jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ke Wang | Jhair Peña | Jinfeng Xing
Source: Photochemistry and Photobiology (review, 2020-03-02)
Field: Materials Science — Materials Chemistry

Upconversion Nanoparticle‐Assisted Photopolymerization Near-infrared (NIR) laser has deeper penetration and lower thermal effect compared with ultraviolet-visible (UV/vis) laser. NIR laser can stimulate upconversion nanoparticles (UCNPs) to emit UV/vis light efficiently. Therefore, the UCNPs become an important kind of materials to produce upconversion luminescence through NIR laser. Compared with photopolymerization induced with UV/vis laser, the UV/vis light from UCNP-assisted photopolymerization presents some unique characters. This review focuses on the research progress of UCNP-assisted photopolymerization and its potential applications. We will discuss the influencing factors of UCNP-assisted polymerization, illustrate the relation between the gel size and monomer concentration and propose the development prospects of UCNP-assisted polymerization in the biological field. Photopolymer ; Laser ; Ultraviolet ; Polymerization ; Materials science ; Photon upconversion ; Nanoparticle ; Nanotechnology ; Visible spectrum ; Photochemistry ; Optoelectronics ; Luminescence ; Optics ; Chemistry ; Polymer ; Composite material ; Physics Luminescence Properties of Advanced Materials ; Photopolymerization techniques and applications ; Nanoplatforms for cancer theranostics
Authors: Meghana Matur | Harishkumar Madhyastha | T.S. Shruthi | Radha Madhyastha | Sangly P. Srinivas | P. N. Navya | Hemant Kumar Daima
Source: Scientific Reports (article, 2020-11-12)
Field: Materials Science — Materials Chemistry

Engineering bioactive surfaces on nanoparticles and their biological interactions The successful integration of nanoparticles into biomedical applications requires modulation of their surface properties so that the interaction with biological systems is regulated to minimize toxicity for biological function. In the present work, we have engineered bioactive surfaces on gold (Au) and silver (Ag) nanoparticles and subsequently evaluated their interaction with mouse skin fibroblasts and macrophages. The Au and Ag nanoparticles were synthesized using tyrosine, tryptophan, isonicotinylhydrazide, epigallocatechin gallate, and curcumin as reducing and stabilizing agents. The nanoparticles thus prepared showed surface corona and exhibited free radical scavenging and enzyme activities with limited cytotoxicity and genotoxicity. We have thus developed avenues for engineering the surface of nanoparticles for biological applications. Nanoparticle ; Computer science ; Nanotechnology ; Materials science Nanoparticles: synthesis and applications ; Graphene and Nanomaterials Applications ; Advanced Nanomaterials in Catalysis
Authors: Weiyi Zhou | Nathan J. Yutronkie | Benoît H. Lessard | Jaclyn L. Brusso
Source: Materials Advances (article, 2020-12-11)
Field: Materials Science — Materials Chemistry

From chemical curiosity to versatile building blocks: unmasking the hidden potential of main-group phthalocyanines in organic field-effect transistors Main-group phthalocyanines are an emerging class of organic semiconductors with versatile charge carriers in field-effect transistors. In this review, an overview is provided on the synthesis of these building blocks and device performances. Transistor ; Organic semiconductor ; Field-effect transistor ; Group (periodic table) ; Field (mathematics) ; Semiconductor ; Curiosity ; Materials science ; Optoelectronics ; Nanotechnology ; Chemistry ; Electrical engineering ; Engineering ; Organic chemistry ; Mathematics ; Psychology Porphyrin and Phthalocyanine Chemistry ; Organic Electronics and Photovoltaics ; Perovskite Materials and Applications
Authors: Goutam Ghosh | Tanwistha Ghosh | Gustavo Fernández
Source: ChemPlusChem (review, 2020-04-30)
Field: Materials Science — Biomaterials

