jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Haiyang Hu | Anil Kumar Pal | Alexander S. Berestennikov | Thomas Weber | Andrei Ştefancu | Emiliano Cortés | Stefan A. Maier | Andreas Tittl
Source: Advanced Optical Materials (article, 2024-02-05)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Surface‐Enhanced Raman Scattering in BIC‐Driven Semiconductor Metasurfaces Abstract Semiconductor‐based surface‐enhanced Raman spectroscopy (SERS) substrates, as a new frontier in the field of SERS, are hindered by their poor electromagnetic field confinement and weak light‐matter interaction. Metasurfaces, a class of 2D artificial materials based on the electromagnetic design of nanophotonic resonators, enable strong electromagnetic field enhancement and optical absorption engineering for a wide range of semiconductors. However, the engineering of semiconductor substrates into metasurfaces for improving SERS activity remains underexplored. Here, an improved SERS metasurface platform is developed that leverages the combination of titanium oxide (TiO 2 ) and the emerging physical concept of optical bound states in the continuum (BICs) to boost the Raman emission. Moreover, fine‐tuning of BIC‐assisted resonant absorption offers a pathway for maximizing the photoinduced charge transfer effect (PICT) in SERS. High values of BIC‐assisted electric field enhancement (|E/E 0 | 2 ≈10 3 ) are achieved, challenging the preconception of weak electromagnetic (EM) field enhancement on semiconductor SERS substrates. The BIC‐assisted TiO 2 metasurface platform offers a new dimension in spectrally‐tunable SERS with earth‐abundant and bio‐compatible semiconductor materials, beyond the traditional plasmonic ones. Materials science ; Semiconductor ; Raman scattering ; Plasmon ; Raman spectroscopy ; Optoelectronics ; Nanophotonics ; Absorption (acoustics) ; Nanotechnology ; Electromagnetic field ; Metamaterial ; Optics ; Physics Gold and Silver Nanoparticles Synthesis and Applications ; Plasmonic and Surface Plasmon Research ; Metamaterials and Metasurfaces Applications
Authors: Mohammad Adnan Raza | Mukesh Sharma | Kushagra Nagori | Parag Jain | Vijayalakshmi Ghosh | Umesh Gupta | M. Ajazuddin
Source: International Journal of Pharmaceutics (review, 2024-09-28)
Field: Materials Science — Biomaterials

Recent trends on polycaprolactone as sustainable polymer-based drug delivery system in the treatment of cancer: Biomedical applications and nanomedicine Nanomedicine ; Polycaprolactone ; Drug delivery ; Drug ; Nanotechnology ; Cancer ; Medicine ; Pharmacology ; Materials science ; Polymer ; Nanoparticle ; Internal medicine biodegradable polymer synthesis and properties ; Electrospun Nanofibers in Biomedical Applications ; Bone Tissue Engineering Materials
Authors: Mengqi Zhang | Rongchen Liu | Fulin Zhang | Hongxiang Zhao | Xia Li | Xianjun Lang | Zhiguang Guo
Source: Journal of Colloid and Interface Science (article, 2024-09-23)
Field: Materials Science — Materials Chemistry

Interfacial design of pyrene-based covalent organic framework for overall photocatalytic H2O2 synthesis in water Pyrene ; Photocatalysis ; Covalent bond ; Chemistry ; Chemical engineering ; Organic chemistry ; Environmental chemistry ; Photochemistry ; Catalysis ; Engineering Covalent Organic Framework Applications ; Advanced Photocatalysis Techniques ; Metal-Organic Frameworks: Synthesis and Applications
Authors: Julian F. Highmoore | Lasith S. Kariyawasam | Scott R. Trenor | Ying Yang
Source: Green Chemistry (article, 2024-01-01)
Field: Materials Science — Biomaterials

Design of depolymerizable polymers toward a circular economy While our society is facing the challenge of accumulating plastic waste, this review discusses recent advances towards polymer circularity with an emphasis on manipulations of the monomer–polymer equilibrium to create chemically recyclable polymers. Circular economy ; Polymer ; Chemistry ; Business ; Chemical engineering ; Organic chemistry ; Engineering ; Biology ; Ecology biodegradable polymer synthesis and properties
Authors: Caiming Wang | Anna Krupp | Carsten Strohmann | Bastian Grabe | Charles C. J. Loh
Source: Journal of the American Chemical Society (article, 2024-04-02)
Field: Chemistry — Organic Chemistry

