jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Thomas A. Swift | Thomas A. A. Oliver | M. Carmen Galán | Heather M. Whitney
Source: Interface Focus (review, 2018-12-14)
Field: Materials Science — Materials Chemistry

Functional nanomaterials to augment photosynthesis: evidence and considerations for their responsible use in agricultural applications At the current population growth rate, we will soon be unable to meet increasing food demands. As a consequence of this potential problem, considerable efforts have been made to enhance crop productivity by breeding, genetics and improving agricultural practices. While these techniques have traditionally been successful, their efficacy since the 'green revolution' has begun to significantly plateau. This stagnation of gains combined with the negative effects of climate change on crop yields has prompted researchers to develop novel and radical methods to increase crop productivity. Recent work has begun exploring the use of nanomaterials as synthetic probes to augment how plants use light. Photosynthesis in crops is often limited by their ability to absorb and exploit solar energy for photochemistry. The capacity to interact with and optimize how plants use light has the potential to increase the productivity of crops and enable the tailoring of crops for different environments and to compensate for predicted climate changes. Advances in the synthesis and surface modification of nanomaterials have overcome previous drawbacks and renewed their potential use as synthetic probes to enhance crop yields. Here, we review the current applications of functional nanomaterials in plants and will make an argument for the continued development of promising new nanomaterials and future applications in agriculture. This will highlight that functional nanomaterials have the clear potential to provide a much-needed route to enhanced future food security. In addition, we will discuss the often-ignored current evidence of nanoparticles present in the environment as well as inform and encourage caution on the regulation of nanomaterials in agriculture. Productivity ; Food security ; Agriculture ; Nanotechnology ; Exploit ; Agricultural productivity ; Computer science ; Biochemical engineering ; Risk analysis (engineering) ; Biotechnology ; Business ; Ecology ; Engineering ; Biology ; Materials science Carbon and Quantum Dots Applications ; Quantum Dots Synthesis And Properties ; Nanoparticles: synthesis and applications
Authors: Qinghua Liu | Xueyao Zhou | Linsen Zhou | Yaolong Zhang | Xuan Luo | Hua Guo | Bin Jiang
Source: The Journal of Physical Chemistry C (article, 2018-01-02)
Field: Materials Science — Materials Chemistry

Constructing High-Dimensional Neural Network Potential Energy Surfaces for Gas–Surface Scattering and Reactions While the ab initio molecular dynamics (AIMD) approach to gas–surface interaction has been instrumental in exploring important issues such as energy transfer and reactivity, it is only amenable to short-time events and a limited number of trajectories because of the on-the-fly nature of the density functional theory (DFT) calculations. Here, we report a high-dimensional global reactive potential energy surface (PES) constructed with high fidelity from judiciously placed DFT points, using a machine learning method; and it is orders-of-magnitude more efficient than AIMD in dynamical calculations and can be employed in various simulations without performing additional electronic structure calculations. Importantly, the surface atoms are included in such a PES, which provides a unique platform for studying energy transfer and scattering/reaction of the impinging molecule on the solid surface on an equal footing. Potential energy surface ; Surface (topology) ; Density functional theory ; Ab initio ; Scattering ; Energy (signal processing) ; High fidelity ; Molecule ; Molecular dynamics ; Electronic structure ; On the fly ; Chemical physics ; Physics ; Materials science ; Statistical physics ; Computational physics ; Chemistry ; Computational chemistry ; Computer science ; Quantum mechanics ; Mathematics ; Geometry Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Quantum, superfluid, helium dynamics
Authors: Zhenguo Gao | Yuanyi Wu | Ying Bao | Junbo Gong | Jingkang Wang | Sohrab Rohani
Source: Crystal Growth & Design (article, 2018-07-02)
Field: Materials Science — Materials Chemistry