Controlled Supramolecular Polymerization of d<sup>8</sup> Metal Complexes through Pathway Complexity and Seeded Growth In recent years, pathway complexity has been studied in detail for a large variety of organic and π-conjugated molecules. However, such investigations on their metal-containing analogues have received only little attention to date, despite the well-known potential of metal complexes in various fields. In this Minireview, we have collected recent examples of d<sup>8</sup> metal complexes (Pt(II), Pd(II) and Au(III) complexes) exhibiting controlled supramolecular polymerization through pathway complexity and seeded-growth approaches. Controlling the competing thermodynamic vs. kinetic pathways in these systems should help develop advanced metallosupramolecular functional materials with excellent photophysical, electronic, magnetic, catalytic, and biomedical applications. Supramolecular chemistry ; Polymerization ; Metal ; Conjugated system ; Nanotechnology ; Chemistry ; Molecule ; Catalysis ; Combinatorial chemistry ; Materials science ; Organic chemistry ; Polymer Supramolecular Self-Assembly in Materials ; Supramolecular Chemistry and Complexes ; Metal-Organic Frameworks: Synthesis and Applications
Authors: Edirisuriya M. Dilanga Siriwardane | Rajendra P. Joshi | Neeraj Kumar | Deniz Çakır
Source: ACS Applied Materials & Interfaces (article, 2020-06-04)
Field: Materials Science — Materials Chemistry

Revealing the Formation Energy–Exfoliation Energy–Structure Correlation of MAB Phases Using Machine Learning and DFT MAB phases became popular as ultrahigh-temperature materials with high damage tolerance and excellent electrical conductivity. MAB is used to exfoliate two-dimensional (2D) transition-metal borides (MBenes), which are promising materials for developing next-generation nanodevices. In this report, we explore the correlation between the formation energy, exfoliation energy, and structural factors of MAB phases with orthorhombic and hexagonal crystal symmetries using density functional theory (DFT) and machine learning. For this, we developed three different machine learning models based on the support vector machine, deep neural network, and random forest regressor to study the stability of the MAB phases by calculating their formation energies. Our support vector machine and deep neural network models are capable of predicting the formation energies with mean absolute errors less than 0.1 eV/atom. MAB phases with the chemical formulas, MAB, M<sub>2</sub>AB<sub>2</sub>, and M<sub>3</sub>AB<sub>4</sub>, where M = Nb, Mn, Ti, W, V, Sc, Cr, Hf, Mo, Zr, Ta, and Fe, and A = group III-A elements (Al, Ga, In and Tl), were investigated to find out the formation energy and their structure correlation. We demonstrated that the stability of a MAB phase for a given transition-metal decreases when the A element changes from Al to Tl. DFT revealed that M-A and B-A bond strength strongly correlates with the stability of MAB phases. In addition, the exfoliation possibility of 2D MBenes becomes higher when the A element changes from Al to Tl because of weakening of M-A and B-A bonds. Materials science ; Orthorhombic crystal system ; Density functional theory ; Exfoliation joint ; Atom (system on chip) ; Binding energy ; Crystal structure ; Crystallography ; Nanotechnology ; Graphene ; Computational chemistry ; Physics ; Atomic physics ; Chemistry ; Computer science MXene and MAX Phase Materials ; Graphene research and applications ; Machine Learning in Materials Science
Authors: Qi Yuan | Alejandro Santana‐Bonilla | Martijn A. Zwijnenburg | Kim E. Jelfs
Source: Nanoscale (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Molecular generation targeting desired electronic properties <i>via</i> deep generative models As we seek to discover new functional materials, we need ways to explore the vast chemical space of precursor building blocks, not only generating large numbers of possible building blocks to investigate, but trying to find non-obvious options, that we might not suggest by chemical experience alone. Artificial intelligence techniques provide a possible avenue to generate large numbers of organic building blocks for functional materials, and can even do so from very small initial libraries of known building blocks. Specifically, we demonstrate the application of deep recurrent neural networks for the exploration of the chemical space of building blocks for a test case of donor-acceptor oligomers with specific electronic properties. The recurrent neural network learned how to produce novel donor-acceptor oligomers by trading off between selected atomic substitutions, such as halogenation or methylation, and molecular features such as the oligomer's size. The electronic and structural properties of the generated oligomers can be tuned by sampling from different subsets of the training database, which enabled us to enrich the library of donor-acceptors towards desired properties. We generated approximately 1700 new donor-acceptor oligomers with a recurrent neural network tuned to target oligomers with a HOMO-LUMO gap <2 eV and a dipole moment <2 Debye, which could have potential application in organic photovoltaics. Generative grammar ; Nanotechnology ; Materials science ; Computational biology ; Computer science ; Artificial intelligence ; Biology Machine Learning in Materials Science ; Advanced Memory and Neural Computing ; Semiconductor materials and devices
Authors: Shengli Jiang | JungHyun Noh | Chulsoon Park | Alexander D. Smith | Nicholas L. Abbott | Ví­ctor M. Zavala
Source: The Analyst (article, 2020-12-10)
Field: Chemistry — Spectroscopy

