jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jin Xiang Wong | Kampachiro Ogura | Shuxiong Chen | Bernd H. A. Rehm
Source: Frontiers in Bioengineering and Biotechnology (review, 2020-03-04)
Field: Materials Science — Biomaterials

Bioengineered Polyhydroxyalkanoates as Immobilized Enzyme Scaffolds for Industrial Applications Enzymes function as biocatalysts and are extensively exploited in industrial applications. Immobilization of enzymes using support materials has been shown to improve enzyme properties, including stability and functionality in extreme conditions and recyclability in biocatalytic processing. This review focuses on the recent advances utilizing the design space of <i>in vivo</i> self-assembled polyhydroxyalkanoate (PHA) particles as biocatalyst immobilization scaffolds. Self-assembly of biologically active enzyme-coated PHA particles is a one-step <i>in vivo</i> production process, which avoids the costly and laborious <i>in vitro</i> chemical cross-linking of purified enzymes to separately produced support materials. The homogeneous orientation of enzymes densely coating PHA particles enhances the accessibility of catalytic sites, improving enzyme function. The PHA particle technology has been developed into a remarkable scaffolding platform for the design of cost-effective designer biocatalysts amenable toward robust industrial bioprocessing. In this review, the PHA particle technology will be compared to other biological supramolecular assembly-based technologies suitable for <i>in vivo</i> enzyme immobilization. Recent progress in the fabrication of biological particulate scaffolds using enzymes of industrial interest will be summarized. Additionally, we outline innovative approaches to overcome limitations of <i>in vivo</i> assembled PHA particles to enable fine-tuned immobilization of multiple enzymes to enhance performance in multi-step cascade reactions, such as those used in continuous flow bioprocessing. Bioprocess ; Polyhydroxyalkanoates ; Immobilized enzyme ; Nanotechnology ; Biocatalysis ; Materials science ; Synthetic biology ; Biochemical engineering ; Chemistry ; Enzyme ; Chemical engineering ; Biochemistry ; Catalysis ; Computational biology ; Engineering ; Biology biodegradable polymer synthesis and properties ; Enzyme Catalysis and Immobilization ; Electrospun Nanofibers in Biomedical Applications
Authors: Luis Portilla | Jianwen Zhao | Yan Wang | Liping Sun | Fengzhu Li | Malo Robin | Miaomiao Wei | Zheng Cui | Luigi G. Occhipinti | Thomas D. Anthopoulos | Vincenzo Pecunia
Source: ACS Nano (article, 2020-09-14)
Field: Materials Science — Materials Chemistry

Ambipolar Deep-Subthreshold Printed-Carbon-Nanotube Transistors for Ultralow-Voltage and Ultralow-Power Electronics The development of ultralow-power and easy-to-fabricate electronics with potential for large-scale circuit integration (<i>i</i>.<i>e</i>., complementary or complementary-like) is an outstanding challenge for emerging off-the-grid applications, <i>e</i>.<i>g</i>., remote sensing, "place-and-forget", and the Internet of Things. Herein we address this challenge through the development of ambipolar transistors relying on solution-processed polymer-sorted semiconducting carbon nanotube networks (sc-SWCNTNs) operating in the deep-subthreshold regime. Application of self-assembled monolayers at the active channel interface enables the fine-tuning of sc-SWCNTN transistors toward well-balanced ambipolar deep-subthreshold characteristics. The significance of these features is assessed by exploring the applicability of such transistors to complementary-like integrated circuits, with respect to which the impact of the subthreshold slope and flatband voltage on voltage and power requirements is studied experimentally and theoretically. As demonstrated with inverter and NAND gates, the ambipolar deep-subthreshold sc-SWCNTN approach enables digital circuits with complementary-like operation and characteristics including wide noise margins and ultralow operational voltages (≤0.5 V), while exhibiting record-low power consumption (≤1 pW/μm). Among thin-film transistor technologies with minimal material complexity, our approach achieves the lowest energy and power dissipation figures reported to date, which are compatible with and highly attractive for emerging off-the-grid applications. Transistor ; Subthreshold conduction ; Ambipolar diffusion ; Materials science ; Nanotechnology ; Electronics ; Carbon nanotube field-effect transistor ; Optoelectronics ; Electronic circuit ; Electrical engineering ; Subthreshold slope ; Threshold voltage ; Computer science ; Voltage ; Field-effect transistor ; Physics ; Engineering ; Electron Carbon Nanotubes in Composites ; Advanced Memory and Neural Computing ; Organic Electronics and Photovoltaics
Authors: Bohua Ren | Eric Croiset | Luis Ricardez‐Sandoval
Source: Journal of Catalysis (article, 2020-02-14)
Field: Materials Science — Materials Chemistry

