jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Valentina Salaris | Adrián Leonés | Daniel López | J. M. Kenny | Laura Peponi
Source: Polymers (review, 2022-02-28)
Field: Materials Science — Biomaterials

Shape-Memory Materials via Electrospinning: A Review This review aims to point out the importance of the synergic effects of two relevant and appealing polymeric issues: electrospun fibers and shape-memory properties. The attention is focused specifically on the design and processing of electrospun polymeric fibers with shape-memory capabilities and their potential application fields. It is shown that this field needs to be explored more from both scientific and industrial points of view; however, very promising results have been obtained up to now in the biomedical field and also as sensors and actuators and in electronics. Electrospinning ; Materials science ; Nanotechnology ; Shape-memory alloy ; Electronics ; Actuator ; Field (mathematics) ; Point (geometry) ; Computer science ; Mechanical engineering ; Composite material ; Polymer ; Electrical engineering ; Engineering ; Artificial intelligence Electrospun Nanofibers in Biomedical Applications ; Polymer composites and self-healing ; Advanced Sensor and Energy Harvesting Materials
Authors: Syed Farooq Adil | Muhammad Ashraf | Mujeeb Khan | Mohamed E. Assal | Mohammed Rafi Shaik | Mufsir Kuniyil | Abdulrahman Al‐Warthan | Mohammed Rafiq H. Siddiqui | Wolfgang Tremel | Muhammad Nawaz Tahir
Source: The Chemical Record (review, 2022-02-01)
Field: Materials Science — Materials Chemistry

Advances in Graphene/Inorganic Nanoparticle Composites for Catalytic Applications Graphene-based nanocomposites with inorganic (metal and metal oxide) nanoparticles leads to materials with high catalytic activity for a variety of chemical transformations. Graphene and its derivatives such as graphene oxide, highly reduced graphene oxide, or nitrogen-doped graphene are excellent support materials due to their high surface area, their extended π-system, and variable functionalities for effective chemical interactions to fabricate nanocomposites. The ability to fine-tune the surface composition for desired functionalities enhances the versatility of graphene-based nanocomposites in catalysis. This review summarizes the preparation of graphene/inorganic NPs based nanocomposites and their use in catalytic applications. We discuss the large-scale synthesis of graphene-based nanomaterials. We have also highlighted the interfacial electronic communication between graphene/inorganic nanoparticles and other factors resulting in increased catalytic efficiencies. Graphene ; Materials science ; Oxide ; Nanocomposite ; Catalysis ; Nanomaterials ; Nanotechnology ; Nanoparticle ; Graphene oxide paper ; Chemistry ; Organic chemistry Graphene research and applications ; Nanomaterials for catalytic reactions ; Advancements in Battery Materials
Authors: Jesús Gándara-Loe | Qi Zhang | Juan J. Villora‐Picó | Antonio Sepúlveda‐Escribano | Laura Pastor‐Pérez | Tomás Ramı́rez Reina
Source: Energy & Fuels (article, 2022-05-25)
Field: Materials Science — Materials Chemistry

Design of Full-Temperature-Range RWGS Catalysts: Impact of Alkali Promoters on Ni/CeO<sub>2</sub> Reverse water gas shift (RWGS) competes with methanation as a direct pathway in the CO<sub>2</sub> recycling route, with methanation being a dominant process in the low-temperature window and RWGS at higher temperatures. This work showcases the design of multi-component catalysts for a full-temperature-range RWGS behavior by suppressing the methanation reaction at low temperatures. The addition of alkali promoters (Na, K, and Cs) to the reference Ni/CeO<sub>2</sub> catalyst allows identifying a clear trend in RWGS activation promotion in both low- and high-temperature ranges. Our characterization data evidence changes in the electronic, structural, and textural properties of the reference catalyst when promoted with selected dopants. Such modifications are crucial to displaying an advanced RWGS performance. Among the studied promoters, Cs leads to a more substantial impact on the catalytic activity. Beyond the improved CO selectivity, our best performing catalyst maintains high conversion levels for long-term runs in cyclable temperature ranges, showcasing the versatility of this catalyst for different operating conditions. All in all, this work provides an illustrative example of the impact of promoters on fine-tuning the selectivity of a CO<sub>2</sub> conversion process, opening new opportunities for CO<sub>2</sub> utilization strategies enabled by multi-component catalysts. Methanation ; Catalysis ; Materials science ; Dopant ; Chemical engineering ; Selectivity ; Atmospheric temperature range ; Alkali metal ; Water-gas shift reaction ; Nanotechnology ; Chemistry ; Doping ; Optoelectronics ; Organic chemistry ; Thermodynamics Catalytic Processes in Materials Science ; CO2 Reduction Techniques and Catalysts ; Catalysts for Methane Reforming
Authors: I.S. Elashmawi | A. M. Ismail | A. M. Abdelghany
Source: Polymer Bulletin (article, 2022-12-01)
Field: Materials Science — Polymers and Plastics

