jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Pengyu Dai | Jingqi Li | Tiedong Sun | Chunxia Chen
Source: The Chemical Record (review, 2023-04-03)
Field: Materials Science — Materials Chemistry

Rhodium‐Based Nanozymes: Recent Advances and Challenges Rhodium (Rh) is a non-toxic transition metal used as various nanomaterials with unique structures and properties. Rh-based nanozymes can mimic the activities of natural enzymes, overcome the limitation of the application scope of natural enzymes, and interact with various biological microenvironments to play a variety of functions. Rh-based nanozymes can be synthesized in various ways, and different modification and regulation methods can also enable users to control catalytic performance by adjusting enzyme active sites. The construction of Rh-based nanozymes has attracted great interest in the biomedical field and impacted the industry and other areas. This paper reviews the typical synthesis and modification strategies, unique properties, applications, challenges, and prospects of Rh-based nanozymes. Next, the unique features of Rh-based nanozymes are emphasized, including adjustable enzyme-like activity, stability, and biocompatibility. In addition, we discuss Rh-based nanozymes biosensors and detection, biomedical therapy, and industrial and other applications. Finally, the future challenges and prospects of Rh-based nanozymes are proposed. Nanotechnology ; Rhodium ; Scope (computer science) ; Chemistry ; Biochemical engineering ; Computer science ; Catalysis ; Materials science ; Engineering ; Biochemistry Advanced Nanomaterials in Catalysis ; Nanocluster Synthesis and Applications ; Advanced biosensing and bioanalysis techniques
Authors: Aileen M. Eagleton | Emma K. Ambrogi | Sophia A. Miller | Nataliia Vereshchuk | Katherine A. Mirica
Source: Angewandte Chemie International Edition (review, 2023-08-24)
Field: Chemistry — Inorganic Chemistry

Fiber Integrated Metal‐Organic Frameworks as Functional Components in Smart Textiles Owing to high modularity and synthetic tunability, metal-organic frameworks (MOFs) on textiles are poised to contribute to the development of state-of-the-art wearable systems with multifunctional performance. While these composite materials have demonstrated promising functions in sensing, filtration, detoxification, and biomedicine, their applicability in multifunctional systems is only beginning to materialize. This review highlights the multifunctionality and versatility of MOF-integrated textile systems. It summarizes the operational goals of MOF@textile composites, encompassing sensing, filtration, detoxification, drug delivery, UV protection, and photocatalysis. Building upon these recent advances, this review concludes with an outlook on emerging opportunities for the diverse applications of MOF@textile systems in the realm of smart wearables. Textile ; Nanotechnology ; Modularity (biology) ; Metal-organic framework ; Wearable computer ; Materials science ; Biomedicine ; Biochemical engineering ; Systems engineering ; Computer science ; Engineering ; Embedded system ; Composite material ; Chemistry Metal-Organic Frameworks: Synthesis and Applications ; Advanced Nanomaterials in Catalysis ; Carbon and Quantum Dots Applications
Authors: Weichao Xue | Luca Pesce | B. Adinarayana | Tanya K. Ronson | Kai Wu | Dawei Zhang | Nicolas Vanthuyne | Thierry Brotin | Alexandre Martinez | Giovanni M. Pavan | Jonathan R. Nitschke
Source: Journal of the American Chemical Society (article, 2023-02-27)
Field: Chemistry — Organic Chemistry

Subtle Stereochemical Effects Influence Binding and Purification Abilities of an Fe<sup>II</sup><sub>4</sub>L<sub>4</sub> Cage A tetrahedral Fe<sup>II</sup><sub>4</sub>L<sub>4</sub> cage assembled from the coordination of triangular chiral, face-capping ligands to iron(II). This cage exists as two diastereomers in solution, which differ in the stereochemistry of their metal vertices, but share the same point chirality of the ligand. The equilibrium between these cage diastereomers was subtly perturbed by guest binding. This perturbation from equilibrium correlated with the size and shape fit of the guest within the host; insight as to the interplay between stereochemistry and fit was provided by atomistic well-tempered metadynamics simulations. The understanding thus gained as to the stereochemical impact on guest binding enabled the design of a straightforward process for the resolution of the enantiomers of a racemic guest. Chemistry ; Diastereomer ; Chirality (physics) ; Enantiomer ; Cage ; Stereochemistry ; Crystallography ; Tetrahedron ; Metadynamics ; Metal ; Computational chemistry ; Molecular dynamics ; Organic chemistry ; Chiral perturbation theory Supramolecular Chemistry and Complexes ; Magnetism in coordination complexes ; Hemoglobin structure and function
Authors: Leyi Chen | Lei Wang | Quanli Shen | Yonggang Liu | Zhenghua Tang
Source: Materials Chemistry Frontiers (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