A theoretical study on CO2 electrolysis through synergistic manipulation of Ni/Mn doping and oxygen vacancies in La(Sr)FeO3 Chemistry ; Oxygen ; Electrolysis ; Doping ; Density functional theory ; Catalysis ; Polarization (electrochemistry) ; Oxide ; Adsorption ; Inorganic chemistry ; Oxygen evolution ; Physical chemistry ; Computational chemistry ; Materials science ; Electrode ; Electrochemistry Advancements in Solid Oxide Fuel Cells ; Chemical Looping and Thermochemical Processes ; Magnetic and transport properties of perovskites and related materials
Authors: Chandrasekaran Dhanush | Mathur Gopalakrishnan Sethuraman
Source: Diamond and Related Materials (article, 2020-07-02)
Field: Materials Science — Materials Chemistry

Influence of phyto-derived nitrogen doped carbon dots from the seeds of Azadirachta indica on the NaBH4 reduction of Safranin-O dye Safranin ; Raman spectroscopy ; Nuclear chemistry ; Chemistry ; Azadirachta ; Carbon fibers ; X-ray photoelectron spectroscopy ; Zeta potential ; Fluorescence ; Materials science ; Chemical engineering ; Nanotechnology ; Nanoparticle ; Botany ; Composite number Carbon and Quantum Dots Applications ; Nanocluster Synthesis and Applications ; Quantum Dots Synthesis And Properties
Authors: André Bloesser | Hannah Kurz | Jana Timm | Florian Wittkamp | Christopher Simon | Shusaku Hayama | Birgit Weber | Ulf‐Peter Apfel | Roland Marschall
Source: ACS Applied Nano Materials (article, 2020-10-26)
Field: Materials Science — Materials Chemistry

Tailoring the Size, Inversion Parameter, and Absorption of Phase-Pure Magnetic MgFe<sub>2</sub>O<sub>4</sub> Nanoparticles for Photocatalytic Degradations Phase-pure magnesium ferrite (MgFe2O4) spinel nanocrystals are synthesized by a fast microwave-assisted route. The elemental composition is optimized via the ratio of the precursor mixture and controlled by energy-dispersive X-ray spectroscopy. Fine-tuning of the magnetic properties without changing the overall elemental composition is demonstrated by superconducting quantum interference device (SQUID) magnetometry and Mössbauer spectroscopy. Together with X-ray absorption spectroscopy and X-ray emission spectroscopy, we confirm that the degree of cation inversion is altered by thermal annealing. We can correlate the magnetic properties with both the nanosize influence and the degree of inversion. The resulting nonlinear course of saturation magnetization (Ms) in correlation with the particle diameter allows to decouple crystallite size and saturation magnetization, by this providing a parameter for the production of very small nanoparticles with high Ms with great potential for magnetic applications like ferrofluids or targeted drug delivery. Our results also suggest that the optical band gap of MgFe2O4 is considerably larger than the fundamental electronic band gap because of the d5 electronic configuration of the iron centers. The presented different electronic transitions contributing to the absorption of visible light are the explanation for the large dissent among the band gaps and band potentials found in the literature. Materials science ; Band gap ; Spectroscopy ; Crystallite ; Absorption spectroscopy ; Analytical Chemistry (journal) ; Magnetization ; Optoelectronics ; Chemistry ; Optics ; Magnetic field Magnetic Properties and Synthesis of Ferrites ; Iron oxide chemistry and applications ; Multiferroics and related materials
Authors: Jingshuo Gao | Qin Xiong | Xueting Wu | Jiajie Deng | Xiaocui Zhang | Xiaohu Zhao | Pengchi Deng | Zhipeng Yu
Source: Communications Chemistry (article, 2020-03-04)
Field: Chemistry — Organic Chemistry

