jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jacob L. Beckham | Kevin M. Wyss | Yunchao Xie | Emily A. McHugh | John T. Li | Paul A. Advincula | Weiyin Chen | Jian Lin | James M. Tour
Source: Advanced Materials (article, 2022-01-22)
Field: Materials Science — Materials Chemistry

Machine Learning Guided Synthesis of Flash Graphene Advances in nanoscience have enabled the synthesis of nanomaterials, such as graphene, from low-value or waste materials through flash Joule heating. Though this capability is promising, the complex and entangled variables that govern nanocrystal formation in the Joule heating process remain poorly understood. In this work, machine learning (ML) models are constructed to explore the factors that drive the transformation of amorphous carbon into graphene nanocrystals during flash Joule heating. An XGBoost regression model of crystallinity achieves an r<sup>2</sup> score of 0.8051 ± 0.054. Feature importance assays and decision trees extracted from these models reveal key considerations in the selection of starting materials and the role of stochastic current fluctuations in flash Joule heating synthesis. Furthermore, partial dependence analyses demonstrate the importance of charge and current density as predictors of crystallinity, implying a progression from reaction-limited to diffusion-limited kinetics as flash Joule heating parameters change. Finally, a practical application of the ML models is shown by using Bayesian meta-learning algorithms to automatically improve bulk crystallinity over many Joule heating reactions. These results illustrate the power of ML as a tool to analyze complex nanomanufacturing processes and enable the synthesis of 2D crystals with desirable properties by flash Joule heating. Joule heating ; Materials science ; Graphene ; Crystallinity ; Flash (photography) ; Nanocrystal ; Nanotechnology ; Joule effect ; Computer science ; Composite material ; Physics Graphene research and applications ; Machine Learning in Materials Science ; Thermal properties of materials
Authors: Yinghui Guo | Mingbo Pu | Fei Zhang | Mingfeng Xu | Xiong Li | Xiaoliang Ma | Xiangang Luo
Source: Photonics Insights (article, 2022-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Classical and generalized geometric phase in electromagnetic metasurfaces The geometric phase concept has profound implications in many branches of physics, from condensed matter physics to quantum systems. Although geometric phase has a long research history, novel theories, devices, and applications are constantly emerging with developments going down to the subwavelength scale. Specifically, as one of the main approaches to implement gradient phase modulation along a thin interface, geometric phase metasurfaces composed of spatially rotated subwavelength artificial structures have been utilized to construct various thin and planar meta-devices. In this paper, we first give a simple overview of the development of geometric phase in optics. Then, we focus on recent advances in continuously shaped geometric phase metasurfaces, geometric-dynamic composite phase metasurfaces, and nonlinear and high-order linear Pancharatnam-Berry phase metasurfaces. Finally, conclusions and outlooks for future developments are presented. Geometric phase ; Phase (matter) ; Planar ; Focus (optics) ; Physics ; Nonlinear system ; Geometric modeling ; Geometrical optics ; Theoretical physics ; Computer science ; Optics ; Geometry ; Mathematics ; Quantum mechanics ; Computer graphics (images) Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Antenna Design and Analysis
Authors: Hee‐Jae Jeon | Hyung Shik Kim | Euiheon Chung | Dong Yun Lee
Source: Theranostics (review, 2022-01-01)
Field: Materials Science — Materials Chemistry

Nanozyme-based colorimetric biosensor with a systemic quantification algorithm for noninvasive glucose monitoring Diabetes mellitus accompanies an abnormally high glucose level in the bloodstream. Early diagnosis and proper glycemic management of blood glucose are essential to prevent further progression and complications. Biosensor-based colorimetric detection has progressed and shown potential in portable and inexpensive daily assessment of glucose levels because of its simplicity, low-cost, and convenient operation without sophisticated instrumentation. Colorimetric glucose biosensors commonly use natural enzymes that recognize glucose and chromophores that detect enzymatic reaction products. However, many natural enzymes have inherent defects, limiting their extensive application. Recently, nanozyme-based colorimetric detection has drawn attention due to its merits including high sensitivity, stability under strict reaction conditions, flexible structural design with low-cost materials, and adjustable catalytic activities. This review discusses various nanozyme materials, colorimetric analytic methods and mechanisms, recent machine learning based analytic methods, quantification systems, applications and future directions for monitoring and managing diabetes. Biosensor ; Limiting ; Glycemic ; Simplicity ; Computer science ; Diabetes mellitus ; Chemistry ; Nanotechnology ; Medicine ; Biochemistry ; Materials science ; Engineering ; Endocrinology Advanced Nanomaterials in Catalysis ; Electrochemical sensors and biosensors ; Advanced biosensing and bioanalysis techniques
Authors: Ivan S. Novikov | Blazej Grabowski | Fritz Körmann | Alexander V. Shapeev
Source: npj Computational Materials (article, 2022-01-25)
Field: Materials Science — Materials Chemistry

