jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Phuong V. Pham | The-Hung Mai | Saroj P. Dash | Vasudevanpillai Biju | Yu‐Lun Chueh | Deep Jariwala | Vincent Tung
Source: ACS Nano (article, 2024-05-29)
Field: Materials Science — Materials Chemistry

Transfer of 2D Films: From Imperfection to Perfection Atomically thin 2D films and their van der Waals heterostructures have demonstrated immense potential for breakthroughs and innovations in science and technology. Integrating 2D films into electronics and optoelectronics devices and their applications in electronics and optoelectronics can lead to improve device efficiencies and tunability. Consequently, there has been steady progress in large-area 2D films for both front- and back-end technologies, with a keen interest in optimizing different growth and synthetic techniques. Parallelly, a significant amount of attention has been directed toward efficient transfer techniques of 2D films on different substrates. Current methods for synthesizing 2D films often involve high-temperature synthesis, precursors, and growth stimulants with highly chemical reactivity. This limitation hinders the widespread applications of 2D films. As a result, reports concerning transfer strategies of 2D films from bare substrates to target substrates have proliferated, showcasing varying degrees of cleanliness, surface damage, and material uniformity. This review aims to evaluate, discuss, and provide an overview of the most advanced transfer methods to date, encompassing wet, dry, and quasi-dry transfer methods. The processes, mechanisms, and pros and cons of each transfer method are critically summarized. Furthermore, we discuss the feasibility of these 2D film transfer methods, concerning their applications in devices and various technology platforms. Nanotechnology ; Electronics ; Materials science ; Heterojunction ; Transfer printing ; van der Waals force ; Thin film ; Optoelectronics ; Molecule ; Chemistry ; Electrical engineering ; Engineering 2D Materials and Applications ; MXene and MAX Phase Materials ; Electronic and Structural Properties of Oxides
Authors: Subodh Barthwal | Surbhi Uniyal | Sumit Barthwal
Source: Micromachines (article, 2024-03-13)
Field: Materials Science — Surfaces, Coatings and Films

Nature-Inspired Superhydrophobic Coating Materials: Drawing Inspiration from Nature for Enhanced Functionality Superhydrophobic surfaces, characterized by exceptional water repellency and self-cleaning properties, have gained significant attention for their diverse applications across industries. This review paper comprehensively explores the theoretical foundations, various fabrication methods, applications, and associated challenges of superhydrophobic surfaces. The theoretical section investigates the underlying principles, focusing on models such as Young's equation, Wenzel and Cassie-Baxter states, and the dynamics of wetting. Various fabrication methods are explored, ranging from microstructuring and nanostructuring techniques to advanced material coatings, shedding light on the evolution of surface engineering. The extensive applications of superhydrophobic surfaces, spanning from self-cleaning technologies to oil-water separation, are systematically discussed, emphasizing their potential contributions to diverse fields such as healthcare, energy, and environmental protection. Despite their promising attributes, superhydrophobic surfaces also face significant challenges, including durability and scalability issues, environmental concerns, and limitations in achieving multifunctionality, which are discussed in this paper. By providing a comprehensive overview of the current state of superhydrophobic research, this review aims to guide future investigations and inspire innovations in the development and utilization of these fascinating surfaces. Nanotechnology ; Coating ; Materials science ; Fabrication ; Wetting ; Superhydrophobic coating ; Scalability ; Computer science ; Engineering ; Mechanical engineering ; Composite material Surface Modification and Superhydrophobicity ; Advanced Sensor and Energy Harvesting Materials ; Electrohydrodynamics and Fluid Dynamics
Authors: Qiqi Tang | Binbin Jiang | Keli Wang | Wu Wang | Baohai Jia | Tianpeng Ding | Zhenlong Huang | Yuan Lin | Jiaqing He
Source: Joule (article, 2024-05-14)
Field: Materials Science — Materials Chemistry