Image Analysis for In-line Measurement of Multidimensional Size, Shape, and Polymorphic Transformation of <scp>l</scp>-Glutamic Acid Using Deep Learning-Based Image Segmentation and Classification In situ tracking of the crystallization process through image segmentation has been developed and has encountered many challenges including improvement of in situ image quality, optimization of algorithms, and increased computation efficiency. In this study, a new method based on computer vision was proposed using the state-of-the-art deep learning technology to track crystal individuals. For the model compound l-glutamic acid, two polymorphic forms with different morphologies were segmented and classified during a seeded polymorphic transformation process. Information such as counts, size, surface area, crystal size distribution, and morphology of α- and β-form crystals was extracted for the individual crystals during the process. A comparative analysis was conducted with traditional process analytical technologies such as in situ Raman and focus beam reflection measurement. Results show a high accuracy of segmentation and classification technique and a reliable tracking of crystals evolution. The image processing speed of up to 10 frames per second makes the proposed approach suitable for in situ tracking and control of crystallization and particulate processes. Our work in this study attempts to bridge the gap between the advanced imaging analysis technology that is available today and the specific needs of solution crystallization, to track, count, and measure the individual crystals. Artificial intelligence ; Crystallization ; Segmentation ; Computer science ; Image segmentation ; Tracking (education) ; Transformation (genetics) ; Deep learning ; Image processing ; Process (computing) ; Materials science ; Pattern recognition (psychology) ; Computer vision ; Image (mathematics) ; Chemistry Crystallization and Solubility Studies ; Enzyme Structure and Function ; Image Processing Techniques and Applications
Authors: Volker L. Deringer | Davide Μ. Proserpio | Gábor Cśanyi | Chris J. Pickard
Source: Faraday Discussions (article, 2018-01-01)
Field: Materials Science — Materials Chemistry

Data-driven learning and prediction of inorganic crystal structures Crystal structure prediction algorithms, including ab initio random structure searching (AIRSS), are intrinsically limited by the huge computational cost of the underlying quantum-mechanical methods. We have recently shown that a novel class of machine learning (ML) based interatomic potentials can provide a way out: by performing a high-dimensional fit to the ab initio energy landscape, these potentials reach comparable accuracy but are orders of magnitude faster. In this paper, we develop our approach, dubbed Gaussian approximation potential-based random structure searching (GAP-RSS), towards a more general tool for exploring configuration spaces and predicting structures. We present a GAP-RSS interatomic potential model for elemental phosphorus, which identifies and correctly "learns" the orthorhombic black phosphorus (A17) structure without prior knowledge of any crystalline allotropes. Using the tubular structure of fibrous phosphorus as an example, we then discuss the limits of free searching, and discuss a possible way forward that combines a recently proposed fragment analysis with GAP-RSS. Examples of possible tubular (1D) and extended (3D) hypothetical allotropes of phosphorus as found by GAP-RSS are discussed. We believe that in the future, ML potentials could become versatile and routine computational tools for materials discovery and design. Crystal structure prediction ; Crystal (programming language) ; On the fly ; Interatomic potential ; Crystal structure ; Computer science ; Energy (signal processing) ; Machine learning ; Materials science ; Statistical physics ; Artificial intelligence ; Molecular dynamics ; Crystallography ; Chemistry ; Physics ; Computational chemistry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Crystallography and molecular interactions
Authors: Stephen B. H. Kent
Source: Protein Science (review, 2018-10-22)
Field: Materials Science — Materials Chemistry

Novel protein science enabled by total chemical synthesis Chemical synthesis is a well-established method for the preparation in the research laboratory of multiple-tens-of-milligram amounts of correctly folded, high purity protein molecules. Chemically synthesized proteins enable a broad spectrum of novel protein science. Racemic mixtures consisting of d-protein and l-protein enantiomers facilitate crystallization and determination of protein structures by X-ray diffraction. d-Proteins enable the systematic development of unnatural mirror image protein molecules that bind with high affinity to natural protein targets. The d-protein form of a therapeutic target can also be used to screen natural product libraries to identify novel small molecule leads for drug development. Proteins with novel polypeptide chain topologies including branched, circular, linear-loop, and interpenetrating polypeptide chains can be constructed by chemical synthesis. Medicinal chemistry can be applied to optimize the properties of therapeutic protein molecules. Chemical synthesis has been used to redesign glycoproteins and for the a priori design and construction of covalently constrained novel protein scaffolds not found in nature. Versatile and precise labeling of protein molecules by chemical synthesis facilitates effective application of advanced physical methods including multidimensional nuclear magnetic resonance and time-resolved FTIR for the elucidation of protein structure-activity relationships. The chemistries used for total synthesis of proteins have been adapted to making artificial molecular devices and protein-inspired nanomolecular constructs. Research to develop mirror image life in the laboratory is in its very earliest stages, based on the total chemical synthesis of d-protein forms of polymerase enzymes. Chemistry ; Small molecule ; Molecule ; Target protein ; Protein engineering ; Native chemical ligation ; Chemical biology ; Chemical synthesis ; Protein biosynthesis ; Biomolecule ; Combinatorial chemistry ; Chemical modification ; Chemical space ; Protein crystallization ; Protein design ; Enantiomer ; Protein structure ; Drug discovery ; Crystallization ; Biochemistry ; Enzyme ; Stereochemistry ; Organic chemistry Enzyme Structure and Function ; Chemical Synthesis and Analysis ; RNA and protein synthesis mechanisms
Authors: Alexander S. Erofeev | Petr V. Gorelkin | Anastasiia S. Garanina | Anna V. Alova | Maria V. Efremova | Nataliya S. Vorobyeva | Christopher R.W. Edwards | Yuri E. Korchev | Alexander G. Majouga
Source: Scientific Reports (article, 2018-05-04)
Field: Chemistry — Electrochemistry