The incorporation of polypyrrole (PPy) in CS/PVA composite films to enhance the structural, optical, and the electrical conductivity Abstract Using the solution casting method, a blend of polyvinyl alcohol/chitosan (PVA/Cs) blend was prepared with different amounts of polypyrrole (PPy) to enhance the structural and electrical conductivity of the films. The miscibility and interaction between PVA/Cs were confirmed by FT-IR measurements by shifting and disappearing some bands suggesting that the PVA functional group interacted with every other functional group on the Cs side chain. For PVA/Cs/PPy, some band intensities varied, indicating the interaction between PPy and PVA/Cs. XRD showed that the addition of PPy to the blend leads to a noticeable decrease in the intensity of the diffraction peak at 2 θ = 20.0° confirming the interactions have occurred between the PVA/Cs mixture and PPy. The UV–visible spectra indicate that increasing the amounts of PPy leads to a dramatic decrease in the energy band gap and an increase in the Urbach energy due to the creation of new energy levels that emerged between conduction and valance bands. After PPy was introduced, the polaron and bipolaron transition peaks at 416, 465, and 560 nm become apparent in the PL spectrum. The dielectric and the electrical properties were reported. The values of ε ′ and ε ′′ were stronger at low frequencies confirming the possibilities of interface polarization processes. The tan δ behavior diagrams have exhibited one peak trend in all samples shift toward higher frequencies as the temperature and PPy rise. The presence of the peaks was explained based on the electrical conductivity mechanism and dielectric behavior. The Cole–Cole plot displays a half one semicircle shape that explains the absence of contact effects. This semicircle expands as both temperature and PPy increase. Polypyrrole ; Materials science ; Bipolaron ; Miscibility ; Conductivity ; Dielectric ; Composite number ; Band gap ; Electrical resistivity and conductivity ; Composite material ; Polaron ; Analytical Chemistry (journal) ; Polymer chemistry ; Polymer ; Physical chemistry ; Chemistry ; Polymerization ; Organic chemistry Conducting polymers and applications ; Polymer Nanocomposite Synthesis and Irradiation ; Advanced Sensor and Energy Harvesting Materials
Authors: Jiaqi Wang | Lin Hu | Wenhao Li | Yuge Ouyang | Liuyang Bai
Source: Nanomaterials (review, 2022-10-12)
Field: Materials Science — Materials Chemistry

Development and Perspectives of Thermal Conductive Polymer Composites With the development of electronic appliances and electronic equipment towards miniaturization, lightweight and high-power density, the heat generated and accumulated by devices during high-speed operation seriously reduces the working efficiency and service life of the equipment. The key to solving this problem is to develop high-performance thermal management materials and improve the heat dissipation efficiency of the equipment. This paper mainly summarizes the research progress of polymer composites with high thermal conductivity and electrical insulation, including the thermal conductivity mechanism of composites, the factors affecting the thermal conductivity of composites, and the research status of thermally conductive and electrical insulation polymer composites in recent years. Finally, we look forward to the research focus and urgent problems that should be addressed of high-performance thermal conductive composites, which will provide strategies for further development and application of advanced thermal and electrical insulation composites. Materials science ; Electrical conductor ; Composite material ; Thermal conductivity ; Thermal management of electronic devices and systems ; Miniaturization ; Thermal insulation ; Electronics ; Thermal ; Thermal bridge ; Filler (materials) ; Engineering physics ; Mechanical engineering ; Electrical engineering ; Nanotechnology ; Engineering Thermal properties of materials ; Dielectric materials and actuators ; Thermal Radiation and Cooling Technologies
Authors: Ahmed A. El-Shanshory | Mona M. Agwa | Ahmed I. Abd‐Elhamid | Hesham M. A. Soliman | Xiumei Mo | El‐Refaie Kenawy
Source: Polymers (article, 2022-01-23)
Field: Materials Science — Biomaterials