Harnessing Multistep Chalcogen Bonding Activation in the α-Stereoselective Synthesis of Iminoglycosides The use of noncovalent interactions (NCIs) has received significant attention as a pivotal synthetic handle. Recently, the exploitation of unconventional NCIs has gained considerable traction in challenging reaction manifolds such as glycosylation due to their capacity to facilitate entry into difficult-to-access sugars and glycomimetics. While investigations involving oxacyclic pyrano- or furanoside scaffolds are relatively common, methods that allow the selective synthesis of biologically important iminosugars are comparatively rare. Here, we report the capacity of a phosphonochalcogenide (PCH) to catalyze the stereoselective α-iminoglycosylation of iminoglycals with a wide array of glycosyl acceptors with remarkable protecting group tolerance. Mechanistic studies have illuminated the counterintuitive role of the catalyst in serially activating both the glycosyl donor and acceptor in the up/downstream stages of the reaction through chalcogen bonding (ChB). The dynamic interaction of chalcogens with substrates opens up new mechanistic opportunities based on iterative ChB catalyst engagement and disengagement in multiple elementary steps. Chemistry ; Chalcogen ; Glycosylation ; Stereoselectivity ; Glycosyl ; Combinatorial chemistry ; Catalysis ; Chemoselectivity ; Glycosyl donor ; Stereochemistry ; Organic chemistry ; Biochemistry Carbohydrate Chemistry and Synthesis ; Glycosylation and Glycoproteins Research ; Enzyme Production and Characterization
Authors: Ming Liu | Muchun Guo | Haiyan Lyu | Yingda Lai | Yuke Zhu | Fengkai Guo | Yueyang Yang | Kuai Yu | Xingyan Dong | Zihang Liu | Wei Cai | Matthias Wuttig | Yuan Yu | Jiehe Sui
Source: Nature Communications (article, 2024-09-27)
Field: Materials Science — Materials Chemistry

Doping strategy in metavalently bonded materials for advancing thermoelectric performance Metavalent bonding is a unique bonding mechanism responsible for exceptional properties of materials used in thermoelectric, phase-change, and optoelectronic devices. For thermoelectrics, the desired performance of metavalently bonded materials can be tuned by doping foreign atoms. Incorporating dopants to form solid solutions or second phases is a crucial route to tailor the charge and phonon transport. Yet, it is difficult to predict if dopants will form a secondary phase or a solid solution, which hinders the tailoring of microstructures and material properties. Here, we propose that the solid solution is more easily formed between metavalently bonded solids, while precipitates prefer to exist in systems mixed by metavalently bonded and other bonding mechanisms. We demonstrate this in a metavalently bonded GeTe compound alloyed with different sulfides. We find that S can dissolve in the GeTe matrix when alloyed with metavalently bonded PbS. In contrast, S-rich second phases are omnipresent via alloying with covalently bonded GeS and SnS. Benefiting from the reduced phonon propagation and the optimized electrical transport properties upon doping PbS in GeTe, a high figure-of-merit ZT of 2.2 at 773 K in (Ge<sub>0.84</sub>Sb<sub>0.06</sub>Te<sub>0.9</sub>)(PbSe)<sub>0.05</sub>(PbS)<sub>0.05</sub> is realized. This strategy can be applied to other metavalently bonded materials to design properties beyond thermoelectrics. Dopant ; Materials science ; Thermoelectric materials ; Thermoelectric effect ; Doping ; Nanotechnology ; Figure of merit ; Phase (matter) ; Phonon ; Solid solution ; Optoelectronics ; Composite material ; Condensed matter physics ; Metallurgy ; Thermal conductivity ; Chemistry ; Thermodynamics ; Organic chemistry Advanced Thermoelectric Materials and Devices ; Chalcogenide Semiconductor Thin Films ; Perovskite Materials and Applications
Authors: Yung‐Chang Lin | Rika Matsumoto | Qiunan Liu | Pablo Solís‐Fernández | Ming‐Deng Siao | Po‐Wen Chiu | Hiroki Ago | Kazu Suenaga
Source: Nature Communications (article, 2024-01-24)
Field: Materials Science — Materials Chemistry