High-entropy thermoelectric materials Thermoelectric effect ; Materials science ; Engineering physics ; Environmental science ; Nuclear engineering ; Engineering ; Thermodynamics ; Physics Advanced Thermoelectric Materials and Devices ; Thermal properties of materials ; High Entropy Alloys Studies
Authors: Shuaibing Zhang | Xuejiao J. Gao | Yuanjie Ma | Kexu Song | Mengyue Ge | Saiyu Ma | Lirong Zhang | Ye Yuan | Wei Jiang | Zhenhua Wu | Lizeng Gao | Xiyun Yan | Bing Jiang
Source: Nature Communications (article, 2024-12-05)
Field: Materials Science — Materials Chemistry

A bioinspired sulfur–Fe–heme nanozyme with selective peroxidase-like activity for enhanced tumor chemotherapy Iron-based nanozymes, recognized for their biocompatibility and peroxidase-like activities, hold promise as catalysts in tumor therapy. However, their concurrent catalase-like activity undermines therapeutic efficacy by converting hydrogen peroxide in tumor tissues into oxygen, thus diminishing hydroxyl radical production. Addressing this challenge, this study introduces the hemin-cysteine-Fe (HCFe) nanozyme, which exhibits exclusive peroxidase-like activity. Constructed through a supramolecular assembly approach involving Fmoc-L-cysteine, heme, and Fe²⁺ coordination, HCFe distinctly incorporates heme and [Fe-S] within its active center. Sulfur coordination to the central Fe atom of Hemin is crucial in modulating the catalytic preference of the HCFe nanozyme towards peroxidase-like activity. This unique mechanism distinguishes HCFe from other bifunctional iron-based nanozymes, enhancing its catalytic selectivity even beyond that of natural peroxidases. This selective activity allows HCFe to significantly elevate ROS production and exert cytotoxic effects, especially against cisplatin-resistant esophageal squamous cell carcinoma (ESCC) cells and their xenografts in female mice when combined with cisplatin. These findings underscore HCFe's potential as a crucial component in multimodal cancer therapy, notably in augmenting chemotherapy efficacy. Hemin ; Peroxidase ; Chemistry ; Heme ; Biocompatibility ; Hydrogen peroxide ; Catalase ; Catalysis ; Combinatorial chemistry ; Reactive oxygen species ; Biochemistry ; Cancer research ; Enzyme ; Organic chemistry ; Medicine Advanced Nanomaterials in Catalysis ; Nanocluster Synthesis and Applications ; Nanoplatforms for cancer theranostics
Authors: Guoli He | Yaqin Zheng | Changda Zhou | Siyang Li | Zhonghong Shi | Yanhui Deng | Zhang‐Kai Zhou
Source: Light Science & Applications (article, 2024-04-28)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Multiplexed manipulation of orbital angular momentum and wavelength in metasurfaces based on arbitrary complex-amplitude control Due to its unbounded and orthogonal modes, the orbital angular momentum (OAM) is regarded as a key optical degree of freedom (DoF) for future information processing with ultra-high capacity and speed. Although the manipulation of OAM based on metasurfaces has brought about great achievements in various fields, such manipulation currently remains at single-DoF level, which means the multiplexed manipulation of OAM with other optical DoFs is still lacking, greatly hampering the application of OAM beams and advancement of metasurfaces. In order to overcome this challenge, we propose the idea of multiplexed coherent pixel (MCP) for metasurfaces. This approach enables the manipulation of arbitrary complex-amplitude under incident lights of both plane and OAM waves, on the basis of which we have realized the multiplexed DoF control of OAM and wavelength. As a result, the MCP method expands the types of incident lights which can be simultaneously responded by metasurfaces, enriches the information processing capability of metasurfaces, and creates applications of information encryption and OAM demultiplexer. Our findings not only provide means for the design of high-security and high-capacity metasurfaces, but also raise the control and application level of OAM, offering great potential for multifunctional nanophotonic devices in the future. Multiplexing ; Angular momentum ; Optics ; Physics ; Amplitude ; Computer science ; Nanophotonics ; Telecommunications Metamaterials and Metasurfaces Applications ; Orbital Angular Momentum in Optics ; Plasmonic and Surface Plasmon Research
Authors: Mara Schilling-Wilhelmi | Martiño Ríos-García | Sherjeel Shabih | M.V. Gil | Santiago Miret | Christoph T. Koch | J.A. Marquez | Kevin Maik Jablonka
Source: Chemical Society Reviews (review, 2024-12-20)
Field: Materials Science — Materials Chemistry

