jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Maria Wątroba | Wiktor Bednarczyk | Piotr Szewczyk | Jakub Kawałko | Krzysztof Mech | Alina Grünewald | Irem Unalan | Nicola Taccardi | Gabriela Boelter | Manuel Banzhaf | Caroline Hain | Piotr Bała | Aldo R. Boccaccini
Source: Journal of Biomedical Materials Research Part B Applied Biomaterials (article, 2022-08-26)
Field: Materials Science — Materials Chemistry

In vitro cytocompatibility and antibacterial studies on biodegradable Zn alloys supplemented by a critical assessment of direct contact cytotoxicity assay In vitro cytotoxicity assessment is indispensable in developing new biodegradable implant materials. Zn, which demonstrates an ideal corrosion rate between Mg- and Fe-based alloys, has been reported to have excellent in vivo biocompatibility. Therefore, modifications aimed at improving Zn's mechanical properties should not degrade its biological response. As sufficient strength, ductility and corrosion behavior required of load-bearing implants has been obtained in plastically deformed Zn-3Ag-0.5Mg, the effect of simultaneous Ag and Mg additions on in vitro cytocompatibility and antibacterial properties was studied, in relation to Zn and Zn-3Ag. Direct cell culture on samples and indirect extract-based tests showed almost no significant differences between the tested Zn-based materials. The diluted extracts of Zn, Zn-3Ag, and Zn-3Ag-0.5Mg showed no cytotoxicity toward MG-63 cells at a concentration of ≤12.5%. The cytotoxic effect was observed only at high Zn<sup>2+</sup> ion concentrations and when in direct contact with metallic samples. The highest LD<sub>50</sub> (lethal dose killing 50% of cells) of 13.4 mg/L of Zn<sup>2+</sup> ions were determined for the Zn-3Ag-0.5Mg. Similar antibacterial activity against Escherichia coli and Staphylococcus aureus was observed for Zn and Zn alloys, so the effect is attributed mainly to the released Zn<sup>2+</sup> ions exhibiting bactericidal properties. Most importantly, our experiments indicated the limitations of water-soluble tetrazolium salt-based cytotoxicity assays for direct tests on Zn-based materials. The discrepancies between the WST-8 assay and SEM observations are attributed to the interference of Zn<sup>2+</sup> ions with tetrazolium salt, therefore favoring its transformation into formazan, giving false cell viability quantitative results. Cytotoxicity ; In vitro ; Chemistry ; Antibacterial activity ; Nuclear chemistry ; Microbiology ; Combinatorial chemistry ; Materials science ; Bacteria ; Biochemistry ; Biology Nanoparticles: synthesis and applications ; Bone Tissue Engineering Materials ; Orthopaedic implants and arthroplasty
Authors: Diego R. Abujetas | Jorge Olmos‐Trigo | José A. Sánchez‐Gil
Source: Advanced Optical Materials (article, 2022-05-22)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Tailoring Accidental Double Bound States in the Continuum in All‐Dielectric Metasurfaces Abstract Bound states in the continuum (BICs) have been thoroughly investigated due to their formally divergent Q‐factor, especially those emerging in all‐dielectric, nanostructured metasurfaces from symmetry protection at the Γ point (in‐plane wavevector k || = 0). Less attention has been paid to accidental BICs that may appear at any other in the band structure of supported modes, being in turn difficult to predict. Here, a coupled electric/magnetic dipole model is used to determine analytical conditions for the emergence of accidental BICs, valid for any planar array of meta‐atoms that can be described by dipolar resonances, which is the case of many nanostructures in the optical domain. This is explored for all‐dielectric nanospheres through explicit analytical conditions that allow in turn to predict accidental BIC positions in the parameter space (ω, k || ). Finally, such conditions are exploited to determine not only single, but also double (for both linear polarizations) accidental BICs occurring at the same position in the dispersion relation ω − k || for realistic semiconductor nanodisk meta‐atoms. This might pave the way to a variety of BIC‐enhanced light–matter interaction phenomena at the nanoscale such as lasing or nonlinear conversion, that benefit from emerging at wavevectors away from the Γ point (off‐normal incidence) overlapping for both linear polarizations. Dielectric ; Dipole ; Semiconductor ; Condensed matter physics ; Bound state ; Materials science ; Physics ; Optoelectronics ; Quantum mechanics Metamaterials and Metasurfaces Applications ; Plasmonic and Surface Plasmon Research ; Photonic Crystals and Applications
Authors: David Romero-Fierro | Moisés Bustamante-Torres | Francisco Bravo-Plascencia | Héctor Magaña | Emilio Bucio
Source: Polymers (review, 2022-06-17)
Field: Materials Science — Polymers and Plastics