Metronidazole Topically Immobilized Electrospun Nanofibrous Scaffold: Novel Secondary Intention Wound Healing Accelerator The process of secondary intention wound healing includes long repair and healing time. Electrospun nanofibrous scaffolds have shown potential for wound dressing. Biopolymers have gained much attention due to their remarkable characteristics such as biodegradability, biocompatibility, non-immunogenicity and nontoxicity. This study anticipated to develop a new composite metronidazole (MTZ) immobilized nanofibrous scaffold based on poly (3-hydroxy butyrate) (PHB) and Gelatin (Gel) to be utilized as a novel secondary intention wound healing accelerator. Herein, PHB and Gel were mixed together at different weight ratios to prepare polymer solutions with final concentration of (7%), loaded with two different concentrations 5% (Z<sub>1</sub>) and 10% (Z<sub>2</sub>) of MTZ. Nanofibrous scaffolds were obtained by manipulating electrospinning technique. The properties of MTZ immobilized PHB/Gel nanofibrous scaffold were evaluated (SEM, FTIR, TGA, water uptake, contact angle, porosity, mechanical properties and antibacterial activity). Additionally, in vitro cytocompatibility of the obtained nanofibrous scaffolds were assessed by using the cell counting kit-8 (CCK-8 assay). Moreover, in vivo wound healing experiments revealed that the prepared nanofibrous scaffold highly augmented the transforming growth factor (TGF-β) signaling pathway, moderately suppressed the pro-inflammatory cytokine (IL-6). These results indicate that MTZ immobilized PHB/Gel nanofibrous scaffold significantly boost accelerating secondary intention wound healing. Electrospinning ; Scaffold ; Wound healing ; Gelatin ; Biocompatibility ; Materials science ; Nanofiber ; Biomedical engineering ; Chemistry ; Chemical engineering ; Polymer ; Nanotechnology ; Composite material ; Surgery ; Organic chemistry Electrospun Nanofibers in Biomedical Applications ; Wound Healing and Treatments ; Silk-based biomaterials and applications
Authors: Jie Yin | Qian Lei | Xiang Li | Xiaoyan Zhang | Xiangpeng Meng | Yanbin Jiang | Liang Tian | Shuang Zhou | Zhou Li
Source: Acta Materialia (article, 2022-10-13)
Field: Materials Science — Materials Chemistry

A novel neural network-based alloy design strategy: Gated recurrent unit machine learning modeling integrated with orthogonal experiment design and data augmentation Artificial neural network ; Materials science ; Curse of dimensionality ; Hyperparameter ; Artificial intelligence ; Deep learning ; Machine learning ; Deformation (meteorology) ; Experimental data ; Design of experiments ; Dimensionality reduction ; Computer science ; Mathematics ; Composite material ; Statistics Machine Learning in Materials Science ; Hydrogen embrittlement and corrosion behaviors in metals ; Corrosion Behavior and Inhibition
Authors: Mingjian Wen | Samuel M. Blau | Xiaowei Xie | Shyam Dwaraknath | Kristin A. Persson
Source: Chemical Science (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Improving machine learning performance on small chemical reaction data with unsupervised contrastive pretraining Machine learning (ML) methods have great potential to transform chemical discovery by accelerating the exploration of chemical space and drawing scientific insights from data. However, modern chemical reaction ML models, such as those based on graph neural networks (GNNs), must be trained on a large amount of labelled data in order to avoid overfitting the data and thus possessing low accuracy and transferability. In this work, we propose a strategy to leverage unlabelled data to learn accurate ML models for small labelled chemical reaction data. We focus on an old and prominent problem-classifying reactions into distinct families-and build a GNN model for this task. We first pretrain the model on unlabelled reaction data using unsupervised contrastive learning and then fine-tune it on a small number of labelled reactions. The contrastive pretraining learns by making the representations of two augmented versions of a reaction similar to each other but distinct from other reactions. We propose chemically consistent reaction augmentation methods that protect the reaction center and find they are the key for the model to extract relevant information from unlabelled data to aid the reaction classification task. The transfer learned model outperforms a supervised model trained from scratch by a large margin. Further, it consistently performs better than models based on traditional rule-driven reaction fingerprints, which have long been the default choice for small datasets, as well as those based on reaction fingerprints derived from masked language modelling. In addition to reaction classification, the effectiveness of the strategy is tested on regression datasets; the learned GNN-based reaction fingerprints can also be used to navigate the chemical reaction space, which we demonstrate by querying for similar reactions. The strategy can be readily applied to other predictive reaction problems to uncover the power of unlabelled data for learning better models with a limited supply of labels. Computer science ; Artificial intelligence ; Unsupervised learning ; Machine learning ; Natural language processing Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Yang Liu | Jun Li
Source: The Journal of Physical Chemistry Letters (article, 2022-05-24)
Field: Materials Science — Materials Chemistry