From text to insight: large language models for chemical data extraction The vast majority of chemical knowledge exists in unstructured natural language, yet structured data is crucial for innovative and systematic materials design. Traditionally, the field has relied on manual curation and partial automation for data extraction for specific use cases. The advent of large language models (LLMs) represents a significant shift, potentially enabling non-experts to extract structured, actionable data from unstructured text efficiently. While applying LLMs to chemical and materials science data extraction presents unique challenges, domain knowledge offers opportunities to guide and validate LLM outputs. This tutorial review provides a comprehensive overview of LLM-based structured data extraction in chemistry, synthesizing current knowledge and outlining future directions. We address the lack of standardized guidelines and present frameworks for leveraging the synergy between LLMs and chemical expertise. This work serves as a foundational resource for researchers aiming to harness LLMs for data-driven chemical research. The insights presented here could significantly enhance how researchers across chemical disciplines access and utilize scientific information, potentially accelerating the development of novel compounds and materials for critical societal needs. Computer science ; Extraction (chemistry) ; Data extraction ; Natural language processing ; Unstructured data ; Data science ; Chemistry ; MEDLINE ; Data mining ; Big data Machine Learning in Materials Science ; Topic Modeling ; Computational Drug Discovery Methods
Authors: Jonathan Schmidt | Tiago F. T. Cerqueira | A. Romero | Antoine Loew | Fabian Jäger | Hai‐Chen Wang | Silvana Botti | Miguel A. L. Marques
Source: Materials Today Physics (article, 2024-09-25)
Field: Materials Science — Materials Chemistry

Improving machine-learning models in materials science through large datasets The accuracy of a machine learning model is limited by the quality and quantity of the data available for its training and validation. This problem is particularly challenging in materials science, where large, high-quality, and consistent datasets are scarce. Here we present alexandria , an open database of more than 5 million density-functional theory calculations for periodic three-, two-, and one-dimensional compounds. We use this data to train machine learning models to reproduce seven different properties using both composition-based models and crystal-graph neural networks. In the majority of cases, the error of the models decreases monotonically with the training data, although some graph networks seem to saturate for large training set sizes. Differences in the training can be correlated with the statistical distribution of the different properties. We also observe that graph-networks, that have access to detailed geometrical information, yield in general more accurate models than simple composition-based methods. Finally, we assess several universal machine learning interatomic potentials. Crystal geometries optimised with these force fields are very high quality, but unfortunately the accuracy of the energies is still lacking. Furthermore, we observe some instabilities for regions of chemical space that are undersampled in the training sets used for these models. This study highlights the potential of large-scale, high-quality datasets to improve machine learning models in materials science. Materials science ; Nanotechnology ; Machine learning ; Engineering physics ; Computer science ; Engineering Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Advanced Materials Characterization Techniques
Authors: Yasemin Gokcekuyu | Fatih Ekinci | Mehmet Serdar Güzel | Koray Açıcı | Sahin Aydin | Tunç Aşuroğlu
Source: Applied Sciences (review, 2024-07-28)
Field: Materials Science — Materials Chemistry