Alkali metal bilayer intercalation in graphene Alkali metal (AM) intercalation between graphene layers holds promise for electronic manipulation and energy storage, yet the underlying mechanism remains challenging to fully comprehend despite extensive research. In this study, we employ low-voltage scanning transmission electron microscopy (LV-STEM) to visualize the atomic structure of intercalated AMs (potassium, rubidium, and cesium) in bilayer graphene (BLG). Our findings reveal that the intercalated AMs adopt bilayer structures with hcp stacking, and specifically a C<sub>6</sub>M<sub>2</sub>C<sub>6</sub> composition. These structures closely resemble the bilayer form of fcc (111) structure observed in AMs under high-pressure conditions. A negative charge transferred from bilayer AMs to graphene layers of approximately 1~1.5×10<sup>14</sup> e<sup>-</sup>/cm<sup>-2</sup> was determined by electron energy loss spectroscopy (EELS), Raman, and electrical transport. The bilayer AM is stable in BLG and graphite superficial layers but absent in the graphite interior, primarily dominated by single-layer AM intercalation. This hints at enhancing AM intercalation capacity by thinning the graphite material. Intercalation (chemistry) ; Graphene ; Bilayer graphene ; Alkali metal ; Materials science ; Bilayer ; Nanotechnology ; Metal ; Chemistry ; Inorganic chemistry ; Membrane ; Metallurgy ; Biochemistry Graphene research and applications ; Advancements in Battery Materials ; Supercapacitor Materials and Fabrication
Authors: Donghyo Hahm | Valerio Pinchetti | Clément Livache | Namyoung Ahn | Jungchul Noh | Xueyang Li | Jun Du | Kaifeng Wu | Victor I. Klimov
Source: Nature Materials (article, 2024-11-22)
Field: Materials Science — Materials Chemistry

Colloidal quantum dots enable tunable liquid-state lasers Present-day liquid-state lasers are based on organic dyes. Here we demonstrate an alternative class of liquid lasers that use solutions of colloidal quantum dots (QDs). Previous efforts to realize such devices have been hampered by the fast non-radiative Auger recombination of multicarrier states required for optical gain. Here we overcome this challenge by using type-(I + II) QDs, which feature a trion-like optical gain state with strongly suppressed Auger recombination. When combined with a Littrow optical cavity, static (non-circulated) solutions of these QDs exhibit stable lasing tunable from 634 nm to 575 nm. These results indicate the feasibility of technologically viable dye-like QD lasers that exhibit broad spectral tunability and, importantly, provide stable operation without the need for a circulation system-a standard attribute of traditional dye lasers. The latter opens the door to less complex and more compact devices that can be readily integrated with various optical and electro-optical systems. An additional advantage of these lasers is the wide range of potentially available wavelengths that can be selected by controlling the composition, size and structure of the QDs. Lasing threshold ; Trion ; Laser ; Materials science ; Optoelectronics ; Auger effect ; Quantum dot ; Quantum dot laser ; Auger ; Nanotechnology ; Optics ; Semiconductor laser theory ; Wavelength ; Physics ; Semiconductor Quantum Dots Synthesis And Properties ; Strong Light-Matter Interactions ; Semiconductor Lasers and Optical Devices
Authors: Zhenze Yang | Weike Ye | X. L. Lei | Daniel Schweigert | Ha-Kyung Kwon | Arash Khajeh
Source: npj Computational Materials (article, 2024-12-19)
Field: Materials Science — Materials Chemistry

De novo design of polymer electrolytes using GPT-based and diffusion-based generative models Solid polymer electrolytes offer promising advancements for next-generation batteries, boasting superior safety, enhanced specific energy, and extended lifespans over liquid electrolytes. However, low ionic conductivity and the vast polymer space hinder commercialization. This study leverages generative AI for de novo polymer electrolyte design, comparing GPT-based and diffusion-based models with extensive hyperparameter tuning. We evaluate these models using various metrics and full-atom molecular dynamics simulations. Among 46 candidates tested, 17 exhibit superior ionic conductivity, surpassing existing polymers in our database, with some doubling the conductivity values. Additionally, by adopting pretraining and fine-tuning methodologies, we significantly enhance our generative models, achieving quicker convergence, better performance with limited data, and greater diversity. Our method efficiently generates a large number of novel, diverse, and valid polymers, with a high likelihood of synthesizability, enabling the identification of promising candidates with markedly improved efficiency and effectiveness for practical applications. Electrolyte ; Diffusion ; Polymer electrolytes ; Generative grammar ; Polymer ; Generative model ; Materials science ; Chemical engineering ; Computer science ; Chemistry ; Engineering ; Physics ; Thermodynamics ; Artificial intelligence ; Composite material ; Physical chemistry Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Advancements in Battery Materials
Authors: Elnaz Tamjid | Parvin Najafi | Mohammad Amin Khalili | Negar Shokouhnejad | Mahsa Karimi | Nafise Sepahdoost
Source: Discover Nano (article, 2024-02-19)
Field: Materials Science — Polymers and Plastics

