jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Kevin Ryczko | Sebastian J. Wetzel | Roger G. Melko | Isaac Tamblyn
Source: Journal of Chemical Theory and Computation (article, 2022-01-07)
Field: Materials Science — Materials Chemistry

Toward Orbital-Free Density Functional Theory with Small Data Sets and Deep Learning We use voxel deep neural networks to predict energy densities and functional derivatives of electron kinetic energies for the Thomas-Fermi model and Kohn-Sham density functional theory calculations. We show that the ground-state electron density can be found via direct minimization for a graphene lattice without any projection scheme using a voxel deep neural network trained with the Thomas-Fermi model. Additionally, we predict the kinetic energy of a graphene lattice within chemical accuracy after training from only two Kohn-Sham density functional theory (DFT) calculations. We identify an important sampling issue inherent in Kohn-Sham DFT calculations and propose future work to rectify this problem. Furthermore, we demonstrate an alternative, functional derivative-free, Monte Carlo based orbital-free density functional theory algorithm to calculate an accurate two-electron density in a double inverted Gaussian potential with a machine-learned kinetic energy functional. Density functional theory ; Orbital-free density functional theory ; Time-dependent density functional theory ; Statistical physics ; Functional derivative ; Gaussian ; Energy functional ; Hybrid functional ; Physics ; Atomic orbital ; Thomas–Fermi model ; Kinetic energy ; Lattice (music) ; Computer science ; Quantum mechanics ; Electron Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Catalysis and Oxidation Reactions
Authors: Iman Peivaste | Nima H. Siboni | Ghasem Alahyarizadeh | Reza Ghaderi | Bob Svendsen | Dierk Raabe | Jaber Rezaei Mianroodi
Source: Computational Materials Science (article, 2022-08-31)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine-learning-based surrogate modeling of microstructure evolution using phase-field Computer science ; Artificial neural network ; Field (mathematics) ; Computer simulation ; Algorithm ; Phase (matter) ; Frame (networking) ; Statistical physics ; Applied mathematics ; Simulation ; Artificial intelligence ; Mathematics ; Physics Magnetic Properties and Applications ; Solidification and crystal growth phenomena ; Aluminum Alloy Microstructure Properties
Authors: Junkil Park | Yunsung Lim | Sangwon Lee | Jihan Kim
Source: Chemistry of Materials (article, 2022-12-23)
Field: Chemistry — Inorganic Chemistry

Computational Design of Metal–Organic Frameworks with Unprecedented High Hydrogen Working Capacity and High Synthesizability Compared to conventional computational screening studies that are limited by the size of database, inverse design has a great potential to facilitate identifying new materials with optimal properties. In this work, we integrate machine learning with genetic algorithm to computationally design metal–organic frameworks (MOFs) for hydrogen storage applications at cryogenic conditions. As such, we identified 6277 MOFs that exceed the current record (37.2 g/L of NPF-200) at operating conditions between 5 and 100 bar at 77 K. MOFs, whose working capacities exceed 40.0 g/L (system-based 2025 DOE target) were also identified, where the highest working capacity obtained from this work was 41.6 g/L, which is higher than any other hypothetical MOFs reported thus far. Furthermore, synthesizability of the top performing structures was assessed by comparing relative stability with their polymorphic structures while taking into account the possibility of interpenetration. We demonstrate that our methodology can successfully design MOFs with both high hydrogen capacity and synthesizability and we anticipate our workflow can be widely applied to various other materials and applications. Workflow ; Metal-organic framework ; Hydrogen storage ; Work (physics) ; Computer science ; Hydrogen ; Inverse ; Material Design ; Nanotechnology ; Materials science ; Process engineering ; Mechanical engineering ; Chemistry ; Database ; Engineering ; Mathematics Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Catalytic Processes in Materials Science
Authors: Xiaoxi Chen | Yaling Jiao | Bin Liu | Wenhui Chao | Xuchang Duan | Tianli Yue
Source: Food Chemistry (article, 2022-03-23)
Field: Chemistry — Analytical Chemistry