All-alkynyl-protected coinage metal nanoclusters: from synthesis to electrocatalytic CO<sub>2</sub>reduction applications Alkynyl-protected metal nanoclusters possess unique advantages, and the recent progress on the controllable synthesis and CO 2 electroreduction application is discussed, with some explicit examples to elaborate the structure-performance relationship. Nanoclusters ; Metal ; Materials science ; Reduction (mathematics) ; Nanotechnology ; Metallurgy Nanocluster Synthesis and Applications ; Quantum Dots Synthesis And Properties ; Gold and Silver Nanoparticles Synthesis and Applications
Authors: Fulin Zhang | Xiaoyun Dong | Yuexin Wang | Xianjun Lang
Source: Small (article, 2023-05-17)
Field: Materials Science — Materials Chemistry

Design and Synthesis of a Triazine‐Based sp<sup>2</sup> Carbon‐Conjugated Covalent Organic Framework for Blue Light Photocatalysis Fully conjugated covalent organic frameworks (COFs) can exhibit great potential in semiconductor photocatalysis. But their syntheses remain elusive due to the low reversibility of vinylene linkage. Herein, by tuning the amount of base and temperature, a novel triazine-based sp<sup>2</sup> carbon-conjugated COF (TA-sp<sup>2</sup> c-COF) is successfully constructed over Cs<sub>2</sub> CO<sub>3</sub> . Besides, the influence of modulating factors on the chemical and optoelectronic properties of TA-sp<sup>2</sup> c-COF is thoroughly investigated. TA-sp<sup>2</sup> c-COF adopts an eclipsed AA stacking structure with uniform micropores (1.4 nm). The blue light photocatalysis of the highly crystalline TA-sp<sup>2</sup> c-COF is established for the selective oxidative coupling of amines with oxygen, and the predominant role of superoxide is identified in forming imines. This work foretells that meticulous modulation of reaction conditions is the key to constructing sp<sup>2</sup> carbon-conjugated COFs toward solar photocatalysis. Photocatalysis ; Conjugated system ; Covalent bond ; Stacking ; Materials science ; Triazine ; Covalent organic framework ; Carbon fibers ; Photochemistry ; Carbon nitride ; Chemical engineering ; Chemistry ; Organic chemistry ; Polymer chemistry ; Composite number ; Catalysis ; Polymer Covalent Organic Framework Applications ; Metal-Organic Frameworks: Synthesis and Applications ; Advanced Photocatalysis Techniques
Authors: Sobia Noreen | Andreas Bernkop‐Schnürch
Source: Advanced Functional Materials (article, 2023-10-20)
Field: Materials Science — Biomaterials

Thiolated Poly‐ and Oligosaccharide‐Based Hydrogels for Tissue Engineering and Wound Healing Abstract Due to thiolation of poly‐ and oligosaccharides numerous favorable properties for tissue engineering and wound healing can be introduced. Poly‐ and oligosaccharides can be thiolated via hydroxyl‐to‐thiol conversions or the covalent attachment of sulfhydryl ligands to hydroxyl, carbonic acid or amino groups on them. Since thiolated poly‐ and oligosaccharides can cross‐link via disulfide bonds, they form stable 3D networks with defined microarchitecture, stiffness, elasticity, and degradability. Furthermore, thiol groups can enhance cell adhesion since cells exhibit cysteine‐rich subdomains on their surface that form disulfide bonds with them. Sulfhydryl groups can also participate in cell signaling pathways favoring various cellular processes like proliferation, migration, spreading, and differentiation that are beneficial for tissue engineering and wound healing. In addition, a controlled release of active ingredients such as growth factors being bound via disulfide bonds to thiolated poly‐ and oligosaccharides can be achieved via thiol/disulfide exchange reactions. Over the last two decades, the number of thiolated poly‐ and oligosaccharides such as thiolated hyaluronic acid and thiolated chitosan used for tissue engineering and wound healing has increased tremendously. Within this review, an overview is provided about the chemistry of thiolated poly‐ and oligosaccharides, their key properties, applications and performance in clinical trials and as marketed products. Self-healing hydrogels ; Tissue engineering ; Covalent bond ; Thiol ; Disulfide bond ; Cysteine ; Hyaluronic acid ; Wound healing ; Cell adhesion ; Chemistry ; Combinatorial chemistry ; Adhesion ; Materials science ; Polymer chemistry ; Biochemistry ; Organic chemistry ; Biomedical engineering ; Enzyme Electrospun Nanofibers in Biomedical Applications ; Silk-based biomaterials and applications ; biodegradable polymer synthesis and properties
Authors: G.P. Abhilash | Devansh Sharma | Suryasarathi Bose | C. Shivakumara
Source: Heliyon (article, 2023-02-10)
Field: Materials Science — Electronic, Optical and Magnetic Materials