Polymer-Magnetic Semiconductor Nanocomposites for Industrial Electronic Applications Nanocomposite materials have acquired great importance, as have similar composite materials on a macroscopic scale, because the reinforcement complements the defects in the properties of the matrix, thus obtaining materials with better mechanical, thermal, and electrical properties, among others. At the same time, the importance and research of polymeric nanocomposites reinforced with nanoparticles of various types have grown. Among those that have stood out the most in the electronics industry are polymeric matrices reinforced with nanoparticles that present dual behavior, that is, both magnetic and semiconductor. This property has been very well used in developing electronic devices such as televisions, computers, and smartphones, which are part of everyday life. In this sense, this review presents a compilation of the synthetic methods to produce polymer nanocomposites with dual magnetic and semiconductor behavior and their potential applications within electronic fields and new relevant trends. Nanocomposite ; Materials science ; Electronics ; Semiconductor ; Nanotechnology ; Polymer ; Nanoparticle ; Polymer nanocomposite ; Composite material ; Composite number ; Dual (grammatical number) ; Optoelectronics ; Electrical engineering ; Engineering Conducting polymers and applications ; Polymer Nanocomposite Synthesis and Irradiation ; Dielectric materials and actuators
Authors: Zijie Li | Kazem Meidani | Prakarsh Yadav | Amir Barati Farimani
Source: The Journal of Chemical Physics (article, 2022-04-13)
Field: Materials Science — Materials Chemistry

Graph neural networks accelerated molecular dynamics Molecular Dynamics (MD) simulation is a powerful tool for understanding the dynamics and structure of matter. Since the resolution of MD is atomic-scale, achieving long timescale simulations with femtosecond integration is very expensive. In each MD step, numerous iterative computations are performed to calculate energy based on different types of interaction and their corresponding spatial gradients. These repetitive computations can be learned and surrogated by a deep learning model, such as a Graph Neural Network (GNN). In this work, we developed a GNN Accelerated MD (GAMD) model that directly predicts forces, given the state of the system (atom positions, atom types), bypassing the evaluation of potential energy. By training the GNN on a variety of data sources (simulation data derived from classical MD and density functional theory), we show that GAMD can predict the dynamics of two typical molecular systems, Lennard-Jones system and water system, in the NVT ensemble with velocities regulated by a thermostat. We further show that GAMD's learning and inference are agnostic to the scale, where it can scale to much larger systems at test time. We also perform a comprehensive benchmark test comparing our implementation of GAMD to production-level MD software, showing GAMD's competitive performance on the large-scale simulation. Computer science ; Benchmark (surveying) ; Molecular dynamics ; Computation ; Inference ; Scale (ratio) ; Thermostat ; Artificial neural network ; Graph ; Artificial intelligence ; Statistical physics ; Algorithm ; Theoretical computer science ; Physics ; Chemistry ; Computational chemistry Machine Learning in Materials Science ; Protein Structure and Dynamics ; Fuel Cells and Related Materials
Authors: Evan R. Antoniuk | Peggy Li | Bhavya Kailkhura | Anna M. Hiszpanski
Source: Journal of Chemical Information and Modeling (article, 2022-10-31)
Field: Materials Science — Materials Chemistry

Representing Polymers as Periodic Graphs with Learned Descriptors for Accurate Polymer Property Predictions Accurately predicting new polymers' properties with machine learning models apriori to synthesis has potential to significantly accelerate new polymers' discovery and development. However, accurately and efficiently capturing polymers' complex, periodic structures in machine learning models remains a grand challenge for the polymer cheminformatics community. Specifically, there has yet to be an ideal solution for the problems of how to capture the periodicity of polymers, as well as how to optimally develop polymer descriptors without requiring human-based feature design. In this work, we tackle these problems by utilizing a periodic polymer graph representation that accounts for polymers' periodicity and coupling it with a message-passing neural network that leverages the power of graph deep learning to automatically learn chemically relevant polymer descriptors. Remarkably, this approach achieves state-of-the-art performance on 8 out of 10 distinct polymer property prediction tasks. These results highlight the advancement in predictive capability that is possible through learning descriptors that are specifically optimized for capturing the unique chemical structure of polymers. Cheminformatics ; Polymer ; Computer science ; Artificial intelligence ; Representation (politics) ; Property (philosophy) ; Machine learning ; A priori and a posteriori ; Graph ; Feature (linguistics) ; Deep learning ; Theoretical computer science ; Materials science ; Chemistry ; Computational chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemistry and Chemical Engineering
Authors: Taiwo W. Quadri | Lukman O. Olasunkanmi | Omolola E. Fayemi | Ekemini D. Akpan | Han‐Seung Lee | Hassane Lgaz | Chandrabhan Verma | Lei Guo | Savaş Kaya | Eno E. Ebenso
Source: Computational Materials Science (article, 2022-08-27)
Field: Materials Science — Materials Chemistry

