jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Bo Wen | Marcos F. Calegari Andrade | Yuandong Niu | Annabella Selloni
Source: Proceedings of the National Academy of Sciences (article, 2023-01-04)
Field: Materials Science — Materials Chemistry

Water dissociation at the water–rutile TiO <sub>2</sub> (110) interface from ab initio-based deep neural network simulations The interaction of water with TiO<sub>2</sub> surfaces is of crucial importance in various scientific fields and applications, from photocatalysis for hydrogen production and the photooxidation of organic pollutants to self-cleaning surfaces and bio-medical devices. In particular, the equilibrium fraction of water dissociation at the TiO<sub>2</sub>-water interface has a critical role in the surface chemistry of TiO<sub>2</sub>, but is difficult to determine both experimentally and computationally. Among TiO<sub>2</sub> surfaces, rutile TiO<sub>2</sub>(110) is of special interest as the most abundant surface of TiO<sub>2</sub>'s stable rutile phase. While surface-science studies have provided detailed information on the interaction of rutile TiO<sub>2</sub>(110) with gas-phase water, much less is known about the TiO<sub>2</sub>(110)-water interface, which is more relevant to many applications. In this work, we characterize the structure of the aqueous TiO<sub>2</sub>(110) interface using nanosecond timescale molecular dynamics simulations with ab initio-based deep neural network potentials that accurately describe water/TiO<sub>2</sub>(110) interactions over a wide range of water coverages. Simulations on TiO<sub>2</sub>(110) slab models of increasing thickness provide insight into the dynamic equilibrium between molecular and dissociated adsorbed water at the interface and allow us to obtain a reliable estimate of the equilibrium fraction of water dissociation. We find a dissociation fraction of 22 ± 6% with an associated average hydroxyl lifetime of 7.6 ± 1.8 ns<i>.</i> These quantities are both much larger than corresponding estimates for the aqueous anatase TiO<sub>2</sub>(101) interface, consistent with the higher water photooxidation activity that is observed for rutile relative to anatase. Dissociation (chemistry) ; Rutile ; Anatase ; Chemical physics ; Ab initio ; Materials science ; Self-ionization of water ; Aqueous solution ; Ab initio quantum chemistry methods ; Photocatalysis ; Adsorption ; Chemistry ; Physical chemistry ; Molecule ; Catalysis ; Organic chemistry Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Advanced Photocatalysis Techniques
Authors: ABH Kueh | AW Razali | Y.Y. Lee | Sinin Hamdan | Ibrahim Yakub | N. Suhaili
Source: Materials Today Communications (article, 2023-02-02)
Field: Chemistry — Spectroscopy

Acoustical and mechanical characteristics of mortars with pineapple leaf fiber and silica aerogel infills – Measurement and modeling Aerogel ; Materials science ; Composite material ; Compressive strength ; Mortar ; Cement ; Cementitious ; Noise reduction coefficient ; Fiber ; Thermal insulation ; Fourier transform infrared spectroscopy ; Porosity ; Chemical engineering Aerogels and thermal insulation ; Building materials and conservation ; Magnesium Oxide Properties and Applications
Authors: Chit Siong Lau | Sarthak Das | Ivan Verzhbitskiy | Ding Huang | Yiyu Zhang | Teymour Talha-Dean | Wei Fu | Dasari Venkatakrishnarao | Kuan Eng Johnson Goh
Source: ACS Nano (review, 2023-05-31)
Field: Materials Science — Materials Chemistry

Dielectrics for Two-Dimensional Transition-Metal Dichalcogenide Applications Despite over a decade of intense research efforts, the full potential of two-dimensional transition-metal dichalcogenides continues to be limited by major challenges. The lack of compatible and scalable dielectric materials and integration techniques restrict device performances and their commercial applications. Conventional dielectric integration techniques for bulk semiconductors are difficult to adapt for atomically thin two-dimensional materials. This review provides a brief introduction into various common and emerging dielectric synthesis and integration techniques and discusses their applicability for 2D transition metal dichalcogenides. Dielectric integration for various applications is reviewed in subsequent sections including nanoelectronics, optoelectronics, flexible electronics, valleytronics, biosensing, quantum information processing, and quantum sensing. For each application, we introduce basic device working principles, discuss the specific dielectric requirements, review current progress, present key challenges, and offer insights into future prospects and opportunities. Valleytronics ; Nanoelectronics ; Nanotechnology ; Dielectric ; Materials science ; Engineering physics ; Electronics ; Semiconductor ; Scalability ; Computer science ; Transition metal ; Optoelectronics ; Electrical engineering ; Physics ; Engineering ; Chemistry 2D Materials and Applications ; Ferroelectric and Negative Capacitance Devices ; MXene and MAX Phase Materials
Authors: Kai Mundsinger | Aidan Izuagbe | Bryan T. Tuten | Peter W. Roesky | Christopher Barner‐Kowollik
Source: Angewandte Chemie International Edition (review, 2023-10-19)
Field: Chemistry — Organic Chemistry

Single Chain Nanoparticles in Catalysis Over the last six decades folded polymer chains-so-called Single Chain Nanoparticles (SCNPs)-have evolved from the mere concept of intramolecularly crosslinked polymer chains to tailored nanoreactors, underpinned by a plethora of techniques and chemistries to tailor and analyze their morphology and function. These monomolecular polymer entities hold critical promise in a wide range of applications. Herein, we highlight the exciting progress that has been made in the field of catalytically active SCNPs in recent years. Nanoparticle ; Catalysis ; Chain (unit) ; Nanotechnology ; Chemistry ; Materials science ; Physics ; Organic chemistry Nanomaterials for catalytic reactions ; Catalytic Processes in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Marc Riera | Chris Knight | Ethan F. Bull-Vulpe | Xuanyu Zhu | Henry Agnew | Daniel G. A. Smith | Andrew C. Simmonett | Francesco Paesani
Source: The Journal of Chemical Physics (article, 2023-08-01)
Field: Materials Science — Materials Chemistry

MBX: A many-body energy and force calculator for data-driven many-body simulations Many-Body eXpansion (MBX) is a C++ library that implements many-body potential energy functions (PEFs) within the "many-body energy" (MB-nrg) formalism. MB-nrg PEFs integrate an underlying polarizable model with explicit machine-learned representations of many-body interactions to achieve chemical accuracy from the gas to the condensed phases. MBX can be employed either as a stand-alone package or as an energy/force engine that can be integrated with generic software for molecular dynamics and Monte Carlo simulations. MBX is parallelized internally using Open Multi-Processing and can utilize Message Passing Interface when available in interfaced molecular simulation software. MBX enables classical and quantum molecular simulations with MB-nrg PEFs, as well as hybrid simulations that combine conventional force fields and MB-nrg PEFs, for diverse systems ranging from small gas-phase clusters to aqueous solutions and molecular fluids to biomolecular systems and metal-organic frameworks. Molecular dynamics ; Statistical physics ; Monte Carlo method ; Potential energy ; Software ; Force field (fiction) ; Physics ; Computer science ; Computational science ; Biological system ; Chemistry ; Computational chemistry ; Classical mechanics ; Quantum mechanics ; Mathematics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Advanced Chemical Physics Studies
Authors: Danial Khatamsaz | Raymond Neuberger | Arunabha M. Roy | Sina Hossein Zadeh | Richard Otis | Raymundo Arróyave
Source: npj Computational Materials (article, 2023-12-13)
Field: Materials Science — Materials Chemistry

A physics informed bayesian optimization approach for material design: application to NiTi shape memory alloys Abstract The design of materials and identification of optimal processing parameters constitute a complex and challenging task, necessitating efficient utilization of available data. Bayesian Optimization (BO) has gained popularity in materials design due to its ability to work with minimal data. However, many BO-based frameworks predominantly rely on statistical information, in the form of input-output data, and assume black-box objective functions. In practice, designers often possess knowledge of the underlying physical laws governing a material system, rendering the objective function not entirely black-box, as some information is partially observable. In this study, we propose a physics-informed BO approach that integrates physics-infused kernels to effectively leverage both statistical and physical information in the decision-making process. We demonstrate that this method significantly improves decision-making efficiency and enables more data-efficient BO. The applicability of this approach is showcased through the design of NiTi shape memory alloys, where the optimal processing parameters are identified to maximize the transformation temperature. Bayesian optimization ; Leverage (statistics) ; Computer science ; Bayesian probability ; Design of experiments ; Optimal design ; Machine learning ; Industrial engineering ; Artificial intelligence ; Mathematics ; Engineering Machine Learning in Materials Science ; Shape Memory Alloy Transformations ; Topology Optimization in Engineering
Authors: Tuhin Mandal | Shiv Rag Mishra | Vikram Singh
Source: Nanoscale Advances (review, 2023-01-01)
Field: Materials Science — Materials Chemistry

