jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yongfan Zhu | Jia Liu | Zhengkun Liu | Gongping Liu | Wanqin Jin
Source: Materials (article, 2024-09-23)
Field: Materials Science — Materials Chemistry

Design of CO2-Resistant High-Entropy Perovskites Based on Ba0.5Sr0.5Co0.8Fe0.2O3-δ Materials High-entropy perovskite materials (HEPMs), characterized by their multi-element composition and highly disordered structure, can incorporate multiple rare earth elements at the A-site, producing perovskites with enhanced CO<sub>2</sub> resistance, making them stay high performance and structurally stable in the CO<sub>2</sub> atmosphere. However, this modification may result in reduced oxygen permeability. In this study, we investigated La<sub>0.2</sub>Pr<sub>0.2</sub>Nd<sub>0.2</sub>Ba<sub>0.2</sub>Sr<sub>0.2</sub>Co<sub>0.8</sub>Fe<sub>0.2</sub>O<sub>3-δ</sub> (L<sub>0.2</sub>M<sub>1.8</sub>) high-entropy perovskite materials, focusing on enhancing their oxygen permeability in both air and CO<sub>2</sub> atmospheres through strategic design modifications at the B-sites and A/B-sites. We prepared Ni-substituted La<sub>0.2</sub>Pr<sub>0.2</sub>Nd<sub>0.2</sub>Ba<sub>0.2</sub>Sr<sub>0.2</sub>Co<sub>0.7</sub>Fe<sub>0.2</sub>Ni<sub>0.1</sub>O<sub>3-δ</sub> (L<sub>0.2</sub>M<sub>1.7</sub>N<sub>0.1</sub>) HEPMs by introducing Ni elements at the B-site, and further innovatively introduced A-site defects to prepare La<sub>0.2</sub>Pr<sub>0.2</sub>Nd<sub>0.2</sub>Ba<sub>0.2</sub>Sr<sub>0.2</sub>Co<sub>0.7</sub>Fe<sub>0.2</sub>Ni<sub>0.1</sub>O<sub>3-δ</sub> (L<sub>0.1</sub>M<sub>1.7</sub>N<sub>0.1</sub>) materials. In a pure CO<sub>2</sub> atmosphere, the oxygen permeation flux of the L<sub>0.1</sub>M<sub>1.7</sub>N<sub>0.1</sub> membrane can reach 0.29 mL·cm<sup>-2</sup>·min<sup>-1</sup>. Notably, the L<sub>0.1</sub>M<sub>1.7</sub>N<sub>0.1</sub> membrane maintained a good perovskite structure after stability tests extending up to 120 h under 20% CO<sub>2</sub>/80% He atmosphere. These findings suggest that A-site-defect high-entropy perovskites hold great promise for applications in CO<sub>2</sub> capture, storage, and utilization. Materials science ; Perovskite (structure) ; Chemical engineering ; Thermodynamics ; Physics ; Engineering Advancements in Solid Oxide Fuel Cells ; Magnetic and transport properties of perovskites and related materials ; Electronic and Structural Properties of Oxides
Authors: Wook Shin | Yaning Hou | Xin Wang | Zhongyue Yang
Source: Journal of Chemical Theory and Computation (article, 2024-12-06)
Field: Chemistry — Organic Chemistry

