jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Liang Li | Yong‐Zhu Yan | Jufu Liang | Jinchuan Zhao | C. Lyu | Haoxiang Zhai | Xilong Wu | Guizhen Wang
Source: Advanced Science (article, 2024-04-19)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Wearable EMI Shielding Composite Films with Integrated Optimization of Electrical Safety, Biosafety and Thermal Safety Biomaterial-based flexible electromagnetic interference (EMI) shielding composite films are desirable in many applications of wearable electronic devices. However, much research focuses on improving the EMI shielding performance of materials, while optimizing the comprehensive safety of wearable EMI shielding materials has been neglected. Herein, wearable cellulose nanofiber@boron nitride nanosheet/silver nanowire/bacterial cellulose (CNF@BNNS/AgNW/BC) EMI shielding composite films with sandwich structure are fabricated via a simple sequential vacuum filtration method. For the first time, the electrical safety, biosafety, and thermal safety of EMI shielding materials are optimized integratedly. Since both sides of the sandwich structure contain CNF and BC electrical insulation layers, the CNF@BNNS/AgNW/BC composite films exhibit excellent electrical safety. Furthermore, benefiting from the AgNW conductive networks in the middle layer, the CNF@BNNS/AgNW/BC exhibit excellent EMI shielding effectiveness of 49.95 dB and ultra-fast response Joule heating performance. More importantly, the antibacterial property of AgNW ensures the biosafety of the composite films. Meanwhile, the AgNW and the CNF@BNNS layers synergistically enhance the thermal conductivity of the CNF@BNNS/AgNW/BC composite film, reaching a high value of 8.85 W m<sup>‒1</sup> K<sup>‒1</sup>, which significantly enhances its thermal safety when used in miniaturized electronic device. This work offers new ideas for fabricating biomaterial-based EMI shielding composite films with high comprehensive safety. Biosafety ; Electromagnetic shielding ; EMI ; Composite number ; Wearable computer ; Materials science ; Wearable technology ; Electromagnetic interference ; Nanotechnology ; Computer science ; Composite material ; Electrical engineering ; Engineering ; Medicine ; Embedded system Electromagnetic wave absorption materials ; Electrical Contact Performance and Analysis ; Advanced Energy Technologies and Civil Engineering Innovations
Authors: Mingshu Li | Peng Zhang | Zhiling Guo | Weichen Zhao | Yuanbo Li | Tianjing Yi | Weidong Cao | Li Gao | Chang Fu Tian | Qing Chen | Fazheng Ren | Yukui Rui | Jason C. White | Iseult Lynch
Source: Environmental Science & Technology (article, 2024-01-04)
Field: Materials Science — Materials Chemistry

Dynamic Transformation of Nano-MoS<sub>2</sub> in a Soil–Plant System Empowers Its Multifunctionality on Soybean Growth Molybdenum disulfide (nano-MoS<sub>2</sub>) nanomaterials have shown great potential for biomedical and catalytic applications due to their unique enzyme-mimicking properties. However, their potential agricultural applications have been largely unexplored. A key factor prior to the application of nano-MoS<sub>2</sub> in agriculture is understanding its behavior in a complex soil-plant system, particularly in terms of its transformation. Here, we investigate the distribution and transformation of two types of nano-MoS<sub>2</sub> (MoS<sub>2</sub> nanoparticles and MoS<sub>2</sub> nanosheets) in a soil-soybean system through a combination of synchrotron radiation-based X-ray absorption near-edge spectroscopy (XANES) and single-particle inductively coupled plasma mass spectrometry (SP-ICP-MS). We found that MoS<sub>2</sub> nanoparticles (NPs) transform dynamically in soil and plant tissues, releasing molybdenum (Mo) and sulfur (S) that can be incorporated gradually into the key enzymes involved in nitrogen metabolism and the antioxidant system, while the rest remain intact and act as nanozymes. Notably, there is 247.9 mg/kg of organic Mo in the nodule, while there is only 49.9 mg/kg of MoS<sub>2</sub> NPs. This study demonstrates that it is the transformation that leads to the multifunctionality of MoS<sub>2</sub>, which can improve the biological nitrogen fixation (BNF) and growth. Therefore, MoS<sub>2</sub> NPs enable a 30% increase in yield compared to the traditional molybdenum fertilizer (Na<sub>2</sub>MoO<sub>4</sub>). Excessive transformation of MoS<sub>2</sub> nanosheets (NS) leads to the overaccumulation of Mo and sulfate in the plant, which damages the nodule function and yield. The study highlights the importance of understanding the transformation of nanomaterials for agricultural applications in future studies. Molybdenum disulfide ; Nanomaterials ; Molybdenum ; Nanotechnology ; Chemistry ; XANES ; Plant system ; Nanoparticle ; Fertilizer ; Materials science ; Environmental chemistry ; Chemical engineering ; Spectroscopy ; Inorganic chemistry ; Biotechnology ; Organic chemistry ; Metallurgy ; Physics Advanced Nanomaterials in Catalysis ; Nanocluster Synthesis and Applications ; Advanced Photocatalysis Techniques
Authors: Naveen Kumar | Sauraj Singh | Piyush Sharma | Bijender Kumar | Anuj Kumar
Source: Gels (review, 2024-01-14)
Field: Materials Science — Biomaterials