Multilayer perceptron neural network-based QSAR models for the assessment and prediction of corrosion inhibition performances of ionic liquids Quantitative structure–activity relationship ; Artificial neural network ; Molecular descriptor ; Quantum chemical ; Biological system ; Linear regression ; Multilayer perceptron ; Levenberg–Marquardt algorithm ; Mean squared error ; Corrosion ; Perceptron ; Chemistry ; Predictive modelling ; Computer science ; Artificial intelligence ; Machine learning ; Mathematics ; Statistics ; Molecule ; Organic chemistry Corrosion Behavior and Inhibition ; Concrete Corrosion and Durability ; Electrochemical Analysis and Applications
Authors: Peitao Liu | Carla Verdi | Ferenc Karsai | Georg Kresse
Source: Physical review. B./Physical review. B (article, 2022-02-16)
Field: Materials Science — Materials Chemistry

Phase transitions of zirconia: Machine-learned force fields beyond density functional theory Machine-learned force fields (MLFFs) are becoming an increasingly important tool in materials science and physics. However, most MLFFs are constructed based on density functional theory (DFT) calculations, which come with significant limitations. Here, the authors combine an efficient on-the-fly active learning procedure and a ∆-machine learning approach, enabling the generation of MLFFs with an accuracy that exceeds DFT accuracy at a modest computational cost. Using this method, they generated an MLFF for the random phase approximation that allows highly accurate predictions of the phase transition temperatures of zirconia. Density functional theory ; Singular value decomposition ; Random phase approximation ; Computer science ; Kernel (algebra) ; Statistical physics ; Rank (graph theory) ; Phase (matter) ; Time-dependent density functional theory ; Algorithm ; Physics ; Quantum mechanics ; Mathematics Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Protein Structure and Dynamics
Authors: Jean-François Lemineur | Hui Wang | Wei Wang | Frédéric Kanoufi
Source: Annual Review of Analytical Chemistry (article, 2022-02-26)
Field: Chemistry — Electrochemistry

Emerging Optical Microscopy Techniques for Electrochemistry An optical microscope is probably the most intuitive, simple, and commonly used instrument to observe objects and discuss behaviors through images. Although the idea of imaging electrochemical processes operando by optical microscopy was initiated 40 years ago, it was not until significant progress was made in the last two decades in advanced optical microscopy or plasmonics that it could become a mainstream electroanalytical strategy. This review illustrates the potential of different optical microscopies to visualize and quantify local electrochemical processes with unprecedented temporal and spatial resolution (below the diffraction limit), up to the single object level with subnanoparticle or single-molecule sensitivity. Developed through optically and electrochemically active model systems, optical microscopy is now shifting to materials and configurations focused on real-world electrochemical applications. Optical microscope ; Microscopy ; Microscope ; Resolution (logic) ; Scanning electrochemical microscopy ; Diffraction ; Infrared microscopy Electrochemical Analysis and Applications ; Near-Field Optical Microscopy ; Force Microscopy Techniques and Applications
Authors: Jei Vincent | Kam Sheng Lau | Evyan Chia Yan Yang | Siew Xian Chin | Mika Sillanpää | Chin Hua Chia
Source: Nanomaterials (review, 2022-09-23)
Field: Materials Science — Materials Chemistry