Using hyperspectral imaging technology for assessing internal quality parameters of persimmon fruits during the drying process Hyperspectral imaging ; Partial least squares regression ; Principal component analysis ; Tannin ; Support vector machine ; Artificial intelligence ; Least squares support vector machine ; Pattern recognition (psychology) ; Preprocessor ; Mathematics ; Biological system ; Projection (relational algebra) ; Chemometrics ; Artificial neural network ; Chemistry ; Computer science ; Food science ; Statistics ; Chromatography ; Algorithm Spectroscopy and Chemometric Analyses ; Phytochemicals and Antioxidant Activities ; Horticultural and Viticultural Research
Authors: Manika Chaudhary | Ashwani Kumar | Arti Devi | Beer Singh | Bansi D. Malhotra | Kushagr Singhal | Sangeeta Shukla | Srikanth Ponnada | Rakesh K. Sharma | Carmen A. Vega-Olivencia | Shrestha Tyagi | Rahúl Singhal
Source: Materials Advances (review, 2022-11-25)
Field: Chemistry — Electrochemistry

Prospects of nanostructure-based electrochemical sensors for drug detection: a review The present study represents the advancements achieved over the past ten years towards the development of electrochemical sensors based on nanomaterials. Nanomaterials ; Nanotechnology ; Nanostructure ; Materials science Electrochemical Analysis and Applications ; Advanced biosensing and bioanalysis techniques ; Electrochemical sensors and biosensors
Authors: Ben Niu | Wenxuan Jiang | Bo Jiang | Mengqi Lv | Sa Wang | Wei Wang
Source: Nature Communications (article, 2022-04-28)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Determining the depth of surface charging layer of single Prussian blue nanoparticles with pseudocapacitive behaviors Understanding the hybrid charge-storage mechanisms of pseudocapacitive nanomaterials holds promising keys to further improve the performance of energy storage devices. Based on the dependence of the light scattering intensity of single Prussian blue nanoparticles (PBNPs) on their oxidation state during sinusoidal potential modulation at varying frequencies, we present an electro-optical microscopic imaging approach to optically acquire the Faradaic electrochemical impedance spectroscopy (oEIS) of single PBNPs. Here we reveal typical pseudocapacitive behavior with hybrid charge-storage mechanisms depending on the modulation frequency. In the low-frequency range, the optical amplitude is inversely proportional to the square root of the frequency (∆I ∝ f<sup>-0.5</sup>; diffusion-limited process), while in the high-frequency range, it is inversely proportional to the frequency (∆I ∝ f<sup>-1</sup>; surface charging process). Because the geometry of single cuboid-shaped PBNPs can be precisely determined by scanning electron microscopy and atomic force microscopy, oEIS of single PBNPs allows the determination of the depth of the surface charging layer, revealing it to be ~2 unit cells regardless of the nanoparticle size. Prussian blue ; Nanoparticle ; Materials science ; Layer (electronics) ; Nanotechnology ; Chemistry ; Electrode ; Electrochemistry Supercapacitor Materials and Fabrication ; Conducting polymers and applications ; Advanced Sensor and Energy Harvesting Materials
Authors: Jia-Qi Lang | Wenting Wang | Y. Zhou | Dongqin Guo | Rujie Shi | Nong Zhou
Source: Frontiers in Chemistry (article, 2022-04-26)
Field: Chemistry — Spectroscopy

Electrochemical Behavior and Direct Quantitative Determination of Paclitaxel The electrochemical behavior and direct quantitative determination of paclitaxel, a poorly soluble drug made into microemulsion, were researched by cyclic voltammetry in acetate buffer solutions (pH = 4.0) at a glassy carbon electrode. The results show that the oxidation process is irreversible and controlled by diffusion. Moreover, the effects of anodic peak current (Ipa), anodic peak potential, scan rate, pH, and the electrochemical redox mechanism have been studied. The anodic peak current varied linearly with paclitaxel concentration in the range of 5 × 10 −5 mol/L to 5 × 10 −4 mol/L, and the detection limit was 9.15 × 10 −8 mol/L. The results of RSD (0.90%) and recovery (99.22%–101.69%) were obtained. Additionally, it has been proved that one electron and one proton are involved in the electrochemical redox process. The present research has been successfully used to determine paclitaxel in pure and real samples, which further supported the electrochemical behavior investigation of paclitaxel and direct determination of micro-emulsion. Electrochemistry ; Redox ; Anode ; Cyclic voltammetry ; Horizontal scan rate ; Chemistry ; Detection limit ; Diffusion ; Microemulsion ; Glassy carbon ; Electrode ; Emulsion ; Analytical Chemistry (journal) ; Inorganic chemistry ; Chromatography ; Physical chemistry ; Organic chemistry ; Pulmonary surfactant ; Thermodynamics Analytical Chemistry and Chromatography ; Analytical Chemistry and Sensors ; Electrochemical sensors and biosensors
Authors: Jiuyang Zhao | Jacqueline M. Cole
Source: Scientific Data (article, 2022-05-03)
Field: Materials Science — Materials Chemistry

A database of refractive indices and dielectric constants auto-generated using ChemDataExtractor Abstract The ability to auto-generate databases of optical properties holds great potential for advancing optical research, especially with regards to the data-driven discovery of optical materials. An optical property database of refractive indices and dielectric constants is presented, which comprises a total of 49,076 refractive index and 60,804 dielectric constant data records on 11,054 unique chemicals. The database was auto-generated using the state-of-the-art natural language processing software, ChemDataExtractor, using a corpus of 388,461 scientific papers. The data repository offers a representative overview of the information on linear optical properties that resides in scientific papers from the past 30 years. Public availability of these data will enable a quick search for the optical property of certain materials. The large size of this repository will accelerate data-driven research on the design and prediction of optical materials and their properties. To the best of our knowledge, this is the first auto-generated database of optical properties from a large number of scientific papers. We provide a web interface to aid the use of our database. Database ; Dielectric ; Computer science ; Refractive index ; Property (philosophy) ; 3D optical data storage ; Software ; Information retrieval ; Interface (matter) ; Constant (computer programming) ; Data mining ; Materials science ; Optoelectronics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Brahim Benmouna | Ginés Garcı́a-Mateos | Sajad Sabzi | Rubén Fernández-Beltrán | Dolores Parras-Burgos | José Miguel Molina‐Martínez
Source: Food and Bioprocess Technology (article, 2022-07-18)
Field: Chemistry — Analytical Chemistry

Convolutional Neural Networks for Estimating the Ripening State of Fuji Apples Using Visible and Near-Infrared Spectroscopy Abstract The quality of fresh apple fruits is a major concern for consumers and manufacturers. Classification of these fruits according to their ripening stage is one of the most decisive factors in determining their quality. In this regard, the aim of this work is to develop a new method for non-destructive classification of the ripening state of Fuji apples using hyperspectral information in the visible and near-infrared (Vis/NIR) regions. Spectra of 172 apple samples in the range from 450 to 1000 nm were studied, which were selected from four different ripening stages. A convolutional neural network (CNN) model was proposed to perform the classification of the samples. The proposed method was compared with three alternative methods based on artificial neural networks (ANN), support vector machines (SVM), and k -nearest neighbors (KNN). The results revealed that the CNN method outperformed the alternative methods, achieving a correct classification rate (CCR) of 96.5%, compared with an average of 89.5%, 95.93%, and 91.68% for ANN, SVM, and KNN, respectively. These results will help in the development of a new device for fast and accurate estimation of the quality of apples. Ripening ; Convolutional neural network ; Support vector machine ; Hyperspectral imaging ; Artificial intelligence ; Pattern recognition (psychology) ; Artificial neural network ; Computer science ; Chemistry ; Food science Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Water Quality Monitoring and Analysis
Authors: M. Balodis | Manuel Cordova | Albert Hofstetter | Graeme M. Day | Lyndon Emsley
Source: Journal of the American Chemical Society (article, 2022-04-13)
Field: Chemistry — Spectroscopy

<i>De Novo</i> Crystal Structure Determination from Machine Learned Chemical Shifts Determination of the three-dimensional atomic-level structure of powdered solids is one of the key goals in current chemistry. Solid-state NMR chemical shifts can be used to solve this problem, but they are limited by the high computational cost associated with crystal structure prediction methods and density functional theory chemical shift calculations. Here, we successfully determine the crystal structures of ampicillin, piroxicam, cocaine, and two polymorphs of the drug molecule AZD8329 using on-the-fly generated machine-learned isotropic chemical shifts to directly guide a Monte Carlo-based structure determination process starting from a random gas-phase conformation. Chemistry ; Crystal structure prediction ; Chemical shift ; Crystal structure ; Molecule ; Density functional theory ; Monte Carlo method ; Isotropy ; Crystal (programming language) ; Computational chemistry ; Chemical physics ; Crystallography ; Physical chemistry ; Organic chemistry Advanced NMR Techniques and Applications ; X-ray Diffraction in Crystallography ; Crystallography and molecular interactions
Authors: Florian Tanner | Sebastian Tonn | Jos de Wit | Guido Van den Ackerveken | Bettina Berger | Darren Plett
Source: Plant Methods (review, 2022-03-21)
Field: Chemistry — Analytical Chemistry

Sensor-based phenotyping of above-ground plant-pathogen interactions Plant pathogens cause yield losses in crops worldwide. Breeding for improved disease resistance and management by precision agriculture are two approaches to limit such yield losses. Both rely on detecting and quantifying signs and symptoms of plant disease. To achieve this, the field of plant phenotyping makes use of non-invasive sensor technology. Compared to invasive methods, this can offer improved throughput and allow for repeated measurements on living plants. Abiotic stress responses and yield components have been successfully measured with phenotyping technologies, whereas phenotyping methods for biotic stresses are less developed, despite the relevance of plant disease in crop production. The interactions between plants and pathogens can lead to a variety of signs (when the pathogen itself can be detected) and diverse symptoms (detectable responses of the plant). Here, we review the strengths and weaknesses of a broad range of sensor technologies that are being used for sensing of signs and symptoms on plant shoots, including monochrome, RGB, hyperspectral, fluorescence, chlorophyll fluorescence and thermal sensors, as well as Raman spectroscopy, X-ray computed tomography, and optical coherence tomography. We argue that choosing and combining appropriate sensors for each plant-pathosystem and measuring with sufficient spatial resolution can enable specific and accurate measurements of above-ground signs and symptoms of plant disease. Pathosystem ; Plant disease ; Hyperspectral imaging ; Phenomics ; Precision agriculture ; Remote sensing ; Biology ; Environmental science ; Biotechnology ; Computer science ; Agriculture ; Artificial intelligence ; Ecology ; Genomics Spectroscopy and Chemometric Analyses ; Remote Sensing in Agriculture ; Smart Agriculture and AI
Authors: Nemanja Teslić | Filipa Santos | Filipe Oliveira | Alena Stupar | Milica Pojić | Anamarija Mandić | Branimir Pavlić | Aleksandra Cvetanović | Ana Rita C. Duarte | Alexandre Paiva | Aleksandra Mišan
Source: Antioxidants (article, 2022-01-28)
Field: Chemistry — Organic Chemistry

Simultaneous Hydrolysis of Ellagitannins and Extraction of Ellagic Acid from Defatted Raspberry Seeds Using Natural Deep Eutectic Solvents (NADES) Defatted raspberry seeds were used as an alternative source of antioxidants and ellagic acid (EA) extracted using Natural deep eutectic solvents (NADES). In the preliminary study, the best NADES combination (citric acid-betaine) and the most influential variables (temperature, time, and NADES/plant ratio) were selected for the further optimization process. All samples were analyzed in terms of total polyphenol, EA content, and antioxidant activity. Two sets of optimal conditions were generated by response surface methodology. The first set (Opt1) was designed for higher conversion of ellagitannins to EA while the latter set (Opt2) for higher EA content/100 g extract. Opt1 and Opt2 had higher values for all investigated responses compared to 80% ethanolic extract but had a lower conversion rate of ellagitannins to EA compared to acidified methanol extract. The third set of parameters (Opt3) selected beyond the initial experimental domain was used to obtain a sample with the highest EA content/100 g extract. Due to their nature, NADES extracts are ready to use and could have various technological roles in products since they are antioxidants, acidifiers, and colorants. NADES raspberry extracts exhibited higher anti-proliferative activity compared to ethanolic extracts in terms of EC<sub>50</sub> values. However, the main contributor of anti-cancer activity in NADES raspberry extracts were individual NADES compounds and/or their newly formed NADES structure. Ellagic acid ; Blowing a raspberry ; Chemistry ; Citric acid ; Polyphenol ; Food science ; Pyrogallol ; Antioxidant ; Biochemistry Inorganic and Organometallic Chemistry ; Ionic liquids properties and applications ; Free Radicals and Antioxidants
Authors: Banseok Oh | Hyeokjun Seo | Jihoon Choi | Sunggyu Lee | Dong‐Yeun Koh
Source: Nature Communications (article, 2022-08-24)
Field: Materials Science — Materials Chemistry

Electron-mediated control of nanoporosity for targeted molecular separation in carbon membranes Carbon molecular sieve (CMS) membranes are considered game-changers to overcome the challenges that conventional polymeric membranes face. However, CMS membranes also confront a challenge in successfully separating extremely similar-sized molecules. In this article, high-precision tuning of the microstructure of CMS membranes is proposed by controlled electron irradiation for the separation of molecules with size differences less than 0.05 nm. Fitting CMS membranes for targeted molecular separation can be accomplished by irradiation dosage control, resulting in highly-efficient C<sub>2</sub>H<sub>4/</sub>C<sub>2</sub>H<sub>6</sub> separation for low dosages (∼250kGy, with selectivity ∼14) and ultra-selective H<sub>2</sub>/CO<sub>2</sub> separation for high dosages (1000∼2000kGy with selectivity ∼80).The electron irradiated CMS also exhibits highly stabilized permeability and selectivity for long-term operation than the pristine CMS, which suffers from significant performance degradation due to physical aging. This study successfully demonstrates electron irradiation as a possible way to construct "designer" nanoporous carbon membranes out of the standard components mostly confined to pyrolysis conditions. Membrane ; Selectivity ; Materials science ; Nanoporous ; Chemical engineering ; Molecular sieve ; Carbon fibers ; Molecule ; Gas separation ; Nanotechnology ; Chemistry ; Adsorption ; Organic chemistry ; Composite material ; Catalysis Graphene research and applications ; Membrane Separation and Gas Transport ; Fuel Cells and Related Materials
Authors: Vittorio Fortino | Pia Kinaret | Michele Fratello | Angela Serra | Laura Aliisa Saarimäki | Audrey Gallud | Govind Gupta | Gerard Vales | Manuel Correia | Omid Rasool | Jimmy Ytterberg | Marco P. Monopoli | Tiina Skoog | Peter Ritchie | Sergio Moya | Socorro Vázquez‐Campos | Richard D. Handy | Roland Grafström | Lang Tran | Roman A. Zubarev | Riitta Lahesmaa | Kenneth A. Dawson | Katrin Loeschner | Erik Husfeldt Larsen | Fritz Krombach | Hannu Norppa | Juha Kere | Kai Savolainen | Harri Alenius | Bengt Fadeel | Dario Greco
Source: Nature Communications (article, 2022-07-01)
Field: Materials Science — Materials Chemistry

Biomarkers of nanomaterials hazard from multi-layer data There is an urgent need to apply effective, data-driven approaches to reliably predict engineered nanomaterial (ENM) toxicity. Here we introduce a predictive computational framework based on the molecular and phenotypic effects of a large panel of ENMs across multiple in vitro and in vivo models. Our methodology allows for the grouping of ENMs based on multi-omics approaches combined with robust toxicity tests. Importantly, we identify mRNA-based toxicity markers and extensively replicate them in multiple independent datasets. We find that models based on combinations of omics-derived features and material intrinsic properties display significantly improved predictive accuracy as compared to physicochemical properties alone. Nanomaterials ; Layer (electronics) ; Hazard ; Computer science ; Nanotechnology ; Materials science ; Chemistry Nanoparticles: synthesis and applications
Authors: Avelino Corma | Pablo Botella | Eva Rivero‐Buceta
Source: Pharmaceutics (review, 2022-01-04)
Field: Materials Science — Biomaterials

Silica-Based Stimuli-Responsive Systems for Antitumor Drug Delivery and Controlled Release The administration of cytotoxic drugs in classical chemotherapy is frequently limited by water solubility, low plasmatic stability, and a myriad of secondary effects associated with their diffusion to healthy tissue. In this sense, novel pharmaceutical forms able to deliver selectively these drugs to the malign cells, and imposing a space-time precise control of their discharge, are needed. In the last two decades, silica nanoparticles have been proposed as safe vehicles for antitumor molecules due to their stability in physiological medium, high surface area and easy functionalization, and good biocompatibility. In this review, we focus on silica-based nanomedicines provided with specific mechanisms for intracellular drug release. According to silica nature (amorphous, mesostructured, and hybrids) nanocarriers responding to a variety of stimuli endogenously (e.g., pH, redox potential, and enzyme activity) or exogenously (e.g., magnetic field, light, temperature, and ultrasound) are proposed. Furthermore, the incorporation of targeting molecules (e.g., monoclonal antibodies) that interact with specific cell membrane receptors allows a selective delivery to cancer cells to be carried out. Eventually, we present some remarks on the most important formulations in the pipeline for clinical approval, and we discuss the most difficult tasks to tackle in the near future, in order to extend the use of these nanomedicines to real patients. Nanocarriers ; Drug delivery ; Biocompatibility ; Nanotechnology ; Surface modification ; Chemistry ; Drug ; Doxorubicin ; Targeted drug delivery ; Biophysics ; Pharmacology ; Materials science ; Medicine ; Chemotherapy ; Biology Nanoparticle-Based Drug Delivery ; Nanoplatforms for cancer theranostics ; Graphene and Nanomaterials Applications