Single-, Dual-, and Multi-Stimuli-Responsive Nanogels for Biomedical Applications In recent years, stimuli-responsive nanogels that can undergo suitable transitions under endogenous (e.g., pH, enzymes and reduction) or exogenous stimuli (e.g., temperature, light, and magnetic fields) for on-demand drug delivery, have received significant interest in biomedical fields, including drug delivery, tissue engineering, wound healing, and gene therapy due to their unique environment-sensitive properties. Furthermore, these nanogels have become very popular due to some of their special properties such as good hydrophilicity, high drug loading efficiency, flexibility, and excellent biocompatibility and biodegradability. In this article, the authors discuss current developments in the synthesis, properties, and biomedical applications of stimulus-responsive nanogels. In addition, the opportunities and challenges of nanogels for biomedical applications are also briefly predicted. Biocompatibility ; Drug delivery ; Nanotechnology ; Materials science ; Self-healing hydrogels ; Flexibility (engineering) ; Gene delivery ; Tissue engineering ; Biomedical engineering ; Chemistry ; Genetic enhancement ; Engineering Nanoparticle-Based Drug Delivery ; Hydrogels: synthesis, properties, applications ; Advanced Drug Delivery Systems
Authors: Hui Xu | Yan Su | Ethan Gerhard | Denghui Xie | Xiaodong Liu | Bing Zhang | Dongquan Shi | Guillermo A. Ameer | Jian Yang
Source: Advanced Materials (review, 2024-05-27)
Field: Materials Science — Biomaterials

Citric Acid: A Nexus Between Cellular Mechanisms and Biomaterial Innovations Citrate-based biodegradable polymers have emerged as a distinctive biomaterial platform with tremendous potential for diverse medical applications. By harnessing their versatile chemistry, these polymers exhibit a wide range of material and bioactive properties, enabling them to regulate cell metabolism and stem cell differentiation through energy metabolism, metabonegenesis, angiogenesis, and immunomodulation. Moreover, the recent US Food and Drug Administration (FDA) clearance of the biodegradable poly(octamethylene citrate) (POC)/hydroxyapatite-based orthopedic fixation devices represents a translational research milestone for biomaterial science. POC joins a short list of biodegradable synthetic polymers that have ever been authorized by the FDA for use in humans. The clinical success of POC has sparked enthusiasm and accelerated the development of next-generation citrate-based biomaterials. This review presents a comprehensive, forward-thinking discussion on the pivotal role of citrate chemistry and metabolism in various tissue regeneration and on the development of functional citrate-based metabotissugenic biomaterials for regenerative engineering applications. Biomaterial ; Nanotechnology ; Biocompatible material ; Tissue engineering ; Materials science ; Biochemical engineering ; Biomedical engineering ; Engineering biodegradable polymer synthesis and properties ; Bone Tissue Engineering Materials ; Electrospun Nanofibers in Biomedical Applications
Authors: Wendong Liu | Enyu Shi | Hao Wu | Yanjie Liang | Mingyue Chen | Hongyan Zhang | Ruizhong Zhang | Xiyan Li | Yinsong Wang | Libing Zhang
Source: Advanced Functional Materials (article, 2024-05-03)
Field: Materials Science — Materials Chemistry