Biogenic Synthesis of Copper-Based Nanomaterials Using Plant Extracts and Their Applications: Current and Future Directions Plants have been used for multiple purposes over thousands of years in various applications such as traditional Chinese medicine and Ayurveda. More recently, the special properties of phytochemicals within plant extracts have spurred researchers to pursue interdisciplinary studies uniting nanotechnology and biotechnology. Plant-mediated green synthesis of nanomaterials utilises the phytochemicals in plant extracts to produce nanomaterials. Previous publications have demonstrated that diverse types of nanomaterials can be produced from extracts of numerous plant components. This review aims to cover in detail the use of plant extracts to produce copper (Cu)-based nanomaterials, along with their robust applications. The working principles of plant-mediated Cu-based nanomaterials in biomedical and environmental applications are also addressed. In addition, it discusses potential biotechnological solutions and new applications and research directions concerning plant-mediated Cu-based nanomaterials that are yet to be discovered so as to realise the full potential of the plant-mediated green synthesis of nanomaterials in industrial-scale production and wider applications. This review provides readers with comprehensive information, guidance, and future research directions concerning: (1) plant extraction, (2) plant-mediated synthesis of Cu-based nanomaterials, (3) the applications of plant-mediated Cu-based nanomaterials in biomedical and environmental remediation, and (4) future research directions in this area. Nanomaterials ; Nanotechnology ; Plant species ; Plant growth ; Biochemical engineering ; Computer science ; Engineering ; Materials science ; Biology ; Ecology Nanoparticles: synthesis and applications ; Nanotechnology research and applications ; Medicinal Plants and Neuroprotection
Authors: Akif Safeen | Kashif Safeen | Rehan Ullah | Zulfqar | Wiqar Hussain Shah | Quaid Zaman | Khaled Althubeiti | Sattam Al Otaibi | Nasir Rahman | Shahid Iqbal | Alamzeb Khan | Aurangzeb Khan | Rajwali Khan
Source: RSC Advances (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Enhancing the physical properties and photocatalytic activity of TiO<sub>2</sub> nanoparticles <i>via</i> cobalt doping Cobalt-doped TiO<sub>2</sub>-based diluted magnetic semiconductors were successfully synthesized using a co-precipitation method. The X-ray diffraction study of all the samples showed good crystallinity, matching the standard tetragonal anatase phase. The X-ray diffraction peaks of the cobalt-doped sample slightly shifted towards a lower angle showing the decrease in particle size and distortion in the unit cell due to cobalt incorporation in the lattice of TiO<sub>2</sub>. Transmission electron microscopy showed the spherical morphology of the TiO<sub>2</sub> nanoparticles, which decreased with Co-doping. The optical characteristics and band gap investigation revealed that defects and oxygen vacancies resulted in lower band gap energy and maximum absorption in the visible region. Dielectric measurements showed enhancement in the dielectric constant and AC conductivity, while the dielectric loss decreased. The enhancement in the dielectric properties was attributed to interfacial polarization and charge carrier hopping between Co and Ti ions. The magnetic properties displayed that pure TiO<sub>2</sub> was diamagnetic, while Co-doped TiO<sub>2</sub> showed a ferromagnetic response at 300 K. The visible light-driven photocatalytic activity showed an improvement for Co-doped TiO<sub>2</sub>. Our results demonstrate that Co-doping can be used to tune the physical properties and photocatalytic activity of TiO<sub>2</sub> for possible spin-based electronics, optoelectronics, and photo-degradation applications. Materials science ; Dielectric ; Crystallinity ; Photocatalysis ; Doping ; Band gap ; Cobalt ; Anatase ; Analytical Chemistry (journal) ; Nanoparticle ; Tetragonal crystal system ; Nanotechnology ; Optoelectronics ; Crystallography ; Crystal structure ; Chemistry ; Composite material ZnO doping and properties ; Copper-based nanomaterials and applications ; TiO2 Photocatalysis and Solar Cells
Authors: Mauro Coduri | Maths Karlsson | Lorenzo Malavasi
Source: Journal of Materials Chemistry A (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Structure–property correlation in oxide-ion and proton conductors for clean energy applications: recent experimental and computational advancements In the last decade, the field of oxide-ion and proton conductors continued to trigger a significant amount of basic research aimed at improving the properties and the comprehension of actual materials, as well as at discovering novel phases. Proton ; Oxide ; Electrical conductor ; Ion ; Property (philosophy) ; Nanotechnology ; Energy (signal processing) ; Materials science ; Engineering physics ; Computer science ; Physics ; Nuclear physics ; Composite material Advancements in Solid Oxide Fuel Cells ; Electronic and Structural Properties of Oxides ; Magnetic and transport properties of perovskites and related materials
Authors: Qingcheng Lin | Fuyong Lyu | Shiqi Yu | Hui Xiao | Xuefeng Li
Source: IEEE Transactions on Dielectrics and Electrical Insulation (article, 2022-06-16)
Field: Materials Science — Materials Chemistry