Permutation-Invariant-Polynomial Neural-Network-Based Δ-Machine Learning Approach: A Case for the HO <sub>2</sub> Self-Reaction and Its Dynamics Study Δ-machine learning, or the hierarchical construction scheme, is a highly cost-effective method, as only a small number of high-level <i>ab initio</i> energies are required to improve a potential energy surface (PES) fit to a large number of low-level points. However, there is no efficient and systematic way to select as few points as possible from the low-level data set. We here propose a permutation-invariant-polynomial neural-network (PIP-NN)-based Δ-machine learning approach to construct full-dimensional accurate PESs of complicated reactions efficiently. Particularly, the high flexibility of the NN is exploited to efficiently sample points from the low-level data set. This approach is applied to the challenging case of a HO<sub>2</sub> self-reaction with a large configuration space. Only 14% of the DFT data set is used to successfully bring a newly fitted DFT PES to the UCCSD(T)-F12a/AVTZ quality. Then, the quasiclassical trajectory (QCT) calculations are performed to study its dynamics, particularly the mode specificity. Invariant (physics) ; Artificial neural network ; Computer science ; Potential energy surface ; Permutation (music) ; Polynomial ; Flexibility (engineering) ; Ab initio ; Algorithm ; Artificial intelligence ; Mathematics ; Quantum mechanics ; Physics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Spectroscopy and Quantum Chemical Studies
Authors: Ramil Nugmanov | Natalia Dyubankova | Andrey Gedich | Jörg K. Wegner
Source: Journal of Chemical Information and Modeling (review, 2022-07-06)
Field: Materials Science — Materials Chemistry

Bidirectional Graphormer for Reactivity Understanding: Neural Network Trained to Reaction Atom-to-Atom Mapping Task This work introduces <i>GraphormerMapper</i>, a new algorithm for reaction atom-to-atom mapping (AAM) based on a transformer neural network adopted for the direct processing of molecular graphs as sets of atoms and bonds, as opposed to SMILES/SELFIES sequence-based approaches, in combination with the Bidirectional Encoder Representations from Transformers (BERT) network. The graph transformer serves to extract molecular features that are tied to atoms and bonds. The BERT network is used for chemical transformation learning. In a benchmarking study with IBM RxnMapper, which is the best AAM algorithm according to our previous study, we demonstrate that our AAM algorithm is superior to it on our "Golden" benchmarking data set. Benchmarking ; Transformer ; Computer science ; Artificial neural network ; Encoder ; Atom (system on chip) ; Artificial intelligence ; Algorithm ; Pattern recognition (psychology) ; Parallel computing ; Engineering ; Voltage ; Electrical engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Somesh Mohapatra | Joyce An | Rafael Gómez‐Bombarelli
Source: Machine Learning Science and Technology (article, 2022-02-11)
Field: Materials Science — Materials Chemistry

Chemistry-informed macromolecule graph representation for similarity computation, unsupervised and supervised learning Abstract The near-infinite chemical diversity of natural and artificial macromolecules arises from the vast range of possible component monomers, linkages, and polymers topologies. This enormous variety contributes to the ubiquity and indispensability of macromolecules but hinders the development of general machine learning methods with macromolecules as input. To address this, we developed a chemistry-informed graph representation of macromolecules that enables quantifying structural similarity, and interpretable supervised learning for macromolecules. Our work enables quantitative chemistry-informed decision-making and iterative design in the macromolecular chemical space. Macromolecule ; Representation (politics) ; Similarity (geometry) ; Computer science ; Graph ; Chemistry ; Artificial intelligence ; Machine learning ; Theoretical computer science ; Biochemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Polymer Synthesis and Characterization
Authors: Lei Tao | John Byrnes | Vikas Varshney | Ying Li
Source: iScience (article, 2022-06-10)
Field: Materials Science — Materials Chemistry