Spatially Axial Boron Coordinated Single‐Atom Nanozymes with Boosted Multi‐Enzymatic Performances for Periodontitis Treatment Abstract Single‐atom nanozymes (SAzymes) have made significant strides in antibacterial treatment but fall short as natural enzyme and drug replacements due to limited catalytic performance. Here, a rational strategy is presented for incorporating spatially axial boron (B) ligands to effectively modulate the local coordination environment of planar Fe─N 4 motifs (Fe─B/N─C SAzymes). With electronic modulation, the Fe─B/N─C SAzymes exhibit significantly enhanced oxidase‐, peroxidase‐, and catalase‐like activities. Theoretical calculations highlight that the spatially axial B ligands effectively adjust the charge distribution around the planar Fe─N 4 active center, which facilitates the heterolysis of H 2 O 2 and the desorption of O 2 , resulting in accelerated H 2 O 2 decomposition. Furthermore, the intrinsic photothermal effect of Fe─B/N─C SAzymes enhances multienzyme‐like activities, rapidly generating abundant reactive oxygen species (ROS), and achieving chemodynamic/photothermal synergistic therapy for impressive disinfection against periodontal‐related pathogenic bacteria. These findings offer a distinctive viewpoint for optimizing the local coordination environment of SAzymes with axial ligand to enhance their catalytic performance and effectiveness in periodontitis therapy. Materials science ; Boron ; Periodontitis ; Atom (system on chip) ; Nanotechnology ; Organic chemistry ; Dentistry ; Chemistry ; Computer science ; Medicine Advanced Nanomaterials in Catalysis ; Graphene and Nanomaterials Applications ; Advanced biosensing and bioanalysis techniques
Authors: Adam Overvig | Sander A. Mann | Andrea Alù
Source: Light Science & Applications (article, 2024-01-24)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Spatio-temporal coupled mode theory for nonlocal metasurfaces Diffractive nonlocal metasurfaces have recently opened a broad range of exciting developments in nanophotonics research and applications, leveraging spatially extended-yet locally patterned-resonant modes to control light with new degrees of freedom. While conventional grating responses are elegantly captured by temporal coupled mode theory, current approaches are not well equipped to capture the arbitrary spatial response observed in the nascent field of nonlocal metasurfaces. Here, we introduce spatio-temporal coupled mode theory (STCMT), capable of elegantly capturing the key features of the resonant response of wavefront-shaping nonlocal metasurfaces. This framework can quantitatively guide nonlocal metasurface design while maintaining compatibility with local metasurface frameworks, making it a powerful tool to rationally design and optimize a broad class of ultrathin optical components. We validate this STCMT framework against full-wave simulations of various nonlocal metasurfaces, demonstrating that this tool offers a powerful semi-analytical framework to understand and model the physics and functionality of these devices, without the need for computationally intense full-wave simulations. We also discuss how this model may shed physical insights into nonlocal phenomena in photonics and the functionality of the resulting devices. As a relevant example, we showcase STCMT's flexibility by applying it to study and rapidly prototype nonlocal metasurfaces that spatially shape thermal emission. Mode (computer interface) ; Physics ; Coupled mode theory ; Optics ; Computer science ; Human–computer interaction ; Refractive index Metamaterials and Metasurfaces Applications ; Plasmonic and Surface Plasmon Research ; Advanced Antenna and Metasurface Technologies
Authors: Guangheng Qi | Ning Ma | Kai Wang
Source: Energies (article, 2024-05-27)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Predicting the Remaining Useful Life of Supercapacitors under Different Operating Conditions With the rapid development of the new energy industry, supercapacitors have become key devices in the field of energy storage. To forecast the remaining useful life (RUL) of supercapacitors, we introduce a new technology that integrates variational mode decomposition (VMD) with a bidirectional long short-term memory (BiLSTM) neural network. Firstly, the aging experiments of supercapacitors under various temperatures and voltages were carried out to obtain aging data. Then, VMD was implemented to decompose the aging data, which helped to eliminate disturbances, including capacity recovery and test errors. Then, the hyperparameters of BiLSTM were adjusted, employing the sparrow search algorithm (SSA) to improve the consistency between the input data and the network structure. After obtaining the optimal hyperparameters of BiLSTM, the decomposed aging data were input into BiLSTM for prediction. The experimental results showed that the VMD-SSA-BiLSTM model proposed in this paper has high prediction accuracy and high robustness under different temperatures and voltages, with an average RMSE of 0.112519, a decrease of 44.3% compared to BiLSTM, and a minimum of 0.031426. Supercapacitor ; Automotive engineering ; Computer science ; Reliability engineering ; Engineering ; Capacitance ; Chemistry ; Electrode Supercapacitor Materials and Fabrication
Authors: Zong‐Lu Che | Chang‐Cun Yan | Xuedong Wang | Liang‐Sheng Liao
Source: Advanced Photonics (article, 2024-01-08)
Field: Materials Science — Materials Chemistry