Artificial Intelligence in Biomaterials: A Comprehensive Review The importance of biomaterials lies in their fundamental roles in medical applications such as tissue engineering, drug delivery, implantable devices, and radiological phantoms, with their interactions with biological systems being critically important. In recent years, advancements in deep learning (DL), artificial intelligence (AI), machine learning (ML), supervised learning (SL), unsupervised learning (UL), and reinforcement learning (RL) have significantly transformed the field of biomaterials. These technologies have introduced new possibilities for the design, optimization, and predictive modeling of biomaterials. This review explores the applications of DL and AI in biomaterial development, emphasizing their roles in optimizing material properties, advancing innovative design processes, and accurately predicting material behaviors. We examine the integration of DL in enhancing the performance and functional attributes of biomaterials, explore AI-driven methodologies for the creation of novel biomaterials, and assess the capabilities of ML in predicting biomaterial responses to various environmental stimuli. Our aim is to elucidate the pivotal contributions of DL, AI, and ML to biomaterials science and their potential to drive the innovation and development of superior biomaterials. It is suggested that future research should further deepen these technologies’ contributions to biomaterials science and explore new application areas. Biomaterial ; Computer science ; Nanotechnology ; Engineering ; Artificial intelligence ; Biomedical engineering ; Materials science Machine Learning in Materials Science ; Advanced Neural Network Applications ; Industrial Vision Systems and Defect Detection
Authors: Jiaqi Tao | Kexin Zou | Jintang Zhou | Hongjing Wu | Linling Xu | Jin Wang | Xuewei Tao | Hexia Huang | Zhengjun Yao
Source: Nature Communications (article, 2024-11-28)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Phenolic multiple kinetics-dynamics and discrete crystallization thermodynamics in amorphous carbon nanostructures for electromagnetic wave absorption The lack of a chemical platform with high spatial dimensional diversity, coupled with the elusive multi-scale amorphous physics, significantly hinder advancements in amorphous electromagnetic wave absorption (EWA) materials. Herein, we present a synergistic engineering of phenolic multiple kinetic dynamics and discrete crystallization thermodynamics, to elucidate the origin of the dielectric properties in amorphous carbon and the cascade effect during EWA. Leveraging the scalability of phenolic synthesis, we design dozens of morphologies from the bottom up and combine with in-situ pyrolysis to establish a nanomaterial ecosystem of hundreds of amorphous carbon materials. Based on controlled discrete crystallization, nano-curvature regulation of spatial inversion symmetry-breaking structures, and surface electric field enhancement from multi-shell structures, the multi-scale charge imbalance triggers intense polarization. Both experiments and theories show that each scale is essential, which collectively drives broadband absorption (8.46 GHz) and efficient dissipation (-54.77 dB) of EWA performance. Our work on the amorphous nanostructure platform and the cascade effect can contribute to uncovering the missing pieces in amorphous physics and EWA research. Amorphous solid ; Materials science ; Amorphous carbon ; Crystallization ; Dielectric ; Nanostructure ; Nanotechnology ; Chemical physics ; Chemical engineering ; Optoelectronics ; Chemistry ; Organic chemistry Electromagnetic wave absorption materials ; Graphene research and applications ; Carbon Nanotubes in Composites
Authors: Yinshan Zhang | Yingcai Wang | Zhimin Dong | Youqun Wang | Yuhui Liu | Xiaohong Cao | Zhibin Zhang | Chao Xu | Ning Wang | Yunhai Liu
Source: Nature Communications (article, 2024-10-23)
Field: Chemistry — Inorganic Chemistry