Novel method for rapid toxicity screening of magnetic nanoparticles Iron oxide nanoparticles have attracted a great deal of research interest and have been widely used in bioscience and clinical research including as contrast agents for magnetic resonance imaging, hyperthermia and magnetic field assisted radionuclide therapy. It is therefore important to develop methods, which can provide high-throughput screening of biological responses that can predict toxicity. The use of nanoelectrodes for single cell analysis can play a vital role in this process by providing relatively fast, comprehensive, and cost-effective assessment of cellular responses. We have developed a new method for in vitro study of the toxicity of magnetic nanoparticles (NP) based on the measurement of intracellular reactive oxygen species (ROS) by a novel nanoelectrode. Previous studies have suggested that ROS generation is frequently observed with NP toxicity. We have developed a stable probe for measuring intracellular ROS using platinized carbon nanoelectrodes with a cavity on the tip integrated into a micromanipulator on an upright microscope. Our results show a significant difference for intracellular levels of ROS measured in HEK293 and LNCaP cancer cells before and after exposure to 10 nm size iron oxide NP. These results are markedly different from ROS measured after cell incubation with the same concentration of NP using standard methods where no differences have been detected. In summary we have developed a label-free method for assessing nanoparticle toxicity using the rapid (less than 30 minutes) measurement of ROS with a novel nanoelectrode. Toxicity ; Reactive oxygen species ; Intracellular ; Nanoparticle ; Nanotechnology ; Iron oxide nanoparticles ; Biophysics ; Nanotoxicology ; Chemistry ; Materials science ; Biology ; Biochemistry Electrochemical Analysis and Applications ; Nanoparticles: synthesis and applications ; Neuroscience and Neural Engineering
Authors: Chen Zhao | Yuqing Liu | Stephen Beirne | Joselito M. Razal | Jun Chen
Source: Advanced Materials Technologies (article, 2018-06-19)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Recent Development of Fabricating Flexible Micro‐Supercapacitors for Wearable Devices Abstract The rapid development in miniaturized personal wearable devices has significantly stimulated the pursuit for ultrathin, flexible, and lightweight energy storage devices. As one of the most promising power supply devices, supercapacitors have attracted great interest owing to their long operating lifetime, high power density, and fast charge–discharge rate. However, conventional supercapacitors pose challenges when being integrated with miniaturized wearable devices. In this regard, micro‐supercapacitors with unique in‐plane interdigitated electrode design provide flexibility and ease of integration with wearable and portable electronics. This review discusses the recent progress on fabrication strategies of interdigitated electrode arrays for flexible supercapacitors. Strategies toward their practical applications for powering integrated systems are presented. Finally, the future perspectives of flexible micro‐supercapacitor are discussed. Supercapacitor ; Wearable computer ; Flexibility (engineering) ; Wearable technology ; Electronics ; Energy storage ; Nanotechnology ; Fabrication ; Materials science ; Electrical engineering ; Computer science ; Power (physics) ; Engineering ; Electrode ; Embedded system ; Capacitance Supercapacitor Materials and Fabrication ; Advanced Sensor and Energy Harvesting Materials ; Advanced battery technologies research
Authors: Changjian Xie | Xiaolei Zhu | David R. Yarkony | Hua Guo
Source: The Journal of Chemical Physics (article, 2018-10-09)
Field: Materials Science — Materials Chemistry