Organic near-infrared optoelectronic materials and devices: an overview Near-infrared (NIR) light has shown great potential for military and civilian applications owing to its advantages in the composition of sunlight, invisibility to human eyes, deeper penetration into biological tissues, and low optical loss in optical fibers. Therefore, organic optoelectronic materials that can absorb or emit NIR light have aroused great scientific interest in basic science and practical applications. Based on these NIR organic optoelectronic materials, NIR optoelectronic devices have been greatly improved in performance and application. In this review, the representative NIR organic optoelectronic materials used in organic solar cells, organic photodetectors, organic light-emitting diodes, organic lasers, and organic optical waveguide devices are briefly introduced, and the potential applications of each kind of device are briefly summarized. Finally, we summarize and take up the development of NIR organic optoelectronic materials and devices. Optoelectronics ; Materials science ; OLED ; Photodetector ; Near-infrared spectroscopy ; Infrared ; Optical materials ; Nanotechnology ; Optics ; Physics Luminescence and Fluorescent Materials ; Organic Light-Emitting Diodes Research ; Organic Electronics and Photovoltaics
Authors: M.A. Almessiere | N.A. Algarou | Y. Slimani | D.S. Klygach | A. Baykal | A. Demir Korkmaz | T.I. Zubar | Maxim V. Silibin | Maxim G. Vakhitov | Anwar UI‐Hamid | Sergey V. Trukhanov | А.В. Труханов
Source: Journal of the American Ceramic Society (article, 2024-03-30)
Field: Materials Science — Materials Chemistry

Structure, magnetic, and electrodynamic properties of SrIn <i> <sub>x</sub> </i> Fe <sub>12‐</sub> <i> <sub>x</sub> </i> O <sub>4</sub> /Ni <sub>0.5</sub> Zn <sub>0.5</sub> Fe <sub>2</sub> O <sub>4</sub> composites at low temperature Abstract Exchange‐coupled hard–soft bi‐magnetic ferrites can be used as isolators, microwave absorption materials, filters, hyperthermia materials, and biosensors with improved energy properties. The nanocomposites with the chemical composition SrIn x Fe 12‐ x O 4 /Ni 0.5 Zn 05 Fe 2 O 4 (where x varies from 0.00 to 0.04) have the advantages of both materials: spinel and hexaferrite. The combination of Sr‐hexaferrate with spinel ferrite results in proximity effects, reversal of magnetization in two stages, and blocking temperatures that can be tuned in bi‐magnetic hard and soft ferrites. It was found an anomalius kink in the hysteresis loops and two peaks in the switching field distribution at x ≤ 0.02. The exchange interaction on the surfaces of hard and soft nanoparticles explains the behavior of the magnetic characteristics. Materials science ; X-ray crystallography ; Crystallography ; Condensed matter physics ; Mineralogy ; Physics ; Chemistry ; Diffraction ; Optics Magnetic Properties and Synthesis of Ferrites ; Multiferroics and related materials ; Electrical and Thermal Properties of Materials
Authors: Xinqin Meng | Chengbing Qin | Xilong Liang | Guofeng Zhang | Ruiyun Chen | Jianyong Hu | Zhichun Yang | Jianzhong Huo | Liantuan Xiao | Suotang Jia
Source: Frontiers of Physics (article, 2024-04-16)
Field: Materials Science — Materials Chemistry