Review of sustainable, eco-friendly, and conductive polymer nanocomposites for electronic and thermal applications: current status and future prospects Biodegradable polymer nanocomposites (BPNCs) are advanced materials that have gained significant attention over the past 20 years due to their advantages over conventional polymers. BPNCs are eco-friendly, cost-effective, contamination-resistant, and tailorable for specific applications. Nevertheless, their usage is limited due to their unsatisfactory physical and mechanical properties. To improve these properties, nanofillers are incorporated into natural polymer matrices, to enhance mechanical durability, biodegradability, electrical conductivity, dielectric, and thermal properties. Despite the significant advances in the development of BPNCs over the last decades, our understanding of their dielectric, thermal, and electrical conductivity is still far from complete. This review paper aims to provide comprehensive insights into the fundamental principles behind these properties, the main synthesis, and characterization methods, and their functionality and performance. Moreover, the role of nanofillers in strength, permeability, thermal stability, biodegradability, heat transport, and electrical conductivity is discussed. Additionally, the paper explores the applications, challenges, and opportunities of BPNCs for electronic devices, thermal management, and food packaging. Finally, this paper highlights the benefits of BPNCs as biodegradable and biodecomposable functional materials to replace traditional plastics. Finally, the contemporary industrial advances based on an overview of the main stakeholders and recently commercialized products are addressed. Environmentally friendly ; Materials science ; Current (fluid) ; Nanocomposite ; Conductive polymer ; Electrical conductor ; Nanotechnology ; Polymer nanocomposite ; Thermal ; Engineering physics ; Polymer ; Engineering ; Composite material ; Electrical engineering ; Physics Conducting polymers and applications ; Polymer Nanocomposite Synthesis and Irradiation ; Carbon Nanotubes in Composites
Authors: Aun Raza | Wei Wu
Source: Asian Journal of Pharmaceutical Sciences (article, 2024-08-23)
Field: Chemistry — Inorganic Chemistry

Metal-organic frameworks in oral drug delivery Metal-organic frameworks (MOFs) offer innovative solutions to the limitations of traditional oral drug delivery systems through their unique combination of metal ions and organic ligands. This review systematically examines the structural properties and principles of MOFs, setting the stage for their application in drug delivery. It discusses various classes of MOFs, including those based on zirconium, iron, zinc, copper, titanium, aluminum, potassium, and magnesium, assessing their drug-loading capacities, biocompatibility, and controlled release mechanisms. The effectiveness of MOFs is illustrated through case studies that highlight their capabilities in enhancing drug solubility, providing protection against the harsh gastrointestinal environment, and enabling precise drug release. The review addresses potential challenges, particularly the toxicity concerns associated with MOFs, and calls for further research into their biocompatibility and interactions with biological systems. It concludes by emphasizing the potential of MOFs in revolutionizing oral drug delivery, highlighting the critical need for comprehensive research to harness their full potential in clinical applications. Drug ; Drug delivery ; Metal-organic framework ; Medicine ; Business ; Pharmacology ; Intensive care medicine ; Chemistry ; Nanotechnology ; Materials science ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Dendrimers and Hyperbranched Polymers ; X-ray Diffraction in Crystallography
Authors: Xingyue Shi | Linming Zhou | Yuhui Huang | Yongjun Wu | Zijian Hong
Source: Materials Genome Engineering Advances (review, 2024-06-01)
Field: Materials Science — Materials Chemistry

A review on the applications of graph neural networks in materials science at the atomic scale Abstract In recent years, interdisciplinary research has become increasingly popular within the scientific community. The fields of materials science and chemistry have also gradually begun to apply the machine learning technology developed by scientists from computer science. Graph neural networks (GNNs) are new machine learning models with powerful feature extraction, relationship inference, and compositional generalization capabilities. These advantages drive researchers to design computational models to accelerate material property prediction and new materials design, dramatically reducing the cost of traditional experimental methods. This review focuses on the principles and applications of the GNNs. The basic concepts and advantages of the GNNs are first introduced and compared to the traditional machine learning and neural networks. Then, the principles and highlights of seven classic GNN models, namely crystal graph convolutional neural networks, iCGCNN, Orbital Graph Convolutional Neural Network, MatErials Graph Network, Global Attention mechanism with Graph Neural Network, Atomistic Line Graph Neural Network, and BonDNet are discussed. Their connections and differences are also summarized. Finally, insights and prospects are provided for the rapid development of GNNs in materials science at the atomic scale. Computer science ; Artificial intelligence ; Graph ; Inference ; Artificial neural network ; Convolutional neural network ; Machine learning ; Network science ; Deep learning ; Theoretical computer science ; Data science ; Complex network Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Simone Perego | Luigi Bonati
Source: npj Computational Materials (article, 2024-12-19)
Field: Materials Science — Materials Chemistry