Using machine learning and liquid crystal droplets to identify and quantify endotoxins from different bacterial species Detection and quantification of bacterial endotoxins is important in a range of health-related contexts, including during pharmaceutical manufacturing of therapeutic proteins and vaccines. Chemistry ; Nanotechnology ; Chemical engineering ; Materials science ; Engineering Analytical Chemistry and Chromatography ; Advanced Biosensing Techniques and Applications ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Christopher M. Andolina | Philip T. F. Williamson | Wissam A. Saidi
Source: The Journal of Chemical Physics (article, 2020-04-16)
Field: Materials Science — Materials Chemistry

Optimization and validation of a deep learning CuZr atomistic potential: Robust applications for crystalline and amorphous phases with near-DFT accuracy We show that a deep-learning neural network potential (DP) based on density functional theory (DFT) calculations can well describe Cu-Zr materials, an example of a binary alloy system, that can coexist in as ordered intermetallic and as an amorphous phase. The complex phase diagram for Cu-Zr makes it a challenging system for traditional atomistic force-fields that cannot accurately describe the different properties and phases. Instead, we show that a DP approach using a large database with ∼300k configurations can render results generally on par with DFT. The training set includes configurations of pristine and bulk elementary metals and intermetallic structures in the liquid and solid phases in addition to slab and amorphous configurations. The DP model was validated by comparing bulk properties such as lattice constants, elastic constants, bulk moduli, phonon spectra, and surface energies to DFT values for identical structures. Furthermore, we contrast the DP results with values obtained using well-established two embedded atom method potentials. Overall, our DP potential provides near DFT accuracy for the different Cu-Zr phases but with a fraction of its computational cost, thus enabling accurate computations of realistic atomistic models, especially for the amorphous phase. Materials science ; Density functional theory ; Amorphous solid ; Intermetallic ; Phase diagram ; Molecular dynamics ; Lattice constant ; Amorphous metal ; Thermodynamics ; Phase (matter) ; Embedded atom model ; Chemical physics ; Alloy ; Computational chemistry ; Crystallography ; Chemistry ; Physics ; Metallurgy Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Block Copolymer Self-Assembly
Authors: Ruben F. Kranenburg | Joshka Verduin | Yannick Weesepoel | Martin Alewijn | Marcel Heerschop | G.C.M. Koomen | Peter H. J. Keizers | Frank Bakker | Fionn Wallace | Annette van Esch | Annemieke Hulsbergen | Arian van Asten
Source: Drug Testing and Analysis (article, 2020-07-08)
Field: Chemistry — Analytical Chemistry