Magnetic Moment Tensor Potentials for collinear spin-polarized materials reproduce different magnetic states of bcc Fe Abstract We present the magnetic Moment Tensor Potentials (mMTPs), a class of machine-learning interatomic potentials, accurately reproducing both vibrational and magnetic degrees of freedom as provided, e.g., from first-principles calculations. The accuracy is achieved by a two-step minimization scheme that coarse-grains the atomic and the spin space. The performance of the mMTPs is demonstrated for the prototype magnetic system bcc iron, with applications to phonon calculations for different magnetic states, and molecular-dynamics simulations with fluctuating magnetic moments. Magnetic moment ; Condensed matter physics ; Tensor (intrinsic definition) ; Electron magnetic dipole moment ; Physics ; Spin (aerodynamics) ; Moment (physics) ; Space (punctuation) ; Neutron magnetic moment ; Magnetic field ; Classical mechanics ; Quantum mechanics ; Magnetic dipole ; Computer science ; Mathematics ; Geometry Machine Learning in Materials Science ; Ferroelectric and Negative Capacitance Devices ; X-ray Diffraction in Crystallography
Authors: David G. Madden | Daniel O’Nolan | Nakul Rampal | Robin Babu | Ceren Çamur | Ali Nabeeh Shakhs | Shi‐Yuan Zhang | Graham A. Rance | Javier Pérez | Nicola Casati | Carlos Cuadrado‐Collados | Denis O’Sullivan | Nicholas Rice | Thomas Gennett | Philip A. Parilla | Sarah Shulda | Katherine E. Hurst | Vitalie Stavila | Mark D. Allendorf | Joaquín Silvestre‐Albero | Alexander C. Forse | Neil R. Champness | Karena W. Chapman | David Fairen‐Jiménez
Source: Journal of the American Chemical Society (article, 2022-07-25)
Field: Chemistry — Inorganic Chemistry

Densified HKUST-1 Monoliths as a Route to High Volumetric and Gravimetric Hydrogen Storage Capacity We are currently witnessing the dawn of hydrogen (H<sub>2</sub>) economy, where H<sub>2</sub> will soon become a primary fuel for heating, transportation, and long-distance and long-term energy storage. Among diverse possibilities, H<sub>2</sub> can be stored as a pressurized gas, a cryogenic liquid, or a solid fuel <i>via</i> adsorption onto porous materials. Metal-organic frameworks (MOFs) have emerged as adsorbent materials with the highest theoretical H<sub>2</sub> storage densities on both a volumetric and gravimetric basis. However, a critical bottleneck for the use of H<sub>2</sub> as a transportation fuel has been the lack of densification methods capable of shaping MOFs into practical formulations while maintaining their adsorptive performance. Here, we report a high-throughput screening and deep analysis of a database of MOFs to find optimal materials, followed by the synthesis, characterization, and performance evaluation of an optimal monolithic MOF (<sub>mono</sub>MOF) for H<sub>2</sub> storage. After densification, this <sub>mono</sub>MOF stores 46 g L<sup>-1</sup> H<sub>2</sub> at 50 bar and 77 K and delivers 41 and 42 g L<sup>-1</sup> H<sub>2</sub> at operating pressures of 25 and 50 bar, respectively, when deployed in a combined temperature-pressure (25-50 bar/77 K → 5 bar/160 K) swing gas delivery system. This performance represents up to an 80% reduction in the operating pressure requirements for delivering H<sub>2</sub> gas when compared with benchmark materials and an 83% reduction compared to compressed H<sub>2</sub> gas. Our findings represent a substantial step forward in the application of high-density materials for volumetric H<sub>2</sub> storage applications. Chemistry ; Gravimetric analysis ; Hydrogen storage ; Chemical engineering ; Hydrogen ; Organic chemistry Zeolite Catalysis and Synthesis ; Hydrogen Storage and Materials ; Chemical Synthesis and Characterization
Authors: Fan Zhang | Zhuang Wang | Willie J.G.M. Peijnenburg | Martina G. Vijver
Source: Environmental Science & Technology (review, 2022-10-05)
Field: Materials Science — Materials Chemistry