Direct ring-strain loading for visible-light accelerated bioorthogonal ligation via diarylsydnone-dibenzo[b,f ][1,4,5]thiadiazepine photo-click reactions Ultra-fast and selective covalent-bond forming reactions with spatiotemporal controllability are foundational for developing a bioorthogonal approach with high manipulability. However, it is challenging to exploit a reporter functional group to achieve these requirements simultaneously. Here, 11H-Dibenzo[c,f][1,2]diazepine and a set of heterocyclic analogues are investigated for both their photo-switching natures and their ability to serve as dipolarophiles in photo-click reactions with diarylsydnone. Sulfur-containing dibenzothiadiazepine (DBTD) is discovered to be an excellent chemical reporter in cycloaddition with visible-light excitation for in-situ ring-strain loading via its (Z) → (E) photo-isomerization. The bioorthogonal utility of the DBTD tag in spatiotemporally controlled ligation for protein modifications on live cells is also demonstrated. Bioorthogonal chemistry ; Cycloaddition ; Covalent bond ; Chemistry ; Click chemistry ; Ring (chemistry) ; Functional group ; Isomerization ; Combinatorial chemistry ; Strain (injury) ; Photochemistry ; Catalysis ; Organic chemistry ; Biology Click Chemistry and Applications ; Chemical Synthesis and Analysis ; Advanced biosensing and bioanalysis techniques
Authors: Yang Chen | Liang Dang | Chun‐Yu Ho
Source: Nature Communications (article, 2020-05-08)
Field: Chemistry — Organic Chemistry

NHC-Ni catalyzed enantioselective synthesis of 1,4-dienes by cross-hydroalkenylation of cyclic 1,3-dienes and heterosubstituted terminal olefins Enantioenriched 1,4-dienes are versatile building blocks in asymmetric synthesis, therefore their efficient synthesis directly from chemical feedstock is highly sought after. Here, we show an enantioselective cross-hydroalkenylation of cyclic 1,3-diene and hetero-substituted terminal olefin by using a chiral [NHC-Ni(allyl)]BAr<sup>F</sup> catalyst. Using a structurally flexible chiral C<sub>2</sub> NHC-Ni design is key to access a broad scope of chiral 1,4-diene 3 or 3' with high enantioselectivity. This study also offers insights on how to regulate chiral C<sub>2</sub> NHC-Ni(II) 1,3-allylic shift on cyclic diene 1 and to build sterically more hindered endocyclic chiral allylic structures on demand. Enantioselective synthesis ; Diene ; Allylic rearrangement ; Steric effects ; Olefin fiber ; Catalysis ; Chemistry ; Combinatorial chemistry ; Stereochemistry ; Organic chemistry N-Heterocyclic Carbenes in Organic and Inorganic Chemistry ; Asymmetric Hydrogenation and Catalysis ; Synthetic Organic Chemistry Methods
Authors: Xinmiao Niu | Zhuoran Kuang | Miquel Planells | Yuanyuan Guo | Neil Robertson | Andong Xia
Source: Physical Chemistry Chemical Physics (article, 2020-01-01)
Field: Chemistry — Physical and Theoretical Chemistry

Electron-donating strength dependent symmetry breaking charge transfer dynamics of quadrupolar molecules The excited state symmetry breaking charge transfer (SBCT) dynamics of two diacetylide-triphenylamine (DATPA) derivatives with different electron-donating abilities are investigated by femtosecond transient absorption and fluorescence spectroscopy. By tracking the evolution of the excited states by transient absorption spectra and the kinetics of the instantaneous emission dipole moments obtained from transient fluorescence spectroscopy, it is found that, in nonpolar solvent, the relaxed S<sub>1</sub> state is quadrupolar with little change of emission dipole moments for the two molecules within 30 ps, whereas in polar solvent, the quadrupolar state evolves to a symmetry broken S<sub>1</sub> state, in which, the emission dipole moment exhibits a fast reduction in the first few picoseconds. The larger reduction in emission transition dipole moment for the molecule with stronger electron-donating methoxy groups indicates a larger extent of symmetry breaking compared with the one with weak electron-donating methyl groups. Consequently, we revealed that the magnitude of symmetry breaking can be tuned by changing the electron-donors in quadrupolar molecules. Charge (physics) ; Dynamics (music) ; Chemical physics ; Symmetry (geometry) ; Molecule ; Electron transfer ; Transfer (computing) ; Symmetry breaking ; Electron ; Molecular dynamics ; Chemistry ; Materials science ; Physics ; Molecular physics ; Condensed matter physics ; Computational chemistry ; Physical chemistry ; Quantum mechanics Photochemistry and Electron Transfer Studies ; Spectroscopy and Quantum Chemical Studies ; Molecular Junctions and Nanostructures
Authors: Ryan J. Milcarek | Vincent P. DeBiase | Jeongmin Ahn
Source: Energy (article, 2020-02-13)
Field: Materials Science — Materials Chemistry