PANI-wrapped BaFe12O19 and SrFe12O19 with rGO composite materials for electromagnetic interference shielding applications We report electromagnetic interference (EMI) shielding efficiency in the PANI-wrapped BaFe<sub>12</sub>O<sub>19</sub> and SrFe<sub>12</sub>O<sub>19</sub> with rGO composites. Barium and strontium hexaferrites were synthesized using the nitrate citrate gel combustion method. These hexaferrites were polymerized <i>in situ</i> with aniline. The PANI-coated ferrite-based composite materials were developed along with reduced graphene oxide (rGO) in acrylonitrile butadiene styrene (ABS) polymer, and their shielding effectiveness was assessed in X-band frequency range (8.2-12.4 GHz). The reflection (SE<sub>R</sub>) and absorption (SE<sub>A</sub>) mechanism of shielding effectiveness was discussed with the different rGO concentrations. The results reveal that 5 wt% of rGO with PANI-coated barium and strontium hexaferrite polymer composites exhibit shielding efficiency of 21.5 dB and 19.5 dB, respectively, for 1 mm thickness composite. These hexaferrite polymer-based composite materials can be used as an attractive candidate for EM shielding materials in various technological applications. Materials science ; Electromagnetic shielding ; Composite material ; Composite number ; Graphene ; Acrylonitrile butadiene styrene ; Ferrite (magnet) ; Strontium ; Polymer ; Barium ferrite ; Reflection loss ; Electromagnetic interference ; Natural rubber ; Oxide ; Nanotechnology ; Metallurgy Electromagnetic wave absorption materials ; Magnetic Properties and Synthesis of Ferrites ; Advancements in Battery Materials
Authors: Amar M. Patil | Sunil Moon | Sanjib Baran Roy | J. Y. Ha | Nilesh R. Chodankar | Deepak P. Dubal | Arti A. Jadhav | Guoqing Guan | Keonwook Kang | Seong Chan Jun
Source: Small (article, 2023-05-08)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Electronic Structure Engineered Heteroatom Doped All Transition Metal Sulfide Carbon Confined Heterostructure for Extrinsic Pseudocapacitor Ultra-high energy density battery-type materials are promising candidates for supercapacitors (SCs); however, slow ion kinetics and significant volume expansion remain major barriers to their practical applications. To address these issues, hierarchical lattice distorted α-/γ-MnS@Co<sub>x</sub> S<sub>y</sub> core-shell heterostructure constrained in the sulphur (S), nitrogen (N) co-doped carbon (C) metal-organic frameworks (MOFs) derived nanosheets (α-/γ-MnS@Co<sub>x</sub> S<sub>y</sub> @N, SC) have been developed. The coordination bonding among Co<sub>x</sub> S<sub>y</sub> , and α-/γ-MnS nanoparticles at the interfaces and the π-π stacking interactions developed across α-/γ-MnS@Co<sub>x</sub> S<sub>y</sub> and N, SC restrict volume expansion during cycling. Furthermore, the porous lattice distorted heteroatom-enriched nanosheets contain a sufficient number of active sites to allow for efficient electron transportation. Density functional theory (DFT) confirms the significant change in electronic states caused by heteroatom doping and the formation of core-shell structures, which provide more accessible species with excellent interlayer and interparticle conductivity, resulting in increased electrical conductivity. . The α-/γ-MnS@Co<sub>x</sub> S<sub>y</sub> @N, SC electrode exhibits an excellent specific capacity of 277 mA hg<sup>-1</sup> and cycling stability over 23 600 cycles. A quasi-solid-state flexible extrinsic pseudocapacitor (QFEPs) assembled using layer-by-layer deposited multi-walled carbon nanotube/Ti<sub>3</sub> C<sub>2</sub> T<sub>X</sub> nanocomposite negative electrode. QFEPs deliver specific energy of 64.8 Wh kg<sup>-1</sup> (1.62 mWh cm<sup>-3</sup> ) at a power of 933 W kg<sup>-1</sup> and 92% capacitance retention over 5000 cycles. Pseudocapacitor ; Materials science ; Heteroatom ; Supercapacitor ; Density functional theory ; Nanotechnology ; Chemical engineering ; Nanocomposite ; Electrode ; Capacitance ; Chemistry ; Physical chemistry ; Computational chemistry ; Organic chemistry Supercapacitor Materials and Fabrication ; Advanced battery technologies research ; MXene and MAX Phase Materials
Authors: Daniel Wines | Ramya Gurunathan | Kevin F. Garrity | Brian DeCost | Adam J. Biacchi | Francesca Tavazza | Kamal Choudhary
Source: Applied Physics Reviews (article, 2023-10-18)
Field: Materials Science — Materials Chemistry