Permutation invariant polynomial neural network approach to fitting potential energy surfaces. IV. Coupled diabatic potential energy matrices A machine learning method is proposed for representing the elements of diabatic potential energy matrices (PEMs) with high fidelity. This is an extension of the so-called permutation invariant polynomial-neural network (PIP-NN) method for representing adiabatic potential energy surfaces. While for one-dimensional irreducible representations the diagonal elements of a diabatic PEM are invariant under exchange of identical nuclei in a molecular system, the off-diagonal elements require special symmetry consideration, particularly in the presence of a conical intersection. A multiplicative factor is introduced to take into consideration the particular symmetry properties while maintaining the PIP-NN framework. We demonstrate here that the extended PIP-NN approach is accurate in representing diabatic PEMs, as evidenced by small fitting errors and by the reproduction of absorption spectra and product branching ratios in both H2O(X̃/B̃) and NH3(X̃/Ã) non-adiabatic photodissociation. Diabatic ; Adiabatic process ; Invariant (physics) ; Diagonal ; Potential energy ; Chemistry ; Physics ; Quantum mechanics ; Mathematics ; Geometry Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Computational Drug Discovery Methods
Authors: So Fujikake | Volker L. Deringer | Tae Hoon Lee | Marcin Krynski | Stephen R. Elliott | Gábor Csányi
Source: The Journal of Chemical Physics (article, 2018-03-16)
Field: Materials Science — Materials Chemistry

Gaussian approximation potential modeling of lithium intercalation in carbon nanostructures We demonstrate how machine-learning based interatomic potentials can be used to model guest atoms in host structures. Specifically, we generate Gaussian approximation potential (GAP) models for the interaction of lithium atoms with graphene, graphite, and disordered carbon nanostructures, based on reference density functional theory data. Rather than treating the full Li-C system, we demonstrate how the energy and force differences arising from Li intercalation can be modeled and then added to a (prexisting and unmodified) GAP model of pure elemental carbon. Furthermore, we show the benefit of using an explicit pair potential fit to capture "effective" Li-Li interactions and to improve the performance of the GAP model. This provides proof-of-concept for modeling guest atoms in host frameworks with machine-learning based potentials and in the longer run is promising for carrying out detailed atomistic studies of battery materials. Gaussian ; Density functional theory ; Lithium (medication) ; Interatomic potential ; Intercalation (chemistry) ; Carbon fibers ; Molecular dynamics ; Battery (electricity) ; Coherent potential approximation Machine Learning in Materials Science ; Advancements in Battery Materials ; Advanced Chemical Physics Studies
Authors: Olusegun Fatoba | R. Leiva‐García | Sergey V. Lishchuk | N.O. Larrosa | R. Akid
Source: Corrosion Science (article, 2018-03-20)
Field: Materials Science — Materials Chemistry

Simulation of stress-assisted localised corrosion using a cellular automaton finite element approach Corrosion ; Finite element method ; Cellular automaton ; Stress (linguistics) ; Materials science ; Structural engineering ; Computer science ; Composite material ; Engineering ; Algorithm Corrosion Behavior and Inhibition ; Concrete Corrosion and Durability ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: James E. Hallett | Francesco Turci | C. Patrick Royall
Source: Nature Communications (article, 2018-08-10)
Field: Materials Science — Materials Chemistry

