jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: G. Gowrisankar | R. Mariappan | E. Palani | E. Krishnamoorthy
Source: Research Square (preprint, 2024-03-26)
Field: Materials Science — Polymers and Plastics

Ultrasound - assisted synthesized multi-phase copper vanadate (Cu 3 V 2 O 8 /Cu 2 V 2 O 7 /Cu 0.4 V 2 O 5 ) shape-controlled nanoparticles as electrode material for energy storage applications Abstract In this novel study, complex phases of copper vanadium oxides, including Cu 3 V 2 O 8 , Cu 2 V 2 O 7 , and Cu 0.4 V 2 O 5 , were synthesized using an ultrasound-assisted co-precipitation technique and evaluated as suitable electrodes for energy storage devices that exhibit pseudo-capacitive behavior.The structural properties investigation of CuV, CuV + 0.3%PVP and CuV + 3%PVP nanoparticles at 400 o C. The XRD patterns confirm monoclinic crystal system with multi-phase nature of copper vanadate’s further morphology have been optimized with stabilizing and shape-directing agent Polyvinylpyrrolidone (CuV + 0.3%PVP and CuV + 3%PVP) only at elevated temperature 400℃. The electrochemical behavior of CuV + 0.3%PVP and CuV + 3%PVP were observed under cyclic voltammetry (CV),Galvanostatic charge and discharge (GCD) and Electronic impedance spectroscopy (EIS). Most significantly, the shape-controlled copper vanadate nanoparticles (CuV + 3%PVP) exhibited exceptional electrochemical performance, demonstrating a notable specific capacitance increased significantly23.2% improvement. The specific capacitances of CuV and CuV + 3%PVP nanoparticles are 211.09 F/g 260.4 F/g respectively, an impressive energy density of 107.25 Wh/kg and power density of 297.9W/kg at 0.5 A/g. Thus, copper-vanadate materials may be observed as capable short time e high voltage electrode for pseudo capacitor applications. Vanadate ; Copper ; Nanoparticle ; Materials science ; Electrode ; Phase (matter) ; Nuclear chemistry ; Ultrasound ; Chemistry ; Analytical Chemistry (journal) ; Metallurgy ; Nanotechnology ; Physical chemistry ; Chromatography ; Physics ; Organic chemistry Transition Metal Oxide Nanomaterials ; Copper-based nanomaterials and applications ; Advanced battery technologies research
Authors: Qistina, Omar | Salmiaton, Ali | Choong, Thomas S.Y. | Izhar, Shamsul | Yap, Yun Hin Taufiq
Source: Universiti Putra Malaysia Institutional Repository (Universiti Putra Malaysia) (article, 2024-03-08)
Field: Materials Science — Materials Chemistry

Characterization of carbon nanotubes coated monolith synthesized via chemical vapor deposition Carbon nanotubes (CNTs) have been used as catalyst support in various catalytic activity. The existing CNTs in powder form can create high back pressure and inconvenient operational. Therefore, CNTs coated onto monolith structure provides a promising support for catalyst. In this study, the CNT monolith was synthesized using a chemical vapor deposition (CVD) method with deposition catalyst techniques determined by immersion and impregnation method. The synthesized CNTs monolith were characterized for surface morphology analysis, atomic composition, thermal stability, textural properties, functional group determination and crystallinity. The findings show that the CNTs formed are considered mesoporous nanotubes that attained a diameter size distribution scattered between 30 nm and 35 nm. The carbon yield was successfully achieved at more than 95% by the double immersion in the preparation technique. The CNTs monolith showed a very weak peak due to poor infrared transmittance, while the surface analysis of the CNTs monolith exhibited the type IV isotherm with H3 hysteresis in the presents of mesoporous structures with a relative pressure range of P/Po >0.4. The peak at 2 = 26.46° of the XRD pattern demonstrated a decrease after the synthesizing of CNTs growth onto monolith structure due to the production of carbon. The thermal analysis of the CNTs monolith showed a weight loss of moisture and organic residue of 0.13% and 3%, respectively. The results displayed an optional synthesis method and characterization information of CNTs structured monolith as value added for future production and application. Monolith ; Carbon nanotube ; Chemical vapor deposition ; Catalysis ; Mesoporous material ; Specific surface area ; Nanoparticle Carbon Nanotubes in Composites ; Mesoporous Materials and Catalysis ; Nanopore and Nanochannel Transport Studies
Authors: Alexander B. Shearer | Yukio Cho | Miso Kim | Andreas Werbrouck | Tzu‐Ling Liu | Chris Takacs | Bonggeun Shong | Stacey F. Bent
Source: ChemRxiv (preprint, 2024-12-12)
Field: Materials Science — Polymers and Plastics