Rapid and robust on‐scene detection of cocaine in street samples using a handheld near‐infrared spectrometer and machine learning algorithms On-scene drug detection is an increasingly significant challenge due to the fast-changing drug market as well as the risk of exposure to potent drug substances. Conventional colorimetric cocaine tests involve handling of the unknown material and are prone to false-positive reactions on common pharmaceuticals used as cutting agents. This study demonstrates the novel application of 740-1070 nm small-wavelength-range near-infrared (NIR) spectroscopy to confidently detect cocaine in case samples. Multistage machine learning algorithms are used to exploit the limited spectral features and predict not only the presence of cocaine but also the concentration and sample composition. A model based on more than 10,000 spectra from case samples yielded 97% true-positive and 98% true-negative results. The practical applicability is shown in more than 100 case samples not included in the model design. One of the most exciting aspects of this on-scene approach is that the model can almost instantly adapt to changes in the illicit-drug market by updating metadata with results from subsequent confirmatory laboratory analyses. These results demonstrate that advanced machine learning strategies applied on limited-range NIR spectra from economic handheld sensors can be a valuable procedure for rapid on-site detection of illicit substances by investigating officers. In addition to forensics, this interesting approach could be beneficial for screening and classification applications in the pharmaceutical, food-safety, and environmental domains. Mobile device ; Computer science ; Drug detection ; Exploit ; Sample (material) ; Near-infrared spectroscopy ; Machine learning ; Artificial intelligence ; Metadata ; Algorithm ; Computer security ; Chemistry ; Chromatography ; World Wide Web ; Optics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced Chemical Sensor Technologies
Authors: T. Warren Liao | Guoqiang Li
Source: Journal of Materiomics (article, 2020-02-22)
Field: Materials Science — Materials Chemistry

Metaheuristic-based inverse design of materials – A survey There is a growing interest in the inverse approach to material deign, in which the desired target properties are used as input to identify the atomic identity, composition and structure (ACS) that exhibit such properties. As an overview, this paper surveys and summarizes previous works in metaheuristic-based inverse design of various materials. The basics of metaheuristic-based inverse design of materials are presented, including feature identification (fingerprinting), machine learning of ACS→property models (forward design), metaheuristic algorithms for property→ACS predictions (inverse design), and experimental validations, with focus on inverse design. The past studies are organized into a two-level hierarchy with how properties are predicted at the higher level, either by first principles, simulation, or machine learning model, and the number of target properties considered at the lower level, either one or more than one. The uniqueness and limitation of previous research are discussed and several possible topics for future research are identified. This review intends to serve as the steppingstone/springboard for those interested in advancing this area of research. Metaheuristic ; Inverse ; Computer science ; Parallel metaheuristic ; Inverse problem ; Focus (optics) ; Hierarchy ; Feature (linguistics) ; Identification (biology) ; Property (philosophy) ; Artificial intelligence ; Mathematics Machine Learning in Materials Science ; Topology Optimization in Engineering ; Advanced Multi-Objective Optimization Algorithms
Authors: Piero Procacci
Source: Current Opinion in Structural Biology (review, 2020-11-19)
Field: Materials Science — Materials Chemistry

Methodological uncertainties in drug-receptor binding free energy predictions based on classical molecular dynamics Free energy perturbation ; Molecular dynamics ; Umbrella sampling ; Computer science ; Drug discovery ; Workflow ; Rare events ; Docking (animal) ; Sampling (signal processing) ; Profiling (computer programming) ; Computational biology ; Statistical physics ; Chemistry ; Computational chemistry ; Physics ; Biology ; Medicine ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Hamail Ayaz | Muhammad Ahmad | Manuel Mazzara | Ahmed Sohaib
Source: Applied Sciences (article, 2020-11-03)
Field: Chemistry — Analytical Chemistry

Hyperspectral Imaging for Minced Meat Classification Using Nonlinear Deep Features Minced meat substitution is one of the most common forms of food fraud in the meat industry. Recently, Hyperspectral Imaging (HSI) has been used for the classification and identification of minced meat types. However, conventional methods are based only on spectral information and ignore the spatial variability of the data. Moreover, these methods first tend to reduce the size of the data, which to some extent ignores the abstract level information and does not preserve the spatial information. Therefore, this work proposes a novel Isos-bestic wavelength reduction method for the different minced meat types, by retaining only Myoglobin pigments (Mb) in the meat spectra. A total of 60 HSI cubes are acquired using Fx 10 Hyperspectral sensor. For each HSI cube, a set of preprocessing schemes is applied to extract the Region of Interest (ROI) and spectral preprocessing, i.e., Golay filtering. Later, these preprocessed HSI cubes are fed into a 3D-Convolutional Neural Network (3D-CNN) model for nonlinear feature extraction and classification. The proposed pipeline outperformed several state-of-the-art methods, with an overall accuracy of 94.0%. Hyperspectral imaging ; Pattern recognition (psychology) ; Artificial intelligence ; Computer science ; Preprocessor ; Convolutional neural network ; Computer vision Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Identification and Quantification in Food
Authors: Alena Vishina | Olga Yu. Vekilova | Torbjörn Björkman | Anders Bergman | Heike C. Herper | Olle Eriksson
Source: Physical review. B./Physical review. B (article, 2020-03-03)
Field: Materials Science — Electronic, Optical and Magnetic Materials