Investigation of startup, performance and cycling of a residential furnace integrated with micro-tubular flame-assisted fuel cells for micro-combined heat and power Power density ; Stack (abstract data type) ; Combustion ; Materials science ; NOx ; Ignition system ; Nuclear engineering ; Cathode ; Waste management ; Chemical engineering ; Chemistry ; Power (physics) ; Electrical engineering ; Thermodynamics ; Engineering Advancements in Solid Oxide Fuel Cells ; Fuel Cells and Related Materials ; Electrocatalysts for Energy Conversion
Authors: Maria Ximena Bastidas-Rodríguez | Luisa F. Polanía | Adrien Gruson | Flavio Prieto
Source: Engineering Failure Analysis (article, 2020-04-17)
Field: Materials Science — Materials Chemistry

Deep Learning for fractographic classification in metallic materials Fracture (geology) ; Convolutional neural network ; Artificial intelligence ; Deep learning ; Pattern recognition (psychology) ; Materials science ; Fractography ; Texture (cosmology) ; Computer science ; Scale (ratio) ; Failure mode and effects analysis ; Wavelet transform ; Wavelet ; Image (mathematics) ; Composite material Material Properties and Failure Mechanisms ; Drilling and Well Engineering ; Image Processing and 3D Reconstruction
Authors: Satoshi Watanabe | Wenwen Li | Wonseok Jeong | Dong-Heon Lee | Kōji Shimizu | Emi Mimanitani | Yasunobu Ando | Seungwu Han
Source: Journal of Physics Energy (article, 2020-11-05)
Field: Materials Science — Materials Chemistry

High-dimensional neural network atomic potentials for examining energy materials: some recent simulations Abstract Owing to their simultaneous accuracy and computational efficiency, interatomic potentials machine-learned using first-principles calculation data are promising for investigating phenomena closely related to atomic motion in various energy materials. We have been working with one type of these potentials, high-dimensional (HD) neural network potentials (NNPs), and their applications, but we realized that our current understanding of HD NNPs, e.g. the meaning of the atomic energy mapping, remained insufficient, and that tuning their prediction performance for different target properties/phenomena often requires much trial and error. In this article, we illustrate the usefulness of NNPs through our studies on ion migration and thermal transport in energy and related materials. We also share our experiences with data sampling and training strategies and discuss the meaning of atomic energy mapping in HD NNPs. Energy (signal processing) ; Artificial neural network ; Meaning (existential) ; Atomic energy ; Computer science ; Current (fluid) ; Artificial intelligence ; Physics ; Psychology ; Quantum mechanics Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Thermal properties of materials
Authors: Jan Weinreich | Anton Römer | Martín Leandro Paleico | Jörg Behler
Source: The Journal of Physical Chemistry C (article, 2020-04-09)
Field: Materials Science — Materials Chemistry

Properties of α-Brass Nanoparticles. 1. Neural Network Potential Energy Surface Binary metal clusters are of high interest for applications in heterogeneous catalysis and have received much attention in recent years. To gain insights into their structure and composition at the atomic scale, computer simulations can provide valuable information if reliable interatomic potentials are available. In this paper we describe the construction of a high-dimensional neural network potential (HDNNP) intended for simulations of large brass nanoparticles with thousands of atoms, which is also applicable to bulk α-brass and its surfaces. The HDNNP, which is based on reference data obtained from density-functional theory calculations, is very accurate with a root-mean-square error of 1.7 meV/atom for total energies and 39 meV Å–1 for the forces of structures not included in the training set. The potential has been thoroughly validated for a wide range of energetic and structural properties of bulk α-brass, its surfaces as well as clusters of different size and composition demonstrating its suitability for large-scale molecular dynamics and Monte Carlo simulations with first-principles accuracy. Brass ; Nanoparticle ; Atom (system on chip) ; Molecular dynamics ; Interatomic potential ; Materials science ; Monte Carlo method ; Artificial neural network ; Statistical physics ; Range (aeronautics) ; Binary number ; Density functional theory ; Atomic units ; Chemical physics ; Nanotechnology ; Physics ; Computer science ; Chemistry ; Computational chemistry ; Metallurgy ; Copper ; Mathematics ; Composite material ; Machine learning Machine Learning in Materials Science ; nanoparticles nucleation surface interactions ; Ion-surface interactions and analysis
Authors: Sicong Ma | Cheng Shang | Chuanming Wang | Zhi‐Pan Liu
Source: Chemical Science (article, 2020-01-01)
Field: Chemistry — Inorganic Chemistry