Crystalline 1D Coordination Polymer Inhibitor Layer Leads to Vertical Sidewalls inSelectively Deposited ZnO on Nanoscale Patterns Area-selective atomic layer deposition (AS-ALD) is a promising technique for the fabrication of next generation nanoelectronics. There are two main challenges in AS-ALD: (1) achieving high selectivity of deposition on the growth regions and (2) preventing mushrooming of the growth material onto the nongrowth regions and achieving well-defined interfaces. In this work, we use benzenethiol (BT) as an inhibitor in the selective deposition of ZnO on SiO2 in the presence of copper with and without a native oxide (Cu/CuOx). We observe that BT forms a monolayer on the Cu surface and a Cu-thiolate multilayer structure on CuOx. Using grazing incidence x-ray diffraction combined with simulations, we find that the multilayer structure is crystalline and composed of 1D coordination polymers of Cu-thiolate. Using ellipsometry and x-ray photoelectron spectroscopy, we show that the BT consumes the entirety of the CuOx present in forming the multilayer, and thus the thickness of the multilayer can be tuned by the thickness of the original oxide. Both the monolayer BT and the multilayer BT prove to be effective inhibitors of ZnO ALD, blocking nearly 500 ALD cycles, which is more than twice that achieved with other thiol inhibitors. Finally, we demonstrate that the multilayer structure can prevent mushrooming of the ALD material onto the nongrowth surface of nanoscale patterns, creating vertical sidewalls with welldefined material interfaces and providing excellent pattern transfer even for a relatively thick deposited film. As such, these results demonstrate not only that BT is an effective inhibitor, but also that its ability to form tunable multilayers lends itself towards highly precise nanopatterning applications. Nanoscopic scale ; Layer (electronics) ; Materials science ; Nanotechnology ; Polymer ; Coordination polymer ; Composite material Synthesis and properties of polymers ; Advanced ceramic materials synthesis ; Silicone and Siloxane Chemistry
Authors: Sakhinana Sagar Srinivas | Geethan Sannidhi | Venkataramana Runkana
Source: arXiv (Cornell University) (preprint, 2024-08-24)
Field: Materials Science — Materials Chemistry

Hierarchical Network Fusion for Multi-Modal Electron Micrograph Representation Learning with Foundational Large Language Models Characterizing materials with electron micrographs is a crucial task in fields such as semiconductors and quantum materials. The complex hierarchical structure of micrographs often poses challenges for traditional classification methods. In this study, we propose an innovative backbone architecture for analyzing electron micrographs. We create multi-modal representations of the micrographs by tokenizing them into patch sequences and, additionally, representing them as vision graphs, commonly referred to as patch attributed graphs. We introduce the Hierarchical Network Fusion (HNF), a multi-layered network structure architecture that facilitates information exchange between the multi-modal representations and knowledge integration across different patch resolutions. Furthermore, we leverage large language models (LLMs) to generate detailed technical descriptions of nanomaterials as auxiliary information to assist in the downstream task. We utilize a cross-modal attention mechanism for knowledge fusion across cross-domain representations(both image-based and linguistic insights) to predict the nanomaterial category. This multi-faceted approach promises a more comprehensive and accurate representation and classification of micrographs for nanomaterial identification. Our framework outperforms traditional methods, overcoming challenges posed by distributional shifts, and facilitating high-throughput screening. Representation (politics) ; Modal ; Computer science ; Artificial intelligence ; Natural language processing ; Fusion ; Linguistics ; Materials science ; Philosophy Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques
Authors: Md. Mahfuzur Rahman | Nazmul Hasan | Sumaiya Tabassum | Md. Harun-Or-Rashid | Md Harunur Rashid | Md Arifuzzaman
Source: arXiv (Cornell University) (preprint, 2024-08-21)
Field: Materials Science — Materials Chemistry