Review and Prospects on the Ecotoxicity of Mixtures of Nanoparticles and Hybrid Nanomaterials The rapid development of nanomaterials (NMs) and the emergence of new multicomponent NMs will inevitably lead to simultaneous exposure of organisms to multiple engineered nanoparticles (ENPs) at varying exposure levels. Understanding the joint impacts of multiple ENPs and predicting the toxicity of mixtures of ENPs are therefore evidently of importance. We reviewed the toxicity of mixtures of ENPs to a variety of different species, covering algae, bacteria, daphnia, fish, fungi, insects, and plants. Most studies used the independent-action (IA)-based model to assess the type of joint effects. Using co-occurrence networks, it was revealed that 53% of the cases with specific joint response showed antagonistic, 25% synergistic, and 22% additive effects. The combination of nCuO and nZnO exhibited the strongest interactions in each type of joint interaction. Compared with other species, plants exposed to multiple ENPs were more likely to experience antagonistic effects. The main factors influencing the joint response type of the mixtures were (1) the chemical composition of individual components in mixtures, (2) the stability of suspensions of mixed ENPs, (3) the type and trophic level of the individual organisms tested, (4) the biological level of organization (population, communities, ecosystems), (5) the exposure concentrations and time, (6) the endpoint of toxicity, and (7) the abiotic field conditions (e.g., pH, ionic strength, natural organic matter). This knowledge is critical in developing efficient strategies for the assessment of the hazards induced by combined exposure to multiple ENPs in complex environments. In addition, this knowledge of the joint effects of multiple ENPs assists in the effective prediction of hybrid NMs. Ecotoxicity ; Environmental chemistry ; Daphnia ; Daphnia magna ; Nanotoxicology ; Abiotic component ; Trophic level ; Chemistry ; Biology ; Ecology ; Toxicity Nanoparticles: synthesis and applications ; Environmental Toxicology and Ecotoxicology
Authors: Chen Ma | Li Chen | Lin Wang | Wei Tong | Chenlei Chu | Zhiping Yuan | Cunjing Lv | Quanshui Zheng
Source: Nature Communications (article, 2022-09-14)
Field: Materials Science — Surfaces, Coatings and Films

Condensation droplet sieve Large droplets emerging during dropwise condensation impair surface properties such as anti-fogging/frosting ability and heat transfer efficiency. How to spontaneously detach massive randomly distributed droplets with controlled sizes has remained a challenge. Herein, we present a solution called condensation droplet sieve, through fabricating microscale thin-walled lattice structures coated with a superhydrophobic layer. Growing droplets were observed to jump off this surface once becoming slightly larger than the lattices. The maximum radius and residual volume of droplets were strictly confined to 16 μm and 3.2 nl/mm<sup>2</sup> respectively. We reveal that this droplet radius cut off is attributed to the large tolerance of coalescence mismatch for jumping and effective isolation of droplets between neighboring lattices. Our work brings forth a strategy for the design and fabrication of high-performance anti-dew materials. Coalescence (physics) ; Microscale chemistry ; Condensation ; Materials science ; Chemical physics ; Nanotechnology ; Sublimation (psychology) ; Fabrication ; RADIUS ; Sieve (category theory) ; Chemical engineering ; Dew ; Chemistry ; Thermodynamics ; Physics Surface Modification and Superhydrophobicity ; Fluid Dynamics and Heat Transfer ; Icing and De-icing Technologies
Authors: Shao‐Min Wang | Xuan‐Tong Mu | Haoran Liu | Su‐Tao Zheng | Qing‐Yuan Yang
Source: Angewandte Chemie International Edition (article, 2022-06-08)
Field: Chemistry — Inorganic Chemistry