Recent progress in the JARVIS infrastructure for next-generation data-driven materials design The joint automated repository for various integrated simulations (JARVIS) infrastructure at the National Institute of Standards and Technology is a large-scale collection of curated datasets and tools with more than 80 000 materials and millions of properties. JARVIS uses a combination of electronic structure, artificial intelligence, advanced computation, and experimental methods to accelerate materials design. Here, we report some of the new features that were recently included in the infrastructure, such as (1) doubling the number of materials in the database since its first release, (2) including more accurate electronic structure methods such as quantum Monte Carlo, (3) including graph neural network-based materials design, (4) development of unified force-field, (5) development of a universal tight-binding model, (6) addition of computer-vision tools for advanced microscopy applications, (7) development of a natural language processing tool for text-generation and analysis, (8) debuting a large-scale benchmarking endeavor, (9) including quantum computing algorithms for solids, (10) integrating several experimental datasets, and (11) staging several community engagement and outreach events. New classes of materials, properties, and workflows added to the database include superconductors, two-dimensional (2D) magnets, magnetic topological materials, metal-organic frameworks, defects, and interface systems. The rich and reliable datasets, tools, documentation, and tutorials make JARVIS a unique platform for modern materials design. JARVIS ensures the openness of data and tools to enhance reproducibility and transparency and to promote a healthy and collaborative scientific environment. Computer science ; Workflow ; Benchmarking ; Documentation ; Outreach ; Data science ; Database Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Catalysis and Oxidation Reactions
Authors: Rajeev Kumar | Shubham Sharma | Piyush Gulati | Jaiinder Preet Singh | Kanishka Jha | Changhe Li | Abhinav Kumar | Sayed M. Eldin | Mohamed Abbas
Source: Journal of Materials Research and Technology (article, 2023-06-08)
Field: Materials Science — Polymers and Plastics

Fabrication and characterizations of glass fiber-reinforced functional leaf spring composites with or without microcapsule-based dicyclopentadiene as self-healing agent for automobile industrial applications: comparative analysis This study described a critical review of the biological system of self-curing agents and catalysts in which damage triggers an automatic healing response. In the first phase, a glass fiber-reinforced composite (GFRC) mono-leaf spring was prepared, which is made of glass fiber with a cement-based metal matrix. GFRC was further embedded with a microcapsule-based self-healing agent dicyclopentadiene (DCPD) that prevents sudden breakdown/failure of automobile suspension components resulting in micro-cracks produced in the material due to constant load application. In this paper, GFRC mono leaf spring samples were prepared with and without a healing agent under three different categories of varying thicknesses 20, 30, and 40 mm. In the second phase, the load-carrying capacity of all the samples was investigated and found a continuous increase in load-carrying capacity. Percentage increase in load carrying capacity before the time break was 1.09%, 1.42%, and 1.08% followed by time break of 05 min was 24.24%, 17.67%, and 21.67% respectively. It was clearly identified from the results that the addition of microcapsule-based healing substituents increases the load-carrying capacity of GFRC mono-leaf spring and avoids sudden fracture. Materials science ; Dicyclopentadiene ; Composite material ; Curing (chemistry) ; Glass fiber ; Composite number ; Self-healing ; Fabrication ; Fiber ; Polymer ; Polymerization Natural Fiber Reinforced Composites ; Mechanical Engineering and Vibrations Research ; Polymer composites and self-healing
Authors: Hitarth Choubisa | Petar Todorović́ | João M. Pina | Darshan H. Parmar | Ziliang Li | Oleksandr Voznyy | Isaac Tamblyn | Edward H. Sargent
Source: npj Computational Materials (article, 2023-06-29)
Field: Materials Science — Materials Chemistry