Bi3+ Doped Nanocrystalline Ni-Co-Zn Spinel Ferrites: Tuning of Physical, Electrical, Dielectric and Magnetic Properties for Advanced Spintronics Applications This study reports the synthesis and characterization of nanocrystalline Ni0.5Co0.2Zn0.3BixFe2-xO4 x varis by 0.0, 0.025, 0.050, 0.075, 0.100 ferrites synthesized via the sol-gel auto combustion method.The low coercivity values 23.68 to 87.71 Oe are observed,classifying the investigated materials as soft ferromagnetic.The increased magnetic anisotropy K through Bi3+ doping indicates tunable stability in magnetic orientations,making them suitable for multifunctional applications. Nanocrystalline material ; Spinel ; Spintronics ; Materials science ; Dielectric ; Doping ; Optoelectronics ; Nanotechnology ; Ferromagnetism ; Metallurgy ; Condensed matter physics Magnetic Properties and Synthesis of Ferrites ; Electromagnetic wave absorption materials
Authors: Rohit S. K. Gadde | Sreelaya Devaguptam | Fangning Ren | Rajat Mittal | Lechen Dong | Yao Wang | Fang Liu
Source: ChemRxiv (preprint, 2024-12-26)
Field: Materials Science — Materials Chemistry

Chatbot-Assisted Quantum Chemistry for Explicitly Solvated Molecules Advanced computational chemistry software packages have transformed chemical research by leveraging quantum chemistry and molecular simulations. Despite their capabilities, the complicated design and requirement for specialized computing hardware hinder their applications in the broad chemistry community. Here, we introduce AutoSolvateWeb, a chatbot-assisted computational platform that addresses both challenges simultaneously. This platform employs a user-friendly chatbot interface to guide non-experts through a multistep procedure involving various computational packages, enabling them to configure and execute complex quantum mechanical/molecular mechanical (QM/MM) simulations of explicitly solvated molecules. Moreover, this platform operates on cloud infrastructure, allowing researchers to run simulations without prerequisite computing hardware. As a proof of concept, AutoSolvateWeb demonstrates that combining virtual agents with cloud computing can democratize access to sophisticated computational research tools. Quantum chemistry ; Chemistry ; Molecule ; Quantum ; Chatbot ; Computer science ; Chemical physics ; Physics ; Quantum mechanics ; World Wide Web ; Organic chemistry Machine Learning in Materials Science
Authors: Kevin Tirta Wijaya | Minghao Guo | Michael Sun | Hans‐Peter Seidel | Wojciech Matusik | Vahid Babaei
Source: arXiv (Cornell University) (preprint, 2024-11-05)
Field: Materials Science — Biomaterials

Two-Stage Pretraining for Molecular Property Prediction in the Wild Molecular deep learning models have achieved remarkable success in property prediction, but they often require large amounts of labeled data. The challenge is that, in real-world applications, labels are extremely scarce, as obtaining them through laboratory experimentation is both expensive and time-consuming. In this work, we introduce MoleVers, a versatile pretrained molecular model designed for various types of molecular property prediction in the wild, i.e., where experimentally-validated labels are scarce. MoleVers employs a two-stage pretraining strategy. In the first stage, it learns molecular representations from unlabeled data through masked atom prediction and extreme denoising, a novel task enabled by our newly introduced branching encoder architecture and dynamic noise scale sampling. In the second stage, the model refines these representations through predictions of auxiliary properties derived from computational methods, such as the density functional theory or large language models. Evaluation on 22 small, experimentally-validated datasets demonstrates that MoleVers achieves state-of-the-art performance, highlighting the effectiveness of its two-stage framework in producing generalizable molecular representations for diverse downstream properties. Property (philosophy) ; Stage (stratigraphy) ; Computational biology ; Psychology ; Artificial intelligence ; Chemistry ; Biological system ; Computer science ; Biology ; Philosophy ; Epistemology ; Paleontology Diatoms and Algae Research
Authors: L. V. Petrov | B. L. Psikha | V.M. Soljanikov
Source:  (article, 2024-12-16)
Field: Chemistry — Organic Chemistry