Local structure in deeply supercooled liquids exhibits growing lengthscales and dynamical correlations Glasses are among the most widely used of everyday materials, yet the process by which a liquid's viscosity increases by 14 decades to become a glass remains unclear, as often contradictory theories provide equally good descriptions of the available data. Knowledge of emergent lengthscales and higher-order structure could help resolve this, but this requires time-resolved measurements of dense particle coordinates-previously only obtained over a limited time interval. Here we present an experimental study of a model colloidal system over a dynamic window significantly larger than previous measurements, revealing structural ordering more strongly linked to dynamics than previously found. Furthermore we find that immobile regions and domains of local structure grow concurrently with density, and that these regions have low configurational entropy. We thus show that local structure plays an important role at deep supercooling, consistent with a thermodynamic interpretation of the glass transition rather than a principally dynamic description. Supercooling ; Statistical physics ; Glass transition ; Configuration entropy ; Local structure ; Entropy (arrow of time) ; Chemical physics ; Physics ; Materials science ; Thermodynamics Material Dynamics and Properties ; Liquid Crystal Research Advancements ; Theoretical and Computational Physics
Authors: Dennis Thomas | Jordan N. Smith | Brian D. Thrall | Donald R. Baer | Hadley Jolley | Prabhakaran Munusamy | Vamsi Kodali | Philip Demokritou | Joel M. Cohen | Justin Teeguarden
Source: Particle and Fibre Toxicology (article, 2018-01-25)
Field: Materials Science — Materials Chemistry

ISD3: a particokinetic model for predicting the combined effects of particle sedimentation, diffusion and dissolution on cellular dosimetry for in vitro systems By combining experiments and modeling, we were able to quantify the influence of proteins on silver particle solubility, determine the relative amounts of silver ions and particles in exposed cells, and demonstrate the influence of particle size changes resulting from dissolution on particle delivery to cells in culture. ISD3 is modular and can be adapted to new applications by replacing descriptions of dissolution, sedimentation and boundary conditions with those appropriate for particles other than silver. Dissolution ; Dosimetry ; Particle (ecology) ; Particle size ; Materials science ; Diffusion ; Nanotechnology ; Chemistry ; Physics ; Nuclear medicine ; Thermodynamics ; Medicine ; Biology ; Physical chemistry Nanoparticles: synthesis and applications ; Healthcare and Environmental Waste Management ; Field-Flow Fractionation Techniques
Authors: Lizhong He | Jinyun Yuan | Nan Xia | Lingwen Liao | Xu Liu | Zibao Gan | Chengming Wang | Jinlong Yang | Zhikun Wu
Source: Journal of the American Chemical Society (article, 2018-02-22)
Field: Materials Science — Materials Chemistry

Kernel Tuning and Nonuniform Influence on Optical and Electrochemical Gaps of Bimetal Nanoclusters Fine tuning nanoparticles with atomic precision is exciting and challenging and is critical for tuning the properties, understanding the structure-property correlation and determining the practical applications of nanoparticles. Some ultrasmall thiolated metal nanoparticles (metal nanoclusters) have been shown to be precisely doped, and even the protecting staple metal atom could be precisely reduced. However, the precise addition or reduction of the kernel atom while the other metal atoms in the nanocluster remain the same has not been successful until now, to the best of our knowledge. Here, by carefully selecting the protecting ligand with adequate steric hindrance, we synthesized a novel nanocluster in which the kernel can be regarded as that formed by the addition of two silver atoms to both ends of the Pt@Ag<sub>12</sub> icosohedral kernel of the Ag<sub>24</sub>Pt(SR)<sub>18</sub> (SR: thiolate) nanocluster, as revealed by single crystal X-ray crystallography. Interestingly, compared with the previously reported Ag<sub>24</sub>Pt(SR)<sub>18</sub> nanocluster, the as-obtained novel bimetal nanocluster exhibits a similar absorption but a different electrochemical gap. One possible explanation for this result is that the kernel tuning does not essentially change the electronic structure, but obviously influences the charge on the Pt@Ag<sub>12</sub> kernel, as demonstrated by natural population analysis, thus possibly resulting in the large electrochemical gap difference between the two nanoclusters. This work not only provides a novel strategy to tune metal nanoclusters but also reveals that the kernel change does not necessarily alter the optical and electrochemical gaps in a uniform manner, which has important implications for the structure-property correlation of nanoparticles. Nanoclusters ; Chemistry ; Bimetal ; Nanoparticle ; Atom (system on chip) ; Nanotechnology ; Electrochemistry ; Chemical physics ; Kernel (algebra) ; Metal ; Materials science ; Electrode ; Physical chemistry ; Computer science ; Organic chemistry Nanocluster Synthesis and Applications ; Gold and Silver Nanoparticles Synthesis and Applications ; Advanced Nanomaterials in Catalysis