Comprehensive advances in the synthesis, fluorescence mechanism and multifunctional applications of red-emitting carbon nanomaterials Red emitting fluorescent carbon nanomaterials have drawn significant scientific interest in recent years due to their high quantum yield, water-dispersibility, photostability, biocompatibility, ease of surface functionalization, low cost and eco-friendliness. The red emissive characteristics of fluorescent carbon nanomaterials generally depend on the carbon source, reaction time, synthetic approach/methodology, surface functional groups, average size, and other reaction environments, which directly or indirectly help to achieve red emission. The importance of several factors to achieve red fluorescent carbon nanomaterials is highlighted in this review. Numerous plausible theories have been explained in detail to understand the origin of red fluorescence and tunable emission in these carbon-based nanostructures. The above advantages and fluorescence in the red region make them a potential candidate for multifunctional applications in various current fields. Therefore, this review focused on the recent advances in the synthesis approach, mechanism of fluorescence, and electronic and optical properties of red-emitting fluorescent carbon nanomaterials. This review also explains the several innovative applications of red-emitting fluorescent carbon nanomaterials such as biomedicine, light-emitting devices, sensing, photocatalysis, energy, anticounterfeiting, fluorescent silk, artificial photosynthesis, <i>etc.</i> It is hoped that by choosing appropriate methods, the present review can inspire and guide future research on the design of red emissive fluorescent carbon nanomaterials for potential advancements in multifunctional applications. Nanomaterials ; Nanotechnology ; Mechanism (biology) ; Fluorescence ; Carbon fibers ; Materials science ; Physics ; Optics ; Composite number Carbon and Quantum Dots Applications ; Luminescence and Fluorescent Materials ; Nanocluster Synthesis and Applications
Authors: Ramendra Pati Pandey | Jasmina Vidić | Riya Mukherjee | Chung-Ming Chang
Source: Pharmaceutics (review, 2023-02-11)
Field: Materials Science — Materials Chemistry

Experimental Methods for the Biological Evaluation of Nanoparticle-Based Drug Delivery Risks Many novel medical therapies use nanoparticle-based drug delivery systems, including nanomaterials through drug delivery systems, diagnostics, or physiologically active medicinal products. The approval of nanoparticles with advanced therapeutic and diagnostic potentials for applications in medication and immunization depends strongly on their synthesizing procedure, efficiency of functionalization, and biological safety and biocompatibility. Nanoparticle biodistribution, absorption, bioavailability, passage across biological barriers, and biodistribution are frequently assessed using bespoke and biological models. These methods largely rely on in vitro cell-based evaluations that cannot predict the complexity involved in preclinical and clinical studies. Therefore, assessing the nanoparticle risk has to involve pharmacokinetics, organ toxicity, and drug interactions manifested at multiple cellular levels. At the same time, there is a need for novel approaches to examine nanoparticle safety risks due to increased constraints on animal exploitation and the demand for high-throughput testing. We focus here on biological evaluation methodologies that provide access to nanoparticle interactions with the organism (positive or negative via toxicity). This work aimed to provide a perception regarding the risks associated with the utilization of nanoparticle-based formulations with a particular focus on assays applied to assess the cytotoxicity of nanomaterials. Biodistribution ; Drug delivery ; Nanotechnology ; Drug ; Biochemical engineering ; Nanoparticle ; Pharmacology ; Computer science ; Risk analysis (engineering) ; Medicine ; Materials science ; Biotechnology ; In vivo ; Biology ; Engineering Nanoparticles: synthesis and applications ; Nanoparticle-Based Drug Delivery ; RNA Interference and Gene Delivery
Authors: Keilian MacCulloch | Austin Browning | David Guarin | Stephen McBride | Mustapha Abdulmojeed | Carlos Dedesma | Boyd M. Goodson | Matthew S. Rosen | Eduard Y. Chekmenev | Yi‐Fen Yen | Patrick TomHon | Thomas Theis
Source: Journal of Magnetic Resonance Open (article, 2023-07-13)
Field: Chemistry — Spectroscopy