Interpretable discovery of semiconductors with machine learning Abstract Machine learning models of material properties accelerate materials discovery, reproducing density functional theory calculated results at a fraction of the cost 1–6 . To bridge the gap between theory and experiments, machine learning predictions need to be distilled in the form of interpretable chemical rules that can be used by experimentalists. Here we develop a framework to address this gap by combining evolutionary algorithm-powered search with machine-learning surrogate models. We then couple the search results with supervised learning and statistical testing. This strategy enables the efficient search of a materials space while providing interpretable design rules. We demonstrate its effectiveness by developing rules for the design of direct bandgap materials, stable UV emitters, and IR perovskite emitters. Finally, we conclusively show how DARWIN-generated rules are statistically more robust and applicable to a wide range of applications including the design of UV halide perovskites. Machine learning ; Computer science ; Artificial intelligence ; Bridge (graph theory) ; Range (aeronautics) ; Chemical space ; Space (punctuation) ; Materials science ; Drug discovery ; Chemistry Machine Learning in Materials Science ; Advanced Photocatalysis Techniques ; Electrocatalysts for Energy Conversion
Authors: Aakash Bhardwaj | Akshdeep Singh Ahluwalia | Kamal Kishore Pant | Sreedevi Upadhyayula
Source: Separation and Purification Technology (article, 2023-07-17)
Field: Materials Science — Materials Chemistry

A principal component analysis assisted machine learning modeling and validation of methanol formation over Cu-based catalysts in direct CO2 hydrogenation Principal component analysis ; Artificial neural network ; Principal component regression ; Ternary operation ; Support vector machine ; Machine learning ; Cross-validation ; Computer science ; Methanol ; Artificial intelligence ; Chemistry Machine Learning in Materials Science ; Catalysts for Methane Reforming ; CO2 Reduction Techniques and Catalysts
Authors: Payden Brown | Houlong Zhuang
Source: Materials Today (article, 2023-03-01)
Field: Materials Science — Materials Chemistry

Quantum machine-learning phase prediction of high-entropy alloys Quantum computer ; Computer science ; Quantum machine learning ; Quantum ; Superposition principle ; Artificial neural network ; Quantum algorithm ; Artificial intelligence ; Machine learning ; Algorithm ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Advanced Materials Characterization Techniques
Authors: M. Ghorbani | Mario Boley | Philip N. H. Nakashima | N. Birbilis
Source: Journal of Magnesium and Alloys (article, 2023-10-27)
Field: Materials Science — Biomaterials

A machine learning approach for accelerated design of magnesium alloys. Part B: Regression and property prediction Machine learning (ML) models provide great opportunities to accelerate novel material development, offering a virtual alternative to laborious and resource-intensive empirical methods. In this work, the second of a two-part study, an ML approach is presented that offers accelerated digital design of Mg alloys. A systematic evaluation of four ML regression algorithms was explored to rationalise the complex relationships in Mg-alloy data and to capture the composition-processing-property patterns. Cross-validation and hold-out set validation techniques were utilised for unbiased estimation of model performance. Using atomic and thermodynamic properties of the alloys, feature augmentation was examined to define the most descriptive representation spaces for the alloy data. Additionally, a graphical user interface (GUI) webtool was developed to facilitate the use of the proposed models in predicting the mechanical properties of new Mg alloys. The results demonstrate that random forest regression model and neural network are robust models for predicting the ultimate tensile strength and ductility of Mg alloys, with accuracies of ∼80% and 70% respectively. The developed models in this work are a step towards high-throughput screening of novel candidates for target mechanical properties and provide ML-guided alloy design. Artificial neural network ; Ultimate tensile strength ; Computer science ; Property (philosophy) ; Random forest ; Materials science ; Regression ; Machine learning ; Ductility (Earth science) ; Representation (politics) ; Alloy ; Throughput ; Regression analysis ; Artificial intelligence ; Metallurgy ; Mathematics ; Statistics Magnesium Alloys: Properties and Applications ; Aluminum Alloy Microstructure Properties ; Titanium Alloys Microstructure and Properties
Authors: Joung Sik Suh | Young Min Kim | Chang Dong Yim | Byeong‐Chan Suh | Jun Ho Bae | Ho Won Lee
Source: Journal of Alloys and Compounds (article, 2023-09-03)
Field: Materials Science — Biomaterials

Interpretable machine learning-based analysis of mechanical properties of extruded Mg-Al-Zn-Mn-Ca-Y alloys Microstructure ; Ultimate tensile strength ; Texture (cosmology) ; Materials science ; Extrusion ; Elongation ; Artificial neural network ; Standard deviation ; Artificial intelligence ; Machine learning ; Computer science ; Metallurgy ; Mathematics ; Statistics ; Image (mathematics) Magnesium Alloys: Properties and Applications ; Metal and Thin Film Mechanics ; Aluminum Alloys Composites Properties