Optimized Denoising Method for Weak Acoustic Emission Signal in Partial Discharge Detection Acoustic emission (AE) technology can predict the occurrence of partial discharge (PD) faults, which is used to improve the safe operation level of gas-insulated switchgear (GIS) equipment. However, the strong noise interference from the production site is still the main factor affecting the identification accuracy. In this study, a simplified model is designed to approximate the accumulation of free metal particles on the surface of the GIS internal insulation structure, and white noise of various intensities is added to the collected PD-induced AE signals to simulate the background interference. The results prove that the proposed denoising method can achieve a better denoising effect in various signal-to-noise ratio (SNR) conditions. In particular, in the case of low SNR, the recognition accuracy of the accumulation degree of metal particles has been improved by more than 15%. Partial discharge ; Switchgear ; Noise reduction ; Interference (communication) ; Acoustic emission ; Noise (video) ; Acoustics ; SIGNAL (programming language) ; Electromagnetic interference ; Signal-to-noise ratio (imaging) ; White noise ; Materials science ; Computer science ; Electronic engineering ; Electrical engineering ; Engineering ; Physics ; Artificial intelligence ; Voltage ; Telecommunications High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Geophysical Methods and Applications
Authors: Martin Amezcua | Jeffry Setiadi | Ge Yunhui | David L. Mobley
Source: Journal of Computer-Aided Molecular Design (article, 2022-10-01)
Field: Chemistry — Physical and Theoretical Chemistry

An overview of the SAMPL8 host–guest binding challenge The SAMPL series of challenges aim to focus the community on specific modeling challenges, while testing and hopefully driving progress of computational methods to help guide pharmaceutical drug discovery. In this study, we report on the results of the SAMPL8 host-guest blind challenge for predicting absolute binding affinities. SAMPL8 focused on two host-guest datasets, one involving the cucurbituril CB8 (with a series of common drugs of abuse) and another involving two different Gibb deep-cavity cavitands. The latter dataset involved a previously featured deep cavity cavitand (TEMOA) as well as a new variant (TEETOA), both binding to a series of relatively rigid fragment-like guests. Challenge participants employed a reasonably wide variety of methods, though many of these were based on molecular simulations, and predictive accuracy was mixed. As in some previous SAMPL iterations (SAMPL6 and SAMPL7), we found that one approach to achieve greater accuracy was to apply empirical corrections to the binding free energy predictions, taking advantage of prior data on binding to these hosts. Another approach which performed well was a hybrid MD-based approach with reweighting to a force matched QM potential. In the cavitand challenge, an alchemical method using the AMOEBA-polarizable force field achieved the best success with RMSE less than 1 kcal/mol, while another alchemical approach (ATM/GAFF2-AM1BCC/TIP3P/HREM) had RMSE less than 1.75 kcal/mol. The work discussed here also highlights several important lessons; for example, retrospective studies of reference calculations demonstrate the sensitivity of predicted binding free energies to ethyl group sampling and/or guest starting pose, providing guidance to help improve future studies on these systems. Cavitand ; Binding affinities ; Computer science ; Force field (fiction) ; Chemistry ; Computational chemistry ; Binding potential ; Cucurbituril ; Machine learning ; Artificial intelligence ; Molecule ; Supramolecular chemistry Crystallography and molecular interactions ; Mass Spectrometry Techniques and Applications ; Protein Structure and Dynamics
Authors: Nikhil Pandey | Pavlos Anastasiadis | Christine P. Carney | Pranjali Kanvinde | Graeme F. Woodworth | Jeffrey A. Winkles | Anthony J. Kim
Source: Advanced Drug Delivery Reviews (review, 2022-07-03)
Field: Materials Science — Biomaterials

Nanotherapeutic treatment of the invasive glioblastoma tumor microenvironment Glioblastoma ; Brain cancer ; Brain tumor ; Tumor microenvironment ; Medicine ; Cancer research ; Cancer ; Tumor cells ; Oncology ; Pathology ; Internal medicine Nanoparticle-Based Drug Delivery ; Glioma Diagnosis and Treatment ; Nanoplatforms for cancer theranostics