Data efficient machine learning potentials for modeling catalytic reactivity via active learning and enhanced sampling Abstract Simulating catalytic reactivity under operative conditions poses a significant challenge due to the dynamic nature of the catalysts and the high computational cost of electronic structure calculations. Machine learning potentials offer a promising avenue to simulate dynamics at a fraction of the cost, but they require datasets containing all relevant configurations, particularly reactive ones. Here, we present a scheme to construct reactive potentials in a data-efficient manner. This is achieved by combining enhanced sampling methods first with Gaussian processes to discover transition paths and then with graph neural networks to obtain a uniformly accurate description. The necessary configurations are extracted via a Data-Efficient Active Learning (DEAL) procedure based on local environment uncertainty. We validated our approach by studying several reactions related to the decomposition of ammonia on iron-cobalt alloy catalysts. Our scheme proved to be efficient, requiring only ~1000 DFT calculations per reaction, and robust, sampling reactive configurations from the different accessible pathways. Using this potential, we calculated free energy profiles and characterized reaction mechanisms, showing the ability to provide microscopic insights into complex processes under dynamic conditions. Active learning (machine learning) ; Reactivity (psychology) ; Sampling (signal processing) ; Computer science ; Machine learning ; Catalysis ; Artificial intelligence ; Biological system ; Chemistry ; Biology ; Medicine ; Organic chemistry Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Farooq Ahmed Shah | Noreen Sher Akbar | Tayyab Zamir | Magda Abd El‐Rahman | Waqas Khan
Source: Swarm and Evolutionary Computation (article, 2024-11-20)
Field: Materials Science — Materials Chemistry

Thermal energy analysis using artificial neural network and particle swarm optimization approach in partially ionized hyperbolic tangent material with ternary hybrid nanomaterials Ternary operation ; Tangent ; Computer science ; Artificial neural network ; Particle swarm optimization ; Hyperbolic function ; Nanomaterials ; Energy (signal processing) ; Materials science ; Algorithm ; Artificial intelligence ; Mathematical analysis ; Physics ; Mathematics ; Geometry ; Nanotechnology Thermal properties of materials ; Polymer Nanocomposites and Properties ; Polymer composites and self-healing
Authors: Xinyu Lu | Hao-ping Wu | Hao Ma | Hui Li | Jia Li | Yan-Ti Liu | Zheng-Yan Pan | Yi Xie | Lei Wang | Bin Ren | Guokun Liu
Source: Analytical Chemistry (article, 2024-04-25)
Field: Materials Science — Materials Chemistry

Deep Learning-Assisted Spectrum–Structure Correlation: State-of-the-Art and Perspectives Spectrum-structure correlation is playing an increasingly crucial role in spectral analysis and has undergone significant development in recent decades. With the advancement of spectrometers, the high-throughput detection triggers the explosive growth of spectral data, and the research extension from small molecules to biomolecules accompanies massive chemical space. Facing the evolving landscape of spectrum-structure correlation, conventional chemometrics becomes ill-equipped, and deep learning assisted chemometrics rapidly emerges as a flourishing approach with superior ability of extracting latent features and making precise predictions. In this review, the molecular and spectral representations and fundamental knowledge of deep learning are first introduced. We then summarize the development of how deep learning assist to establish the correlation between spectrum and molecular structure in the recent 5 years, by empowering spectral prediction (i.e., forward structure-spectrum correlation) and further enabling library matching and <i>de novo</i> molecular generation (i.e., inverse spectrum-structure correlation). Finally, we highlight the most important open issues persisted with corresponding potential solutions. With the fast development of deep learning, it is expected to see ultimate solution of establishing spectrum-structure correlation soon, which would trigger substantial development of various disciplines. Chemistry ; Spectrum (functional analysis) ; Correlation ; Statistical physics ; Geometry ; Quantum mechanics Machine Learning in Materials Science ; Spectroscopy Techniques in Biomedical and Chemical Research ; Cell Image Analysis Techniques