Boosting uranium extraction from Seawater by micro-redox reactors anchored in a seaweed-like adsorbent Efficient extraction of uranium from seawater is expected to provide virtually infinite fuel sources to power nuclear reactors and thus enable sustainable development of nuclear energy. The extraction efficiency for uranium greatly depends on the availability of active adsorption sites on the adsorbents. Maximization of the utilization rate of the binding sites in the adsorbent is vital for improving adsorption capacity. Herein, micro-redox reactors functioned by Cu(I)/Cu(II) conversion are constructed internally in an adsorbent bearing both amidoxime and carboxyl groups to induce active regeneration of the inactivated binding sites to enhance uranium capture. This adsorbent has high adsorption capacity (962.40 mg-U/g-Ads), superior anti-fouling ability as well as excellent uranium uptake (14.62 mg-U/g-Ads) in natural seawater after 56 days, placing it at the top of high-performance sorbent materials for uranium harvest from seawater. Uranium ; Seawater ; Adsorption ; Boosting (machine learning) ; Extraction (chemistry) ; Algae ; Redox ; Chemistry ; Chemical engineering ; Environmental science ; Environmental chemistry ; Materials science ; Biology ; Chromatography ; Inorganic chemistry ; Ecology ; Metallurgy ; Organic chemistry ; Computer science Radioactive element chemistry and processing ; Radioactive contamination and transfer ; Chemical Synthesis and Characterization
Authors: Jin Yao | Fangxing Lai | Yubin Fan | Yuhan Wang | Shih‐Hsiu Huang | Borui Leng | Yao Liang | Rong Lin | Shufan Chen | Mu Ku Chen | Pin Chieh Wu | Shumin Xiao | Din Ping Tsai
Source: Nature Communications (article, 2024-08-02)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Nonlocal meta-lens with Huygens’ bound states in the continuum Meta-lenses composed of artificial meta-atoms have stimulated substantial interest due to their compact and flexible wavefront shaping capabilities, outperforming bulk optical devices. The operating bandwidth is a critical factor determining the meta-lens' performance across various wavelengths. Meta-lenses that operate in a narrowband manner relying on nonlocal effects can effectively reduce disturbance and crosstalk from non-resonant wavelengths, making them well-suitable for specialized applications such as nonlinear generation and augmented reality/virtual reality display. However, nonlocal meta-lenses require striking a balance between local phase manipulation and nonlocal resonance excitation, which involves trade-offs among factors like quality-factor, efficiency, manipulation dimensions, and footprint. In this work, we experimentally demonstrate the nonlocal meta-lens featuring Huygens' bound states in the continuum (BICs) and its near-infrared imaging application. All-dielectric integrated-resonant unit is particularly optimized to efficiently induce both the quasi-BIC and generalized Kerker effect, while ensuring the rotation-angle robustness for generating geometric phase. The experimental results show that the single-layer nonlocal Huygens' meta-lens possesses a high quality-factor of 104 and achieves a transmission polarization conversion efficiency of 55%, exceeding the theoretical limit of 25%. The wavelength-selective two-dimensional focusing and imaging are demonstrated as well. This work will pave the way for efficient nonlocal wavefront shaping and meta-devices. Wavefront ; Optics ; Physics ; Lens (geology) ; Wavelength ; Computer science Metamaterials and Metasurfaces Applications ; Plasmonic and Surface Plasmon Research ; Advanced Antenna and Metasurface Technologies
Authors: Jing Bai | Dingyao Liu | Xinyu Tian | Yan Wang | Bin‐Bin Cui | Yilin Yang | Shilei Dai | Wensheng Lin | Jixiang Zhu | Jinqiang Wang | Aimin Xu | Zhen Gu | Shiming Zhang
Source: Science Advances (article, 2024-04-19)
Field: Materials Science — Polymers and Plastics

Coin-sized, fully integrated, and minimally invasive continuous glucose monitoring system based on organic electrochemical transistors Continuous glucose monitoring systems (CGMs) are critical toward closed-loop diabetes management. The field's progress urges next-generation CGMs with enhanced antinoise ability, reliability, and wearability. Here, we propose a coin-sized, fully integrated, and wearable CGM, achieved by holistically synergizing state-of-the-art interdisciplinary technologies of biosensors, minimally invasive tools, and hydrogels. The proposed CGM consists of three major parts: (i) an emerging biochemical signal amplifier, the organic electrochemical transistor (OECT), improving the signal-to-noise ratio (SNR) beyond traditional electrochemical sensors; (ii) a microneedle array to facilitate subcutaneous glucose sampling with minimized pain; and (iii) a soft hydrogel to stabilize the skin-device interface. Compared to conventional CGMs, the OECT-CGM offers a high antinoise ability, tunable sensitivity and resolution, and comfort wearability, enabling personalized glucose sensing for future precision diabetes health care. Last, we discuss how OECT technology can help push the limit of detection of current wearable electrochemical biosensors, especially when operating in complicated conditions. Transistor ; Electrochemistry ; Computer science ; Continuous glucose monitoring ; Nanotechnology ; Materials science ; Medicine ; Chemistry ; Electrical engineering ; Electrode ; Internal medicine ; Engineering Conducting polymers and applications ; Electrochemical sensors and biosensors ; Analytical Chemistry and Sensors