High-throughput and data-mining approach to predict new rare-earth free permanent magnets We present an application of a high-throughput search of new rare-earth free permanent magnets focusing on $3d\text{\ensuremath{-}}5d$ transition metal compounds. The search involved a part of the Inorganic Crystal Structure Database, together with tailored search criteria and electronic structure calculations of magnetic properties. Our results suggest that it is possible to find candidates for rare-earth free permanent magnets using a data-mining/data-filtering approach. The most promising candidates identified here are ${\mathrm{Pt}}_{2}\mathrm{FeNi}$, ${\mathrm{Pt}}_{2}\mathrm{FeCu}$, and ${\mathrm{W}}_{2}{\mathrm{FeB}}_{2}$. We suggest these materials to be a good platform for further investigations in the search of novel rare-earth free permanent magnets. Magnet ; Rare earth ; Throughput ; Materials science ; Computer science ; Mechanical engineering ; Engineering ; Metallurgy Magnetic Properties of Alloys ; Machine Learning in Materials Science ; Magnetic properties of thin films
Authors: Charles C. Davis | Julien Champ | Daniel Park | Ian Breckheimer | Goia de Mattos Lyra | Junxi Xie | Alexis Joly | Dharmesh Tarapore | Aaron M. Ellison | Pierre Bonnet
Source: Frontiers in Plant Science (article, 2020-07-31)
Field: Chemistry — Analytical Chemistry

A New Method for Counting Reproductive Structures in Digitized Herbarium Specimens Using Mask R-CNN Phenology-the timing of life-history events-is a key trait for understanding responses of organisms to climate. The digitization and online mobilization of herbarium specimens is rapidly advancing our understanding of plant phenological response to climate and climatic change. The current practice of manually harvesting data from individual specimens, however, greatly restricts our ability to scale-up data collection. Recent investigations have demonstrated that machine-learning approaches can facilitate this effort. However, present attempts have focused largely on simplistic binary coding of reproductive phenology (e.g., presence/absence of flowers). Here, we use crowd-sourced phenological data of buds, flowers, and fruits from >3,000 specimens of six common wildflower species of the eastern United States (<i>Anemone canadensis</i> L., <i>A. hepatica</i> L., <i>A. quinquefolia</i> L., <i>Trillium erectum</i> L., <i>T. grandiflorum</i> (Michx.) Salisb., and <i>T. undulatum</i> Wild.) to train models using Mask R-CNN to segment and count phenological features. A single global model was able to automate the binary coding of each of the three reproductive stages with >87% accuracy. We also successfully estimated the relative abundance of each reproductive structure on a specimen with ≥90% accuracy. Precise counting of features was also successful, but accuracy varied with phenological stage and taxon. Specifically, counting flowers was significantly less accurate than buds or fruits likely due to their morphological variability on pressed specimens. Moreover, our Mask R-CNN model provided more reliable data than non-expert crowd-sourcers but not botanical experts, highlighting the importance of high-quality human training data. Finally, we also demonstrated the transferability of our model to automated phenophase detection and counting of the three <i>Trillium</i> species, which have large and conspicuously-shaped reproductive organs. These results highlight the promise of our two-phase crowd-sourcing and machine-learning pipeline to segment and count reproductive features of herbarium specimens, thus providing high-quality data with which to investigate plant responses to ongoing climatic change. Herbarium ; Biology ; Computer science ; Artificial intelligence ; Botany Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Remote Sensing in Agriculture