Interplay between Energy and Entropy Mediates Ambimodal Selectivity of Cycloadditions One ambimodal transition state can lead to the formation of multiple products. However, it remains fundamentally unknown how the energy and entropy along the post-TS pathways mediate ambimodal selectivity. Here, we investigated the energy and entropy profiles along the post-TS pathways in four [4 + 2]/[6 + 4] cycloadditions. We observe that the pathway leading to the minor product involves a more pronounced entropic trap. These entropic traps, resulting from the conformational change in the dynamic course of ring closure, act as a reservoir of longer-lived dynamic intermediates that roam on the potential energy surface and have a higher likelihood of redistributing to form the other product. The SpnF-catalyzed Diels-Alder reaction produces [4 + 2] and [6 + 4] adducts with nearly equal product distribution and relatively flat energy profiles, in contrast to other cycloadditions. Unexpectedly, the entropy profiles for these two adducts are distinctly different. The formation of the [6 + 4] adduct encounters an entropic barrier acting as a dynamical bottleneck, while the [4 + 2] adduct involves a substantial entropic trap to maintain long-lived intermediates. These opposing effects hinder both product formations and likely cancel each other out so that an equal product distribution is observed. Selectivity ; Entropy (arrow of time) ; Computer science ; Chemistry ; Statistical physics ; Physics ; Thermodynamics ; Biochemistry Organic Chemistry Cycloaddition Reactions ; Advanced Chemical Physics Studies ; Molecular Junctions and Nanostructures
Authors: Ahmed H. Mokhtar | David Serban | D. G. Porter | G. Nisbet | Stephen P. Collins | A. Bombardi | Mary Newton
Source: npj Computational Materials (article, 2024-05-07)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Imaging and ferroelectric orientation mapping of photostriction in a single Bismuth Ferrite nanocrystal Abstract The exploration of multiferroic materials and their interaction with light at the nanoscale presents a captivating frontier in materials science. Bismuth Ferrite (BiFeO 3 , BFO), a standout among these materials, exhibits room-temperature ferroelectric and antiferromagnetic behaviour and magnetoelectric coupling. Of particular interest is the phenomenon of photostriction, the light-induced deformation of crystal structures, which enhances the prospect for device functionality based on these materials. Understanding and harnessing multiferroic phenomena holds significant promise in various technological applications, from optoelectronics to energy storage. The orientation of the ferroelectric axis is an important design parameter for devices formed from multiferroic materials. Determining its orientation in the laboratory frame of reference usually requires knowing multiple wavevector transfer (Q-Vector) directions, which can be challenging to establish due to the need for extensive reciprocal-space searches. Our study demonstrates a method to identify the ferroelectric axis orientation using Bragg Coherent X-ray Diffraction Imaging (BCDI) measurements at a single Q-vector direction. This method involves applying photostriction-inducing laser illumination across various laser polarisations. Our findings reveal that photostriction primarily occurs as a surface phenomenon at the nanoscale. Moreover, a photo-induced crystal length change ranging from 30 to 60 nm was observed, consistent with earlier findings on bulk material. Nanocrystal ; Bismuth ferrite ; Ferroelectricity ; Materials science ; Bismuth ; Ferrite (magnet) ; Nanotechnology ; Optoelectronics ; Metallurgy ; Composite material ; Multiferroics ; Dielectric Multiferroics and related materials ; Magneto-Optical Properties and Applications ; Magnetic Properties and Synthesis of Ferrites
Authors: Ramakrishnan Kulasekaran Sathish Kumar | R. Sasikumar | N. Nagaprasad | Rathinam Ezhilvannan | Krishnaraj Ramaswamy
Source: Advances in Materials Science and Engineering (article, 2024-01-13)
Field: Materials Science — Polymers and Plastics

Investigation of Mechanical and Thermal Stabilities of Tamarind Seed- and Peanut Shell Powder-Reinforced Vinyl Ester Composite Efficient exploitation of agricultural waste results in a more sustainable and ecofriendly environment since it lessens the burden of their disposal, which has become increasingly important in recent times. Due to their high mechanical strength and high thermal stability, these biodegradable low-value agrosolid wastes have the potential to successfully replace synthetic fibers and fillers in polymer matrices in the form of reinforcements. This work deals with the addition of low-cost and renewable hybrid natural fillers, tamarind seed filler (TMS), and peanut shell powder (PNS) as particulate reinforcements to the vinyl ester (VE) resin. Traditional compression molding creates TMS/PNS-VE hybrid composites with filler loadings ranging from 5% to 30%. After the composites were fabricated, they were tested for strength properties and heat deflection temperature. A detailed experimental analysis of the mechanical properties was conducted. According to the findings, 20 wt.% hybrid filler loading to the vinyl ester polymer exhibited peak tensile, flexural, and impact strengths of 40.3 MPa, 142 MPa, and 16 kJ/m2, respectively, which is 1.52, 1.69, and 1.29 times the properties of the virgin polymer. However, the peak elongation at break 3.9% was obtained at 30 wt.%. Similarly, the heat deflection temperature (HDT) test of TMS/PNS-VE composites showed a maximum rise of 50.91% at 25 wt.% of filler loading. This is 1.51 times greater than the heat deflection temperature of the pure vinyl ester resin. The findings made it quite clear that adding 20 wt.% biosolid waste hybrid particulate fillers made out of tamarind seed and peanut shell to vinyl ester is the optimum weight, which improves the mechanical and thermal properties of the TMS/PNS-VE composite, making it suitable for making cost-effective materials for lightweight applications. This study also utilizes scanning electron microscopy (SEM) to investigate the microstructural characteristics of the composites, correlating these features with their mechanical performance. Materials science ; Vinyl ester ; Heat deflection temperature ; Composite material ; Ultimate tensile strength ; Flexural strength ; Composite number ; Compression molding ; Thermal stability ; Filler (materials) ; Izod impact strength test ; Polymer ; Monomer ; Mold ; Chemical engineering Natural Fiber Reinforced Composites ; Mechanical Engineering and Vibrations Research ; Structural Behavior of Reinforced Concrete
Authors: Thomas D. Turner | Cai Y. | Yuosef Al Ayoub | Radoslav Y. Penchev | Neil Dawson | Martyn D. Ticehurst | Robert Docherty | Kevin J. Roberts
Source: Powder Technology (article, 2024-09-11)
Field: Materials Science — Materials Chemistry

Calculating the surface energies of crystals on a face-specific and whole particle basis: Case study of the α- and β-polymorphic forms of L-glutamic acid Molecular-scale modelling for predicting surface energies on a face-specific and whole particle basis is applied to all the crystallographically-independent surfaces of L-glutamic acid forms. The predicted data is found to be in good general agreement with measured surface energies using inverse gas chromatography and Washburn capillary rise techniques with the former revealing higher values compared to the prediction, perhaps consistent with the polar (zwitterionic) nature of this material. This fusion of experimental and computational data provides a high-fidelity definition of the face-by-face breakdown of the energetic anisotropy of the crystals. There is increasing industrial interest in defining the potential impact of whole particle properties on the performance of formulated drug product and their manufacturability especially as the community accelerates the molecule to medicine journey. The overall molecular modelling approach highlights its application in designing ingredients for optimising face-specific particle surface energies for product formulatability particularly in early phase process development. A route map from molecular and crystallographic structure to surface energy calculation with combined experimental workflow. • A methodology fusing experimental and computational workflows for the investigation of crystal surface energy has been presented. • Molecular-scale (synthonic) modelling used to predict the surface energies of α and β forms of L-glutamic acid on a face-specific and whole particle basis. • Predicted surface energies are in good general agreement with measurements using IGC and Washburn capillary rise techniques. • Calculated surface energies have the same trend as those from experiments though they are higher than the experimental values, which are consistent with literature findings. • The overall molecular modelling approach demonstrates its application in designing crystal habit for optimising particle surface energies for product formulatability particularly in early phase process development. Particle (ecology) ; Basis (linear algebra) ; Face (sociological concept) ; Glutamic acid ; Surface (topology) ; Chemistry ; Crystallography ; Materials science ; Chemical physics ; Biology ; Biochemistry ; Geometry ; Mathematics ; Amino acid ; Philosophy ; Ecology Crystallization and Solubility Studies ; Chemical Thermodynamics and Molecular Structure ; Crystallography and molecular interactions
Authors: Andreas Møller Slavensky | Bjørk Hammer
Source: The Journal of Chemical Physics (article, 2024-07-03)
Field: Materials Science — Materials Chemistry