Thermodynamic rules for zeolite formation from machine learning based global optimization While the [TO<sub>4</sub>] tetrahedron packing rule leads to millions of likely zeolite structures, there are currently only 252 types of zeolite frameworks reported after decades of synthetic efforts. The subtle synthetic conditions, <i>e.g.</i> the structure-directing agents, pH and the feed ratio, were often blamed for the limited zeolite types due to the complex kinetics. Here by developing machine learning global optimization techniques, we are now able to establish the global potential energy surface of a typical zeolite system, Si <sub><i>x</i></sub> Al <sub><i>y</i></sub> P <sub><i>z</i></sub> O<sub>2</sub>H <sub><i>y</i>-<i>z</i></sub> with 12 T atoms (T: Si, Al and P) that is the general formula shared by CHA, ATS, ATO and ATV zeolite frameworks. After analyzing more than 10<sup>6</sup> minima data, we identify thermodynamic rules on energetics and local bonding patterns for stable zeolites. These rules provide general guidelines to classify zeolite types and correlate them with synthesis conditions. The machine learning based atomistic simulation thus paves a new way towards rational design and synthesis of stable zeolite frameworks with desirable compositions. Maxima and minima ; Zeolite ; Potential energy surface ; Energetics ; Energy (signal processing) ; Surface (topology) ; Computer science ; Thermodynamics ; Artificial intelligence ; Chemistry ; Nanotechnology ; Materials science ; Physics ; Mathematics ; Quantum mechanics ; Molecule ; Organic chemistry Zeolite Catalysis and Synthesis ; Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Yiqun Wang | Akshay Iyer | Wei Chen | James M. Rondinelli
Source: Applied Physics Reviews (article, 2020-11-06)
Field: Materials Science — Materials Chemistry

Featureless adaptive optimization accelerates functional electronic materials design Electronic materials that exhibit phase transitions between metastable states (e.g., metal-insulator transition materials with abrupt electrical resistivity transformations) are challenging to decode. For these materials, conventional machine learning methods display limited predictive capability due to data scarcity and the absence of features that impede model training. In this article, we demonstrate a discovery strategy based on multi-objective Bayesian optimization to directly circumvent these bottlenecks by utilizing latent variable Gaussian processes combined with high-fidelity electronic structure calculations for validation in the chalcogenide lacunar spinel family. We directly and simultaneously learn phase stability and bandgap tunability from chemical composition alone to efficiently discover all superior compositions on the design Pareto front. Previously unidentified electronic transitions also emerge from our featureless adaptive optimization engine. Our methodology readily generalizes to optimization of multiple properties, enabling co-design of complex multifunctional materials, especially where prior data is sparse. Computer science ; Adaptive optimization ; Materials science ; Nanotechnology ; Programming language Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices ; Chalcogenide Semiconductor Thin Films
Authors: Natalya Sheremetyeva | Michael Lamparski | Colin Daniels | Benoît Van Troeye | Vincent Meunier
Source: Carbon (article, 2020-07-31)
Field: Materials Science — Materials Chemistry

Machine-learning models for Raman spectra analysis of twisted bilayer graphene Raman spectroscopy ; Artificial intelligence ; Bilayer graphene ; Context (archaeology) ; Spectral line ; Raman scattering ; Computer science ; Robustness (evolution) ; Materials science ; Machine learning ; Graphene ; Optics ; Chemistry ; Physics ; Nanotechnology ; Quantum mechanics Graphene research and applications ; Machine Learning in Materials Science ; 2D Materials and Applications
Authors: Janine George | Geoffroy Hautier | Albert P. Bartók | Gábor Csányi | Volker L. Deringer
Source: The Journal of Chemical Physics (article, 2020-07-23)
Field: Materials Science — Materials Chemistry

Combining phonon accuracy with high transferability in Gaussian approximation potential models Machine learning driven interatomic potentials, including Gaussian approximation potential (GAP) models, are emerging tools for atomistic simulations. Here, we address the methodological question of how one can fit GAP models that accurately predict vibrational properties in specific regions of configuration space while retaining flexibility and transferability to others. We use an adaptive regularization of the GAP fit that scales with the absolute force magnitude on any given atom, thereby exploring the Bayesian interpretation of GAP regularization as an "expected error" and its impact on the prediction of physical properties for a material of interest. The approach enables excellent predictions of phonon modes (to within 0.1 THz-0.2 THz) for structurally diverse silicon allotropes, and it can be coupled with existing fitting databases for high transferability across different regions of configuration space, which we demonstrate for liquid and amorphous silicon. These findings and workflows are expected to be useful for GAP-driven materials modeling more generally. Transferability ; Phonon ; Gaussian ; Workflow ; Interatomic potential ; Regularization (linguistics) ; Computation ; Gaussian process Machine Learning in Materials Science ; Thermal properties of materials ; Quantum many-body systems