Machine learning strategies for the structure-property relationship of copolymers Establishing the structure-property relationship is extremely valuable for the molecular design of copolymers. However, machine learning (ML) models can incorporate both chemical composition and sequence distribution of monomers, and have the generalization ability to process various copolymer types (e.g., alternating, random, block, and gradient copolymers) with a unified approach are missing. To address this challenge, we formulate four different ML models for investigation, including a feedforward neural network (FFNN) model, a convolutional neural network (CNN) model, a recurrent neural network (RNN) model, and a combined FFNN/RNN (Fusion) model. We use various copolymer types to systematically validate the performance and generalizability of different models. We find that the RNN architecture that processes the monomer sequence information both forward and backward is a more suitable ML model for copolymers with better generalizability. As a supplement to polymer informatics, our proposed approach provides an efficient way for the evaluation of copolymers. Copolymer ; Recurrent neural network ; Generalizability theory ; Computer science ; Artificial intelligence ; Artificial neural network ; Sequence (biology) ; Feedforward neural network ; Machine learning ; Algorithm ; Polymer ; Chemistry ; Mathematics ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Polymer Synthesis and Characterization
Authors: Elif Esra Altuner | Rima Nour Elhouda Tiri | Ayşenur Aygün | Fulya Gülbağça | Fatih Şen | Alireza Iranbakhsh | Fatemeh Karimi | Yasser Vasseghian | Elena-Niculina Drăgoi
Source: Process Safety and Environmental Protection (article, 2022-05-23)
Field: Materials Science — Materials Chemistry

Hydrogen production and photocatalytic activities from NaBH4 using trimetallic biogenic PdPtCo nanoparticles: Development of machine learning model Photodegradation ; Catalysis ; Nanomaterial-based catalyst ; Photocatalysis ; Hydrogen production ; Fourier transform infrared spectroscopy ; Nanoparticle ; Chemistry ; Nuclear chemistry ; Materials science ; Chemical engineering ; Nanotechnology ; Organic chemistry Hydrogen Storage and Materials ; Hydrogen's biological and therapeutic effects ; Advanced Photocatalysis Techniques
Authors: Bin Cao | Shuang Yang | Ankang Sun | Ziqiang Dong | Tong‐Yi Zhang
Source: Journal of Materials Informatics (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Domain knowledge-guided interpretive machine learning: formula discovery for the oxidation behavior of ferritic-martensitic steels in supercritical water A general formula with high generalization and accurate prediction power is highly desirable for science, technology and engineering. In addition to human beings, artificial intelligence algorithms show great promise for the discovery of formulas. In this study, we propose a domain knowledge-guided interpretive machine learning strategy and demonstrate it by studying the oxidation behavior of ferritic-martensitic steels in supercritical water. The oxidation Cr equivalent is, for the first time, proposed in the present work to represent all contributions of alloying elements to oxidation, derived by our domain knowledge and interpretive machine learning algorithms. An open-source tree classifier for linear regression algorithm is also, for the first time, developed to materialize the formula with collected data. This algorithm effectively captures the linear correlation between compositions, testing environments and oxidation behaviors from the data. The sure independence screening and sparsifying operator algorithm finally assembles the information derived from the tree classifier for linear regression algorithm, resulting in a general formula. The general formula with the determined parameters has the power to predict, quantitatively and accurately, the oxidation behavior of ferritic-martensitic steels with multiple alloying elements exposed to various supercritical water environments, thereby providing guidance for the design of anti-oxidation steels and hence promoting the development of power plants with improved safety. The present work demonstrates the power of domain knowledge-guided interpretive machine learning with respect to the data-driven discovery of physics-informed formulas and the acceleration of materials informatics development. Machine learning ; Classifier (UML) ; Artificial intelligence ; Computer science ; Domain (mathematical analysis) ; Knowledge extraction ; Algorithm ; Domain knowledge ; Support vector machine ; Learning classifier system ; Mathematics ; Artificial neural network Machine Learning in Materials Science ; Hydrogen embrittlement and corrosion behaviors in metals ; Subcritical and Supercritical Water Processes