Accelerating structure search using atomistic graph-based classifiers We introduce an atomistic classifier based on a combination of spectral graph theory and a Voronoi tessellation method. This classifier allows for the discrimination between structures from different minima of a potential energy surface, making it a useful tool for sorting through large datasets of atomic systems. We incorporate the classifier as a filtering method in the Global Optimization with First-principles Energy Expressions (GOFEE) algorithm. Here, it is used to filter out structures from exploited regions of the potential energy landscape, whereby the risk of stagnation during the searches is lowered. We demonstrate the usefulness of the classifier by solving the global optimization problem of two-dimensional pyroxene, three-dimensional olivine, Au12, and Lennard-Jones LJ55 and LJ75 nanoparticles. Maxima and minima ; Classifier (UML) ; Voronoi diagram ; Computer science ; Artificial intelligence ; Graph ; Pattern recognition (psychology) ; Algorithm ; Mathematics ; Theoretical computer science ; Geometry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Surface Chemistry and Catalysis
Authors: Zhenan Chen | Xiaoming Xue | Haoqi Wu | Handong Gao | Guangyu Wang | Geyi Ni | Tianyi Cao
Source: Frontiers in Plant Science (article, 2024-05-31)
Field: Chemistry — Analytical Chemistry

Visible/near-infrared hyperspectral imaging combined with machine learning for identification of ten Dalbergia species These findings underscore the effectiveness of Vis/NIR HSI in wood species identification. They also highlight the importance of precise wavelength selection and preprocessing techniques to maximize both accuracy and cost-efficiency. This research contributes substantially to ecological conservation and the regulation of the timber trade by providing a reliable, non-destructive method for identifying threatened wood species. Hyperspectral imaging ; Infrared ; Identification (biology) ; Remote sensing ; Computer science ; Botany ; Artificial intelligence ; Environmental science ; Biology ; Optics ; Physics ; Geography Spectroscopy and Chemometric Analyses ; Identification and Quantification in Food ; Wood and Agarwood Research
Authors: Giulia Mirra | Lorenzo Cursi | Luca Boselli | Pier Paolo Pompa
Source: Small Science (article, 2024-12-06)
Field: Materials Science — Materials Chemistry

Screening Oxidoreductase Activities of Nanozymes: Toward Activity‐Tailored Guidelines Nanozymes (NZs) are nanomaterials able to mimic natural enzymes, offering the advantages of better durability, wider range of operational conditions, and easier functionalization. In addition, some NZs exhibit multiple enzyme‐like activities and effective tandem/self‐cascade reactions. In this context, metallic and metal–oxide nanoparticles are among the most promising candidates for biomedical/biotechnological applications, thanks to their efficient oxidoreductase activities. However, characterizing NZs is challenging because the experimental set‐up of different assays strongly influence their performances. Consequently, currently available literature provides limited understanding of the characteristics of the various NZs, especially in terms of activity‐related performance, hindering their optimal selection and applicative potential. Here, leveraging on accurate characterization methodology, we report a direct comparison of several NZs (gold‐Au, platinum‐Pt, palladium‐Pd, ceria, iron oxide) possessing multi‐enzymatic activities. Our main findings indicate that 1) PtNZs outperform the other tested NZs in catalase‐like activity; 2) Pt and PdNZs present superior superoxide dismutase‐like activity; 3) for peroxidase‐ and oxidase‐like activity, the best‐performing NZ is substrate‐dependent; 4) competitive reactions in multifunctional NZs play a key role in their specific performance, and AuNZs can be the optimal choice in some peroxidase/oxidase configurations; 5) metallic NZs are more efficient than metal‐oxide ones, but 6) ceria presents unique phosphatase‐like activity. Context (archaeology) ; Chemistry ; Computer science ; Combinatorial chemistry ; Substrate (aquarium) ; Biochemical engineering ; Oxide ; Materials science ; Nanotechnology ; Engineering ; Organic chemistry ; Biology Advanced Nanomaterials in Catalysis ; Nanocluster Synthesis and Applications ; Nanoparticles: synthesis and applications
Authors: Farook Sattar
Source:  (article, 2024-03-11)
Field: Chemistry — Inorganic Chemistry