Deep learning in two-dimensional materials: Characterization, prediction, and design Abstract Since the isolation of graphene, two-dimensional (2D) materials have attracted increasing interest because of their excellent chemical and physical properties, as well as promising applications. Nonetheless, particular challenges persist in their further development, particularly in the effective identification of diverse 2D materials, the domains of large-scale and high-precision characterization, also intelligent function prediction and design. These issues are mainly solved by computational techniques, such as density function theory and molecular dynamic simulation, which require powerful computational resources and high time consumption. The booming deep learning methods in recent years offer innovative insights and tools to address these challenges. This review comprehensively outlines the current progress of deep learning within the realm of 2D materials. Firstly, we will briefly introduce the basic concepts of deep learning and commonly used architectures, including convolutional neural and generative adversarial networks, as well as U-net models. Then, the characterization of 2D materials by deep learning methods will be discussed, including defects and materials identification, as well as automatic thickness characterization. Thirdly, the research progress for predicting the unique properties of 2D materials, involving electronic, mechanical, and thermodynamic features, will be evaluated succinctly. Lately, the current works on the inverse design of functional 2D materials will be presented. At last, we will look forward to the application prospects and opportunities of deep learning in other aspects of 2D materials. This review may offer some guidance to boost the understanding and employing novel 2D materials. Deep learning ; Computer science ; Characterization (materials science) ; Artificial intelligence ; Identification (biology) ; Function (biology) ; Generative grammar ; Data science ; Machine learning ; Nanotechnology ; Materials science Graphene research and applications ; 2D Materials and Applications ; Machine Learning in Materials Science
Authors: Yan Cao | Ali Taghvaie Nakhjiri | Mahdi Ghadiri
Source: Engineering Applications of Artificial Intelligence (article, 2024-06-20)
Field: Materials Science — Materials Chemistry

Different applications of machine learning approaches in materials science and engineering: Comprehensive review Computer science ; Artificial intelligence ; Machine learning ; Science and engineering ; Data science ; Engineering ethics Machine Learning in Materials Science ; Mineral Processing and Grinding ; X-ray Diffraction in Crystallography
Authors: Xiaoshan Luo | Zhenyu Wang | Pengyue Gao | Jian Lv | Yanchao Wang | Changfeng Chen | Yanming Ma
Source: npj Computational Materials (preprint, 2024-11-12)
Field: Materials Science — Materials Chemistry

Deep learning generative model for crystal structure prediction Recent advances in deep learning generative models (GMs) have created high capabilities in accessing and assessing complex high-dimensional data, allowing superior efficiency in navigating vast material configuration space in search of viable structures. Coupling such capabilities with physically significant data to construct trained models for materials discovery is crucial to moving this emerging field forward. Here, we present a universal GM for crystal structure prediction (CSP) via a conditional crystal diffusion variational autoencoder (Cond-CDVAE) approach, which is tailored to allow user-defined material and physical parameters such as composition and pressure. This model is trained on an expansive dataset containing over 670,000 local minimum structures, including a rich spectrum of high-pressure structures, along with ambient-pressure structures in Materials Project database. We demonstrate that the Cond-CDVAE model can generate physically plausible structures with high fidelity under diverse pressure conditions without necessitating local optimization, accurately predicting 59.3% of the 3547 unseen ambient-pressure experimental structures within 800 structure samplings, with the accuracy rate climbing to 83.2% for structures comprising fewer than 20 atoms per unit cell. These results meet or exceed those achieved via conventional CSP methods based on global optimization. The present findings showcase substantial potential of GMs in the realm of CSP. Generative grammar ; Artificial intelligence ; Generative model ; Deep learning ; Computer science Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Bingqing Cheng
Source: npj Computational Materials (article, 2024-07-18)
Field: Materials Science — Materials Chemistry

Cartesian atomic cluster expansion for machine learning interatomic potentials Abstract Machine learning interatomic potentials are revolutionizing large-scale, accurate atomistic modeling in material science and chemistry. Many potentials use atomic cluster expansion or equivariant message-passing frameworks. Such frameworks typically use spherical harmonics as angular basis functions, followed by Clebsch-Gordan contraction to maintain rotational symmetry. We propose a mathematically equivalent and simple alternative that performs all operations in the Cartesian coordinates. This approach provides a complete set of polynormially independent features of atomic environments while maintaining interaction body orders. Additionally, we integrate low-dimensional embeddings of various chemical elements, trainable radial channel coupling, and inter-atomic message passing. The resulting potential, named Cartesian Atomic Cluster Expansion (CACE), exhibits good accuracy, stability, and generalizability. We validate its performance in diverse systems, including bulk water, small molecules, and 25-element high-entropy alloys. Cluster (spacecraft) ; Interatomic potential ; Cartesian coordinate system ; Cluster expansion ; Computer science ; Physics ; Materials science ; Atomic physics ; Mathematics ; Molecular dynamics ; Geometry ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography