jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ken‐ichi Saitow
Source: Bulletin of the Chemical Society of Japan (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Bright silicon quantum dot synthesis and LED design: insights into size–ligand–property relationships from slow- and fast-band engineering Abstract Multicolor, bright silicon quantum dots (SiQDs)—SiQDs with photoluminescence in a range of colors and quantum yields (PLQYs) of &amp;gt;90%—are promising heavy-metal-free light sources for full-color displays, lighting, and biomedical imaging. Colloidal SiQDs can be used to manufacture devices via printing and roll-to-roll processing. Furthermore, the in vivo use of biodegradable SiQDs and Si nanomaterials, for imaging cancer cells and as drug delivery systems, has been demonstrated. However, a large body of research demonstrates that the photoluminescence (PL) wavelength and PLQY of colloidal SiQDs are dependent not only on the SiQD particle size but also on the methods and/or procedures and chemical reagents used to synthesize them. This is because SiQDs are quite sensitive to both the intrinsic properties of Si and external factors. These intrinsic and external factors can be respectively linked to different PL mechanisms: the quantum confinement effect, which produces a slow-decaying “S”-band PL signal, and surface ligand effects, corresponding to fast-decaying “F”-band PL. This review focuses on mechanistic insights into the relationships linking the structures, ligands, and optical properties of SiQDs. Synthesis methods and the application performance of bright multicolor colloidal SiQDs, based on excellent state-of-the-art experimental and theoretical studies, are also reviewed. Photoluminescence ; Chemistry ; Nanotechnology ; Quantum dot ; Nanomaterials ; Nanoparticle ; Ligand (biochemistry) ; Colloid ; Optoelectronics ; Materials science ; Physical chemistry Silicon Nanostructures and Photoluminescence ; Quantum Dots Synthesis And Properties ; Carbon and Quantum Dots Applications
Authors: Giovanni Fortunato | Paul van den Tempel | Ranjita K. Bose
Source: Polymer (article, 2024-01-11)
Field: Materials Science — Polymers and Plastics

Advances in self-healing coatings based on Diels-Alder chemistry Dynamic covalent polymer networks based on the Diels-Alder (DA) cycloaddition between furan and maleimide represent powerful and highly investigated tools in the design of self-healing coatings. In this minireview the most recent demonstrations of repairable coatings based on such platform chemistry are comprehensively discussed. In particular, the trends in the synthesis of DA networks, the enhancement of properties and advanced coating applications over the last ten years are highlighted. Furthermore, the current challenges in terms of the sustainability of DA coatings, performance assessment and the technological feasibility of healing procedures are outlined in the context of a future large-scale implementation. Context (archaeology) ; Coating ; Maleimide ; Self-healing ; Furan ; Nanotechnology ; Sustainability ; Diels–Alder reaction ; Computer science ; Materials science ; Biochemical engineering ; Chemistry ; Organic chemistry ; Engineering ; Catalysis ; Biology Polymer composites and self-healing ; Synthetic Organic Chemistry Methods ; Photochromic and Fluorescence Chemistry
Authors: Burcu Önal Acet | Désirée Gül | Roland H. Stauber | Mehmet Odabaşı | Ömür Acet
Source: Nanomaterials (review, 2024-05-08)
Field: Materials Science — Biomaterials

A Review for Uncovering the “Protein-Nanoparticle Alliance”: Implications of the Protein Corona for Biomedical Applications Understanding both the physicochemical and biological interactions of nanoparticles is mandatory for the biomedical application of nanomaterials. By binding proteins, nanoparticles acquire new surface identities in biological fluids, the protein corona. Various studies have revealed the dynamic structure and nano-bio interactions of the protein corona. The binding of proteins not only imparts new surface identities to nanoparticles in biological fluids but also significantly influences their bioactivity, stability, and targeting specificity. Interestingly, recent endeavors have been undertaken to harness the potential of the protein corona instead of evading its presence. Exploitation of this 'protein-nanoparticle alliance' has significant potential to change the field of nanomedicine. Here, we present a thorough examination of the latest research on protein corona, encompassing its formation, dynamics, recent developments, and diverse bioapplications. Furthermore, we also aim to explore the interactions at the nano-bio interface, paving the way for innovative strategies to advance the application potential of the protein corona. By addressing challenges and promises in controlling protein corona formation, this review provides insights into the evolving landscape of the 'protein-nanoparticle alliance' and highlights emerging. Nanomedicine ; Nanotechnology ; Corona (planetary geology) ; Nanoparticle ; Nanotoxicology ; Nanomaterials ; Materials science ; Chemistry ; Biophysics ; Physics ; Biology Nanoparticle-Based Drug Delivery ; Polymer Surface Interaction Studies ; Iron oxide chemistry and applications
Authors: Tim Schiller | Thomas Scheibel
Source: Communications Materials (article, 2024-04-18)
Field: Materials Science — Biomaterials

Bioinspired and biomimetic protein-based fibers and their applications Abstract Protein-based fibers combine unique mechanical properties with biocompatibility and biodegradability, and often outperform polymer-based fibers. Furthermore, a growing need for sustainable materials has triggered a revival in the study of protein fibers, including keratin, collagen, elastin, and silk, which do not require environmentally damaging petrochemicals for their synthesis. Nowadays, bioinspired research intends to mimic the underlying proteins as well as their natural assembly or spinning processes, to achieve fibers with properties equivalent to those of their natural counterparts. Protein-based fibers can also be used to mimic functions in nature, which can otherwise not be achieved with synthetic polymer-based fibers. Here, we review promising protein fibers, their synthesis, and applications, such as air and water filtration, energy conversion, smart textiles, and in biosensoring and biomedical fields. Nanotechnology ; Materials science ; Chemistry Silk-based biomaterials and applications ; Electrospun Nanofibers in Biomedical Applications ; Additive Manufacturing and 3D Printing Technologies
Authors: Minghao Liu | Qing Xu | Gaofeng Zeng
Source: Angewandte Chemie (article, 2024-04-02)
Field: Materials Science — Materials Chemistry

Ionic Covalent Organic Frameworks in Adsorption and Catalysis Abstract The ion extraction and electro/photo catalysis are promising methods to address environmental and energy issues. Covalent organic frameworks (COFs) are a class of promising template to construct absorbents and catalysts because of their stable frameworks, high surface areas, controllable pore environments, and well‐defined catalytic sites. Among them, ionic COFs as unique class of crystalline porous materials, with charges in the frameworks or along the pore walls, have shown different properties and resulting performance in these applications with those from charge‐neutral COFs. In this review, current research progress based on the ionic COFs for ion extraction and energy conversion, including cationic/anionic materials and electro/photo catalysis is reviewed in terms of the synthesis strategy, modification methods, mechanisms of adsorption and catalysis, as well as applications. Finally, we demonstrated the current challenges and future development of ionic COFs in design strategies and applications. Ionic bonding ; Catalysis ; Covalent bond ; Adsorption ; Cationic polymerization ; Nanotechnology ; Ionic liquid ; Chemistry ; Materials science ; Chemical engineering ; Ion ; Combinatorial chemistry ; Organic chemistry Covalent Organic Framework Applications ; Metal-Organic Frameworks: Synthesis and Applications ; Advanced Photocatalysis Techniques
Authors: Risheng Bai | Guangyuan He | Junyan Li | Lin Li | Tianjun Zhang | Xingxing Wang | Wei Zhang | Yongcun Zou | Jichao Zhang | Donghai Mei | Avelino Corma | Jihong Yu
Source: Angewandte Chemie International Edition (article, 2024-07-29)
Field: Materials Science — Materials Chemistry

Heteroatoms‐Stabilized Single Palladium Atoms on Amorphous Zeolites: Breaking the Tradeoff between Catalytic Activity and Selectivity for Alkyne Semihydrogenation As a fundamental industrial catalytic process, the semihydrogenation of alkynes presents a challenge in striking a balance between activity and selectivity due to the issue of over-hydrogenation. Herein, we develop an efficient catalytic system based on single-atom Pd catalysts supported on boron-containing amorphous zeolites (Pd/AZ-B), achieving the tradeoff breaking between the activity and selectivity for the selective hydrogenation of alkynes. Advanced characterizations and theoretical density functional theory calculations confirm that the incorporated B atoms in the Pd/AZ-B can not only alter the geometric and electronic properties of Pd atoms by controlling the electron migration from Pd but also mitigate the interaction between alkene and the catalyst supports. This boosts the exceptional catalytic efficacy in the semihydrogenation of phenylacetylene to styrene under mild conditions (298 K, 2 bar H<sub>2</sub>), achieving a recorded turnover frequency (TOF) value of 24198 h<sup>-1</sup> and demonstrating 95 % selectivity to styrene at full conversion of phenylacetylene. By comparison, the heteroatom-free amorphous zeolite-anchored Pd nanoparticles and the commercial Lindlar catalyst have styrene selectivities of 73 % and 15 %, respectively, under identical reaction conditions. This work establishes a solid foundation for developing highly active and selective hydrogenation catalysts by controllably optimizing their electronic and steric properties. Heteroatom ; Palladium ; Selectivity ; Alkyne ; Catalysis ; Chemistry ; Amorphous solid ; Combinatorial chemistry ; Materials science ; Organic chemistry Catalytic Processes in Materials Science ; Nanomaterials for catalytic reactions ; Ammonia Synthesis and Nitrogen Reduction
Authors: Yang Gao | Baiyang Jiang | Nathan C. Friede | Arianne C. Hunter | Dylan G. Boucher | Shelley D. Minteer | Matthew S. Sigman | Sarah E. Reisman | Phil S. Baran
Source: Journal of the American Chemical Society (article, 2024-02-07)
Field: Chemistry — Organic Chemistry

Electrocatalytic Asymmetric Nozaki–Hiyama–Kishi Decarboxylative Coupling: Scope, Applications, and Mechanism The first general enantioselective alkyl-Nozaki-Hiyama-Kishi (NHK) coupling reactions are disclosed herein by employing a Cr-electrocatalytic decarboxylative approach. Using easily accessible aliphatic carboxylic acids (via redox-active esters) as alkyl nucleophile synthons, in combination with aldehydes and enabling additives, chiral secondary alcohols are produced in a good yield with high enantioselectivity under mild reductive electrolysis. This reaction, which cannot be mimicked using stoichiometric metal or organic reductants, tolerates a broad range of functional groups and is successfully applied to dramatically simplify the synthesis of multiple medicinally relevant structures and natural products. Mechanistic studies revealed that this asymmetric alkyl e-NHK reaction was enabled by using catalytic tetrakis(dimethylamino)ethylene, which acts as a key reductive mediator to mediate the electroreduction of the Cr<sup>III</sup>/chiral ligand complex. Chemistry ; Alkyl ; Synthon ; Nucleophile ; Enantioselective synthesis ; Combinatorial chemistry ; Catalysis ; Electrolysis ; Yield (engineering) ; Organic chemistry Radical Photochemical Reactions ; Catalytic C–H Functionalization Methods ; Asymmetric Hydrogenation and Catalysis
Authors: Guojun Zhou | Yilin Mao | Jian Zhang | Qiqiong Ren | Мaxim S. Моlokeev | Zhiguo Xia | Xian‐Ming Zhang
Source: Advanced Functional Materials (article, 2024-10-18)
Field: Materials Science — Materials Chemistry

Dynamic Phosphorescence/Fluorescence Switching in Hybrid Metal Halides Toward Time‐Resolved Multi‐Level Anti‐Counterfeiting Abstract Hybrid metal halides (HMHs) with time‐resolved luminescence behavior promise to be a breakthrough in multi‐level anti‐counterfeiting, but controlling the dynamic switching between phosphorescence and fluorescence is extremely challenging. Herein, an array of 0D HMHs is constructed by screening the π ‐conjugated ligand with room‐temperature phosphorescence (RTP). Compared to the organic chromophore, (ETPP) 2 ZrCl 6 possesses a misaligned stacking and rigid structure, contributing to an improved phosphorescence quantum yield (Φ P = 27.50%) and an extended phosphorescence lifetime ( τ = 0.6234 s), as the intervening of inorganic unit [ZrCl 6 ] 2− suppresses the energy losses caused by nonradiative relaxation and prompts the intersystem crossover (ISC) process. Not only that, the interplay of phosphorescence‐fluorescence dual‐mode emission can be intelligently controlled by doping the active metal Te 4+ , resulting in a dynamic switching between RTP phosphorescence and self‐trapped exciton (STE) fluorescence. DFT calculations reveal the governing origins of RTP‐STE from the intermolecular ISC channels and spin‐orbit coupling (SOC) coefficients. These precise images into periodic pixelated arrays enable the multi‐level anti‐counterfeiting and information encryption. This work proposes a fluorescence‐phosphorescence co‐modulating strategy under the premise of dissecting the structural origins for optimizing RTP phosphorescence, which paves the way for designing high‐security‐level anti‐counterfeiting materials. Phosphorescence ; Materials science ; Fluorescence ; Halide ; Optoelectronics ; Photochemistry ; Metal ; Nanotechnology ; Inorganic chemistry ; Optics ; Metallurgy Luminescence and Fluorescent Materials ; Organic Light-Emitting Diodes Research ; Nanocluster Synthesis and Applications
Authors: Hongxuan Liu | Haoyu Yin | Zhiyao Luo | Xiaonan Wang
Source: Synthetic and Systems Biotechnology (article, 2024-07-24)
Field: Materials Science — Materials Chemistry

Integrating chemistry knowledge in large language models via prompt engineering This paper presents a study on the integration of domain-specific knowledge in prompt engineering to enhance the performance of large language models (LLMs) in scientific domains. The proposed domain-knowledge embedded prompt engineering method outperforms traditional prompt engineering strategies on various metrics, including capability, accuracy, F1 score, and hallucination drop. The effectiveness of the method is demonstrated through case studies on complex materials including the MacMillan catalyst, paclitaxel, and lithium cobalt oxide. The results suggest that domain-knowledge prompts can guide LLMs to generate more accurate and relevant responses, highlighting the potential of LLMs as powerful tools for scientific discovery and innovation when equipped with domain-specific prompts. The study also discusses limitations and future directions for domain-specific prompt engineering development. Domain (mathematical analysis) ; Computer science ; Domain-specific language ; Domain knowledge ; Management science ; Engineering ; Software engineering ; Mathematics Machine Learning in Materials Science ; Ferroelectric and Negative Capacitance Devices ; Topic Modeling
Authors: Haresh Kumar | Muhammad Shafiq | Kimmo Kauhaniemi | Mohammed Elmusrati
Source: Energies (review, 2024-02-28)
Field: Materials Science — Materials Chemistry

A Review on the Classification of Partial Discharges in Medium-Voltage Cables: Detection, Feature Extraction, Artificial Intelligence-Based Classification, and Optimization Techniques Medium-voltage (MV) cables often experience a shortened lifespan attributed to insulation breakdown resulting from accelerated aging and anomalous operational and environmental stresses. While partial discharge (PD) measurements serve as valuable tools for assessing the insulation state, complexity arises from the presence of diverse discharge sources, making the evaluation of PD data challenging. The reliability of diagnostics for MV cables hinges on the precise interpretation of PD activity. To streamline the repair and maintenance of cables, it becomes crucial to discern and categorize PD types accurately. This paper presents a comprehensive review encompassing the realms of detection, feature extraction, artificial intelligence, and optimization techniques employed in the classification of PD signals/sources. Its exploration encompasses a variety of sensors utilized for PD detection, data processing methodologies for efficient feature extraction, optimization techniques dedicated to selecting optimal features, and artificial intelligence-based approaches for the classification of PD sources. This synthesized review not only serves as a valuable reference for researchers engaged in the application of methods for PD signal classification but also sheds light on potential avenues for future developments of techniques within the context of MV cables. Context (archaeology) ; Categorization ; Artificial intelligence ; Computer science ; Reliability (semiconductor) ; Feature extraction ; Partial discharge ; Pattern recognition (psychology) ; Feature (linguistics) ; Machine learning ; Voltage ; Engineering ; Data mining ; Power (physics) ; Electrical engineering High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Electrical Fault Detection and Protection
Authors: Siyao Zhu | Yue Zhang | Beichen Zhu | Jiaming Zhang | Yuhuai He | Xu We
Source: International Journal of Fatigue (article, 2024-02-10)
Field: Materials Science — Metals and Alloys

High cycle fatigue life prediction of titanium alloys based on a novel deep learning approach Low-cycle fatigue ; Titanium alloy ; Materials science ; Titanium ; Fatigue testing ; Deep learning ; Metallurgy ; Artificial intelligence ; Computer science ; Composite material ; Alloy Hydrogen embrittlement and corrosion behaviors in metals ; Fatigue and fracture mechanics ; Titanium Alloys Microstructure and Properties
Authors: Zhiyuan Liu | Tianyou Wang | Jin Li | Jian Hua Zeng | Shuai Dong | Fenghua Wang | Fulin Wang | Jie Dong
Source: Journal of Material Science and Technology (article, 2024-10-18)
Field: Materials Science — Biomaterials

Towards high stiffness and ductility—The Mg-Al-Y alloy design through machine learning Ductility (Earth science) ; Alloy ; Materials science ; Stiffness ; Metallurgy ; Composite material ; Structural engineering ; Engineering ; Creep Magnesium Alloys: Properties and Applications ; Metal and Thin Film Mechanics ; Titanium Alloys Microstructure and Properties
Authors: Toshiaki Taniike | Aya Fujiwara | Sunao Nakanowatari | Fernando Garcia‐Escobar | Keisuke Takahashi
Source: Communications Chemistry (article, 2024-01-12)
Field: Materials Science — Materials Chemistry

Automatic feature engineering for catalyst design using small data without prior knowledge of target catalysis The empirical aspect of descriptor design in catalyst informatics, particularly when confronted with limited data, necessitates adequate prior knowledge for delving into unknown territories, thus presenting a logical contradiction. This study introduces a technique for automatic feature engineering (AFE) that works on small catalyst datasets, without reliance on specific assumptions or pre-existing knowledge about the target catalysis when designing descriptors and building machine-learning models. This technique generates numerous features through mathematical operations on general physicochemical features of catalytic components and extracts relevant features for the desired catalysis, essentially screening numerous hypotheses on a machine. AFE yields reasonable regression results for three types of heterogeneous catalysis: oxidative coupling of methane (OCM), conversion of ethanol to butadiene, and three-way catalysis, where only the training set is swapped. Moreover, through the application of active learning that combines AFE and high-throughput experimentation for OCM, we successfully visualize the machine's process of acquiring precise recognition of the catalyst design. Thus, AFE is a versatile technique for data-driven catalysis research and a key step towards fully automated catalyst discoveries. Catalysis ; Computer science ; Process (computing) ; Feature (linguistics) ; Cheminformatics ; Artificial intelligence ; Machine learning ; Feature engineering ; Biochemical engineering ; Chemistry ; Engineering ; Deep learning Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Topic Modeling
Authors: Shu Wu | Jiancong Li | Jin‐Xun Liu | Chuwei Zhu | Tairan Wang | Feng Li | Runhai Ouyang | Wei‐Xue Li
Source: Journal of the American Chemical Society (article, 2024-03-14)
Field: Materials Science — Materials Chemistry

Structure Sensitivity of Metal Catalysts Revealed by Interpretable Machine Learning and First-Principles Calculations The nature of the active sites and their structure sensitivity are the keys to rational design of efficient catalysts but have been debated for almost one century in heterogeneous catalysis. Though the Brønsted-Evans-Polanyi (BEP) relationship along with linear scaling relation has long been used to study the reactivity, explicit geometry, and composition properties are absent in this relationship, a fact that prevents its exploration in structure sensitivity of supported catalysts. In this work, based on interpretable multitask symbolic regression and a comprehensive first-principles data set, we discovered a structure descriptor, the topological under-coordinated number mediated by number of valence electrons and the lattice constant, to successfully address the structure sensitivity of metal catalysts. The database used for training, testing, and transferability investigation includes bond-breaking barriers of 20 distinct chemical bonds over 10 transition metals, two metal crystallographic phases, and 17 different facets. The resulting 2D descriptor composing the structure term and the reaction energy term shows great accuracy to predict the reaction barriers and generalizability over the data set with diverse chemical bonds in symmetry, bond order, and steric hindrance. The theory is physical and concise, providing a constructive strategy not only to understand the structure sensitivity but also to decipher the entangled geometric and electronic effects of metal catalysts. The insights revealed are valuable for the rational design of the site-specific metal catalysts. Chemistry ; Catalysis ; Quantitative structure–activity relationship ; Valence (chemistry) ; Electronic structure ; Chemical physics ; Steric effects ; Valence electron ; Crystal structure ; Theoretical physics ; Generalizability theory ; Scaling ; Computational chemistry ; Crystallography ; Electron ; Quantum mechanics ; Stereochemistry ; Geometry ; Organic chemistry ; Physics ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Surface Chemistry and Catalysis