Facile hyperpolarization chemistry for molecular imaging and metabolic tracking of [1–13C]pyruvate in vivo Hyperpolarization chemistry based on reversible exchange of parahydrogen, also known as Signal Amplification By Reversible Exchange (SABRE), is a particularly simple approach to attain high levels of nuclear spin hyperpolarization, which can enhance NMR and MRI signals by many orders of magnitude. SABRE has received significant attention in the scientific community since its inception because of its relative experimental simplicity and its broad applicability to a wide range of molecules, however in vivo detection of molecular probes hyperpolarized by SABRE has remained elusive. Here we describe a first demonstration of SABRE-hyperpolarized contrast detected in vivo, specifically using hyperpolarized [1-<sup>13</sup>C]pyruvate. Biocompatible formulations of hyperpolarized [1-<sup>13</sup>C]pyruvate in, both, methanol-water mixtures, and ethanol-water mixtures followed by dilution with saline and catalyst filtration were prepared and injected into healthy Sprague Dawley and Wistar rats. Effective hyperpolarization-catalyst removal was performed with silica filters without major losses in hyperpolarization. Metabolic conversion of pyruvate to lactate, alanine, and bicarbonate was detected <i>in vivo</i>. Pyruvate-hydrate was also observed as minor byproduct. Measurements were performed on the liver and kidney at 4.7 T via time-resolved spectroscopy and chemical-shift-resolved MRI. In addition, whole-body metabolic measurements were obtained using a cryogen-free 1.5 T MRI system, illustrating the utility of combining lower-cost MRI systems with simple, low-cost hyperpolarization chemistry to develop safe, and scalable molecular imaging. Hyperpolarization (physics) ; Chemistry ; Spin isomers of hydrogen ; In vivo ; Nuclear magnetic resonance spectroscopy ; Nuclear magnetic resonance ; Hydrogen ; Organic chemistry Advanced NMR Techniques and Applications ; Solid-state spectroscopy and crystallography ; Electron Spin Resonance Studies
Authors: Ethan L. W. Gardner | Julian W. Gardner | Florin Udrea
Source: Sensors (review, 2023-01-06)
Field: Materials Science — Materials Chemistry

Micromachined Thermal Gas Sensors—A Review In recent years, there has been a growing desire to monitor and control harmful substances arising from industrial processes that impact upon our health and quality of life. This has led to a large market demand for gas sensors, which are commonly based on sensors that rely upon a chemical reaction with the target analyte. In contrast, thermal conductivity detectors are physical sensors that detect gases through a change in their thermal conductivity. Thermal conductivity gas sensors offer several advantages over their chemical (reactive) counterparts that include higher reproducibility, better stability, lower cost, lower power consumption, simpler construction, faster response time, longer lifetime, wide dynamic range, and smaller footprint. It is for these reasons, despite a poor selectivity, that they are gaining renewed interest after recent developments in MEMS-based silicon sensors allowing CMOS integration and smart application within the emerging Internet of Things (IoT). This timely review focuses on the state-of-the-art in thermal conductivity sensors; it contains a general introduction, theory of operation, interface electronics, use in commercial applications, and recent research developments. In addition, both steady-state and transient methods of operation are discussed with their relative advantages and disadvantages presented. Finally, some of recent innovations in thermal conductivity gas sensors are explored. Thermal conductivity ; Microelectromechanical systems ; Electronics ; Footprint ; Computer science ; Nanotechnology ; Process engineering ; Materials science ; Engineering ; Electrical engineering Thermal properties of materials ; Mechanical and Optical Resonators ; Gas Sensing Nanomaterials and Sensors
Authors: Ali Erçetin | Kubilay Aslantaş | Özgür Özgün | Mustafa Perçin | Manjunath Patel Gowdru Chandrashekarappa
Source: Micromachines (article, 2023-08-12)
Field: Materials Science — Biomaterials

