., nanoparticles, ionic liquids, and deep eutectic solvents, to prevent asphaltene precipitation. We provide a concise overview of the major accomplishments, analyze the aspects that require attention, and highlight the path-breaking studies having a significant impact on the process of chemical enhanced oil recovery from heavy crude oil reservoirs primarily based on atomistic and molecular simulations. Asphaltene ; Molecular dynamics ; Petroleum industry ; Extraction (chemistry) ; Precipitation ; Lead (geology) ; Environmental science ; Materials science ; Petroleum engineering ; Process engineering ; Chemistry ; Organic chemistry ; Computational chemistry ; Geology ; Engineering ; Environmental engineering Petroleum Processing and Analysis ; Enhanced Oil Recovery Techniques ; Hydrocarbon exploration and reservoir analysis
Authors: Anamaria Apan | Dorina Casoni | Denisa Leonte | Cristina Pop | Irina Iaru | Cristina Mogoșan | Valentin Zaharia
Source: Pharmaceuticals (article, 2024-02-25)
Field: Chemistry — Organic Chemistry

Heterocycles 52: The Drug-Likeness Analysis of Anti-Inflammatory Thiazolo[3,2-b][1,2,4]triazole and Imidazo[2,1-b][1,3,4]thiadiazole Derivatives Lipophilicity, a significant physicochemical parameter of bioactive molecules, along with absorption, distribution, metabolism, excretion parameters and toxicity risk, was investigated for 32 thiazolo[3,2-b][1,2,4]triazole and imidazo[2,1-b][1,3,4]thiadiazole derivatives with anti-inflammatory potential. The experimental lipophilicity study was carried out by reversed-phase thin-layer chromatography in a binary isopropanol-water mobile phase, and the obtained results were compared with the theoretical lipophilicity parameters estimated by various computational methods. Strong correlations were found between the experimental retention factors and calculated partition coefficients. A modified Petra/Osiris/Molinspiration analysis was performed on the previously synthesized compounds, using SwissADME, Osiris and Molinspiration web tools. The predicted in silico parameters highlighted the most promising compounds as potential drug candidates. The compounds showed good gastrointestinal absorption, moderate activity according to the bioactivity score (values situated between -1.25 and -0.06), and a safe toxicity profile. The results obtained in this study will contribute to lipophilicity studies and other future studies focused on modulating new drug candidates starting from thiazolo[3,2-b][1,2,4]triazole and imidazo[2,1-b][1,3,4]thiadiazole derivatives, which are important heterocycles in medicinal chemistry. Lipophilicity ; Chemistry ; Triazole ; Partition coefficient ; In silico ; ADME ; Absorption (acoustics) ; 1,2,4-Triazole ; Stereochemistry ; Combinatorial chemistry ; Chromatography ; Organic chemistry ; In vitro ; Biochemistry ; Materials science Synthesis and biological activity ; Computational Drug Discovery Methods ; Click Chemistry and Applications