Predicting Maturity of Coconut Fruit from Acoustic Signal with Applications of Deep Learning This paper aims to develop an effective AI-driven method to predict the maturity level of coconut (Cocos nucifera) fruits using acoustic signals. The proposed sound-based autonomous approach exploits various deep learning models, including customized CNN pretrained networks, i.e., the ResNet50, InceptionV3, and MobileNetV2, models for maturity level classification of the coconuts. The proposed study also demonstrates the effectiveness of various deep learning models to automatically predict the maturity of coconuts into three classes, i.e., premature, mature, and overmature coconuts, for inspecting the coconut fruits by using a small amount of input acoustic data. We use an open-access dataset containing a total of 122 raw acoustic signals, which is the result of knocking 122 coconut samples. The results achieved by the proposed method for coconut maturity prediction are found to be promising, which enables producers to accurately determine the yield and product quality. Maturity (psychological) ; SIGNAL (programming language) ; Computer science ; Deep learning ; Artificial intelligence ; Machine learning ; Agricultural engineering ; Engineering ; Psychology Coconut Research and Applications ; Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies
Authors: Ralf Wanzenböck | Esther Heid | Michele Riva | Giada Franceschi | Alexander M. Imre | Jesús Carrete | Ulrike Diebold | Georg K. H. Madsen
Source: Digital Discovery (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Exploring inhomogeneous surfaces: Ti-rich SrTiO<sub>3</sub>(110) reconstructions <i>via</i> active learning The investigation of inhomogeneous surfaces, where various local structures coexist, is crucial for understanding interfaces of technological interest, yet it presents significant challenges. Here, we study the atomic configurations of the (2 × <i>m</i>) Ti-rich surfaces at (110)-oriented SrTiO<sub>3</sub> by bringing together scanning tunneling microscopy and transferable neural-network force fields combined with evolutionary exploration. We leverage an active learning methodology to iteratively extend the training data as needed for different configurations. Training on only small well-known reconstructions, we are able to extrapolate to the complicated and diverse overlayers encountered in different regions of the inhomogeneous SrTiO<sub>3</sub>(110)-(2 × <i>m</i>) surface. Our machine-learning-backed approach generates several new candidate structures, in good agreement with experiment and verified using density functional theory. The approach could be extended to other complex metal oxides featuring large coexisting surface reconstructions. Materials science Electronic and Structural Properties of Oxides ; Ferroelectric and Negative Capacitance Devices ; Machine Learning in Materials Science
Authors: Trupti Mohanty | Maitrey Mehta | Hasan M. Sayeed | Vivek Srikumar | Taylor D. Sparks
Source: ChemRxiv (preprint, 2024-12-10)
Field: Materials Science — Materials Chemistry

CrysText: A Generative AI Approach for Text-Conditioned Crystal Structure Generation using LLM Generating crystal structures directly from textual descriptions marks a pivotal advancement in materials informatics, offering a streamlined pathway from concept to discovery. Integrating generative models into Crystal Structure Prediction (CSP) presents a transformative opportunity to enhance efficiency and innovation. While large language models (LLMs) excel at understanding and generating text, their potential in materials discovery remains largely unexplored. Here, we introduce CrysText, an advanced approach for generating crystal structures from simple text prompts, conditioned on material composition and space group number. Leveraging Llama-3.1- 8B fine-tuned with Quantized Low-Rank Adaptation (QLoRA), our approach enables the efficient and scalable generation of CIF-formatted structures directly from input descriptions, eliminating the need for post-processing and ensuring effective fine-tuning with rapid inference. Evaluations on the MP-20 benchmark dataset demonstrate high structure match rates and effective RMSE metrics, showcasing the framework's ability to generate crystal structures that faithfully adhere to specified compositions and crystal symmetries. By conditioning on energy above the hull, we further demonstrate the potential of CrysText to generate stable crystal structures. Our work highlights the transformative role of LLMs in text-prompted inverse design, accelerating the discovery of new materials. Generative grammar ; Computer science ; Transformative learning ; Benchmark (surveying) ; Generative model ; Crystal (programming language) ; Inference ; Rank (graph theory) ; Artificial intelligence ; Theoretical computer science ; Machine learning ; Mathematics ; Programming language ; Geography ; Sociology Machine Learning in Materials Science ; X-ray Diffraction in Crystallography