Optimization of Machining Parameters to Minimize Cutting Forces and Surface Roughness in Micro-Milling of Mg13Sn Alloy This comprehensive study investigates the micro-milling of a Mg13Sn alloy, a material of considerable interest in various high-precision applications, such as biomedical implants. The main objective of the study was to explore the optimizations of variable feed per tooth (fz), cutting speed (Vc), and depth of cut (ap) parameters on the key outcomes of the micro-milling process. A unique experimental setup was employed, employing a spindle capable of achieving up to 60,000 revolutions per minute. Additionally, the study leveraged linear slides backed by micro-step motors to facilitate precise axis movements, thereby maintaining a resolution accuracy of 0.1 μm. Cutting forces were accurately captured by a mini dynamometer and subsequently evaluated based on the peak to valley values for Fx (tangential force) and Fy (feed force). The study results revealed a clear and complex interplay between the varied cutting parameters and their subsequent impacts on the cutting forces and surface roughness. An increase in feed rate and depth of cut significantly increased the cutting forces. However, the cutting forces were found to decrease noticeably with the elevation of cutting speed. Intriguingly, the tangential force (Fx) was consistently higher than the feed force (Fy). Simultaneously, the study determined that the surface roughness, denoted by Sa values, increased in direct proportion to the feed rate. It was also found that the Sa surface roughness values decreased with the increase in cutting speed. This study recommends a parameter combination of fz = 5 µm/tooth feed rate, Vc = 62.8 m/min cutting speed, and ap = 400 µm depth of cut to maintain a Sa surface roughness value of less than 1 µm while ensuring an optimal material removal rate and machining time. The results derived from this study offer vital insights into the micro-milling of Mg13Sn alloys and contribute to the current body of knowledge on the topic. Surface roughness ; Dynamometer ; Machining ; Materials science ; Mechanical engineering ; Surface finish ; Alloy ; Composite material ; Metallurgy ; Engineering Magnesium Alloys: Properties and Applications ; Aluminum Alloys Composites Properties ; Metal and Thin Film Mechanics
Authors: Nan Wang | Hongbo Liu | Yicheng Li | Weijun Zhou | Mingquan Ding
Source: Plants (article, 2023-09-20)
Field: Chemistry — Analytical Chemistry

Segmentation and Phenotype Calculation of Rapeseed Pods Based on YOLO v8 and Mask R-Convolution Neural Networks Rapeseed is a significant oil crop, and the size and length of its pods affect its productivity. However, manually counting the number of rapeseed pods and measuring the length, width, and area of the pod takes time and effort, especially when there are hundreds of rapeseed resources to be assessed. This work created two state-of-the-art deep learning-based methods to identify rapeseed pods and related pod attributes, which are then implemented in rapeseed pots to improve the accuracy of the rapeseed yield estimate. One of these methods is YOLO v8, and the other is the two-stage model Mask R-CNN based on the framework Detectron2. The YOLO v8n model and the Mask R-CNN model with a Resnet101 backbone in Detectron2 both achieve precision rates exceeding 90%. The recognition results demonstrated that both models perform well when graphic images of rapeseed pods are segmented. In light of this, we developed a coin-based approach for estimating the size of rapeseed pods and tested it on a test dataset made up of nine different species of <i>Brassica napus</i> and one of <i>Brassica campestris</i> L. The correlation coefficients between manual measurement and machine vision measurement of length and width were calculated using statistical methods. The length regression coefficient of both methods was 0.991, and the width regression coefficient was 0.989. In conclusion, for the first time, we utilized deep learning techniques to identify the characteristics of rapeseed pods while concurrently establishing a dataset for rapeseed pods. Our suggested approaches were successful in segmenting and counting rapeseed pods precisely. Our approach offers breeders an effective strategy for digitally analyzing phenotypes and automating the identification and screening process, not only in rapeseed germplasm resources but also in leguminous plants, like soybeans that possess pods. Rapeseed ; Brassica ; Point of delivery ; Correlation coefficient ; Artificial intelligence ; Segmentation ; Convolutional neural network ; Statistics ; Pattern recognition (psychology) ; Computer science ; Mathematics ; Horticulture ; Agronomy ; Biology Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Spectroscopy Techniques in Biomedical and Chemical Research