Pore‐Structure Control in Metal–Organic Frameworks (MOFs) for Capture of the Greenhouse Gas SF<sub>6</sub>with Record Separation In the electronics industry, the efficient recovery and capture of sulfur hexafluoride (SF<sub>6</sub> ) from SF<sub>6</sub> /N<sub>2</sub> mixtures is of great importance. Herein, three metal-organic frameworks with fine-tuning pore structures, Cu(peba)<sub>2</sub> , Ni(pba)<sub>2</sub> , and Ni(ina)<sub>2</sub> , were designed for SF<sub>6</sub> capture. Among them, Ni(ina)<sub>2</sub> has perfect pore sizes (6 Å) that are comparable to the kinetic diameter of sulfur hexafluoride (5.2 Å), affording the benchmark binding affinity for SF<sub>6</sub> gas. Ni(ina)<sub>2</sub> exhibits the highest SF<sub>6</sub> /N<sub>2</sub> selectivity (375.1 at 298 K and 1 bar) and ultra-high SF<sub>6</sub> uptake capacity (53.5 cm<sup>3</sup> g<sup>-1</sup> at 298 K and 0.1 bar) at ambient conditions. The remarkable separation performance of Ni(ina)<sub>2</sub> was verified by dynamic breakthrough experiments. Theoretical calculations and the SF<sub>6</sub> -loaded single-crystal structure provided critical insight into the adsorption/separation mechanism. This porous coordination network has the potential to be used in industrial applications. Metal-organic framework ; Sulfur hexafluoride ; Gas separation ; Materials science ; Bar (unit) ; Adsorption ; Sulfur ; Porosity ; Metal ; Chemical engineering ; Nanotechnology ; Chemistry ; Organic chemistry ; Metallurgy ; Composite material ; Membrane Metal-Organic Frameworks: Synthesis and Applications ; Inorganic Fluorides and Related Compounds ; Membrane Separation and Gas Transport
Authors: Yuan Liu | Junhui Liu | Hanting Xiong | Jingwen Chen | Shixia Chen | Zheling Zeng | Shuguang Deng | Jun Wang
Source: Nature Communications (article, 2022-09-20)
Field: Chemistry — Inorganic Chemistry

Negative electrostatic potentials in a Hofmann-type metal-organic framework for efficient acetylene separation Efficient adsorptive separation of acetylene (C<sub>2</sub>H<sub>2</sub>) from carbon dioxide (CO<sub>2</sub>) or ethylene (C<sub>2</sub>H<sub>4</sub>) is industrially important but challenging due to the identical dynamic diameter or the trace amount. Here we show an electrostatic potential compatible strategy in a nitroprusside-based Hofmann-type metal-organic framework, Cu(bpy)NP (NP = nitroprusside, bpy = 4,4'-bipyridine), for efficient C<sub>2</sub>H<sub>2</sub> separation. The intruding cyanide and nitrosyl groups in undulating one-dimensional channels induce negative electrostatic potentials for preferential C<sub>2</sub>H<sub>2</sub> recognition instead of open metal sites in traditional Hofmann-type MOFs. As a result, Cu(bpy)NP exhibits a 50/50 C<sub>2</sub>H<sub>2</sub>/CO<sub>2</sub> selectivity of 47.2, outperforming most rigid MOFs. The dynamic breakthrough experiment demonstrates a 99.9% purity C<sub>2</sub>H<sub>4</sub> productivity of 20.57 mmol g<sup>-1</sup> from C<sub>2</sub>H<sub>2</sub>/C<sub>2</sub>H<sub>4</sub> (1/99, v/v) gas-mixture. Meanwhile, C<sub>2</sub>H<sub>2</sub> can also be captured and recognized from ternary C<sub>2</sub>H<sub>2</sub>/CO<sub>2</sub>/C<sub>2</sub>H<sub>4</sub> (25/25/50, v/v/v) gas-mixture. Furthermore, computational studies and in-situ infrared spectroscopy reveal that the selective C<sub>2</sub>H<sub>2</sub> binding arises from the compatible pore electro-environment generated by the electron-rich N and O atoms from nitroprusside anions. Acetylene ; Separation (statistics) ; Type (biology) ; Materials science ; Chemistry ; Organic chemistry ; Computer science ; Biology Metal-Organic Frameworks: Synthesis and Applications ; Chemical Synthesis and Reactions ; Inorganic Fluorides and Related Compounds
Authors: So Takamoto | Satoshi IZUMI | Ju Li
Source: Computational Materials Science (article, 2022-03-09)
Field: Materials Science — Materials Chemistry

TeaNet: Universal neural network interatomic potential inspired by iterative electronic relaxations A universal interatomic potential for an arbitrary set of chemical elements is urgently needed in computational materials science. Graph convolution neural network (GCN) has rich expressive power, but previously was mainly employed to transport scalars and vectors, not rank ≥2 tensors. As classic interatomic potentials were inspired by tight-binding electronic relaxation framework, we want to represent this iterative propagation of rank ≥2 tensor information by GCN. Here we propose an architecture called the tensor embedded atom network (TeaNet) where angular interaction is translated into graph convolution through the incorporation of Euclidean tensors, vectors and scalars. By applying the residual network (ResNet) architecture and training with recurrent GCN weights initialization, a much deeper (16 layers) GCN was constructed, whose flow is similar to an iterative electronic relaxation. Our training dataset is generated by density functional theory calculation of mostly chemically and structurally randomized configurations. We demonstrate that arbitrary structures and reactions involving the first 18 elements on the periodic table (H to Ar) can be realized satisfactorily by TeaNet, including C–H molecular structures, metals, amorphous SiO2, and water, showing surprisingly good performance (energy mean absolute error 19 meV/atom) and robustness for arbitrary chemistries involving elements from H to Ar. Tensor (intrinsic definition) ; Relaxation (psychology) ; Computer science ; Graph ; Initialization ; Materials science ; Chemistry ; Computational chemistry ; Mathematics ; Theoretical computer science ; Pure mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Xijun Wang | Yunfei Gao | Emily Krzystowczyk | Sherafghan Iftikhar | Henri Dou | Runxia Cai | Haiying Wang | Chongyan Ruan | Sheng Ye | Fanxing Li
Source: Energy & Environmental Science (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

High-throughput oxygen chemical potential engineering of perovskite oxides for chemical looping applications Integrating DFT, machine learning and experimental verifications, a high-throughput screening scheme is performed to rationally engineer the redox properties of SrFeO 3− δ based perovskites for chemical looping applications. Chemical looping combustion ; Throughput ; Perovskite (structure) ; Oxygen ; Materials science ; Redox ; Chemical engineering ; Chemical reaction ; Nanotechnology ; Computer science ; Chemistry ; Engineering ; Metallurgy Electronic and Structural Properties of Oxides ; Advancements in Solid Oxide Fuel Cells ; Catalysis and Oxidation Reactions
Authors: Zhengzheng Guo | Penggang Ren | Zhenxia Lu | Kaidi Hui | Junjun Yang | Zengping Zhang | Zhengyan Chen | Yanling Jin | Fang Ren
Source: ACS Applied Materials & Interfaces (article, 2022-09-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Multifunctional CoFe<sub>2</sub>O<sub>4</sub>@MXene-AgNWs/Cellulose Nanofiber Composite Films with Asymmetric Layered Architecture for High-Efficiency Electromagnetic Interference Shielding and Remarkable Thermal Management Capability Developing high-efficiency electromagnetic interference (EMI) shielding composite films with outstanding flexibility and excellent thermal management capability is vital but challenging for modern integrated electronic devices. Herein, a facile two-step vacuum filtration method was used to fabricate ultrathin, flexible, and multifunctional cellulose nanofiber (CNF)-based composite films with an asymmetric layered architecture. The asymmetric layered structure is composed of a low-conductivity CoFe<sub>2</sub>O<sub>4</sub>@MXene/CNF layer and a highly conductive silver nanowires (AgNWs)/CNF layer. Benefiting from the rational placement of the impedance matching layer and shielding layer, as well as the synergistic effect of electric and magnetic losses, the resultant composite film exhibits an extremely high EMI shielding effectiveness (SE) of 73.3 dB and an average EMI SE of 70.9 dB with low reflected efficiency of 4.9 dB at only 0.1 mm thickness. Sufficiently reliable EMI SE (over 95% reservation) is attained even after suffering from continuous physical deformations and long-term chemical attacks. Moreover, the prepared films exhibit extraordinary flexibility, strong mechanical properties, and satisfactory thermal management capability. This work offers a viable strategy for exploiting high performance EMI shielding films with attractive thermal management capacity, and the resultant films present extensive application potential in aerospace, artificial intelligence, advanced electronics, stealth technology, and the national defense industry, even under harsh environments. Materials science ; Electromagnetic shielding ; EMI ; Composite number ; Nanofiber ; Electromagnetic interference ; Electrical conductor ; Composite material ; Layer (electronics) ; Nanocomposite ; Optoelectronics ; Nanotechnology ; Computer science ; Telecommunications Electromagnetic wave absorption materials ; Advanced Antenna and Metasurface Technologies ; Electromagnetic Compatibility and Noise Suppression