MOLECULAR OXYGEN OXIDATION OF THE STYRENE EPOXIDE- COPPER(II) CHLORIDE-IONOL TERNARY SYSTEM IN METHANOL Epoxide ; Methanol ; Ternary operation ; Styrene ; Copper ; Chloride ; Oxygen ; Chemistry ; Polymer chemistry ; Nuclear chemistry ; Copolymer ; Organic chemistry ; Catalysis ; Polymer ; Computer science Inorganic and Organometallic Chemistry ; Oxidative Organic Chemistry Reactions ; Water Quality Monitoring and Analysis
Authors: O. R. Klyuchnikov
Source:  (article, 2024-12-16)
Field: Materials Science — Polymers and Plastics

DEVELOPMENT OF THE DIRECTION OF VULCANIZATION OF ELASTOMERS WITH QUINOL ETHERS The relevance of the article is due to serious changes in the world's largest economy related to the special status of Hong Kong, which is rapidly losing its autonomy and privileges associated with it.The purpose of the article is to substantiate the possible prospects and limitations of the economic and political future of Hong Kong, taking into account the significance of this problem for the entire world economy.The subject of the research is the features, trends and patterns of development of Hong Kong in the economic and political spheres in the context of economic integration with mainland China.Conclusions.It is established that there can be multiple scenarios: while a full abolition of the special status of Hong Kong seems less realistic because it eliminates possible attempts to build relations on the principle of "one country, two systems". Vulcanization ; Elastomer ; Materials science ; Polymer science ; Computer science ; Composite material ; Natural rubber Synthesis and properties of polymers
Authors: Srishti Gupta | Ajin Rajan | Edvin Fako | Tiago J. Goncalves | Imke Mueller | Jithin John Varghese | Ansgar Schaefer | Sandip De
Source: ChemRxiv (preprint, 2024-12-17)
Field: Materials Science — Materials Chemistry

Exploring the Intricacies of Glycerol Hydrodeoxygenation to Propanediol on Cu surface: A Comprehensive Investigation with the Aid of Machine Learning Forcefield The utilization of biomass to feedstock chemicals often relies on transforming hydroxyl-containing molecules. One such example is glycerol which can undergo a selective hydrodeoxygenation reaction to produce propanediol, a valuable chemical precursor. Hence, glycerol’s hydrodeoxygenation reaction combines immediate industrial application with foundation of fundamental research into the reaction class relevant for sustainable feedstock. Given the complex nature of large organic molecules, most modelling work in heterogeneous catalysis focusses on the reactivity of small (C1-2) organics exclusively. Glycerol, characterized by its C3-backbone, exhibits 75 distinct gas-phase conformers.[1] When considering its 11 reactive bonds (C-O, C-H and O-H), the modeling of glycerol's reactivity spans an extensive conformational and reactive space. High computational costs of Density Functional Theory simulations restrict exhaustive exploration of the factorial reaction space, leading to limited insights of the hydrodeoxygenation (HDO) mechanism and hindering rational catalyst design. Therefore, to date, there is no systematic study focusing on comprehensively sampling the energetics of surface conformers of glycerol and their reactivity. In this study, we employ a message-passing graph neural network architecture (MACE) to develop a machine-learned force-field (MLFF) potential, utilizing active learning to investigate the impact of conformational complexity on the reaction network of glycerol HDO on a Cu(111) surface. Following five iterations, our trained MLFF model accurately predicts surface bound structures with a root-mean-square accuracy of 0.04 eV (< 0.6 meV/atom total energy), essential to accurately determine conformational minima of 24 meta-stable and 26 intermediate states along seven competitive pathways. Conformational sampling uncovers the intricate nature of the complex energy landscape, where conformers with multiple shallow minima lead to non-trivial trends in the transition state energies connecting them. Notably, the investigations predict lower activation barriers for O-H bond scissions of glycerol structures with α- and γ-backbone as compared to β-backbone. This is significant in case of scission of secondary O-H glycerol bonds where the activation barrier varies up to 0.44 eV depending upon the initial glycerol structure motif. Altogether, we identify dehydrogenation-dehydration-hydrogenation as dominant pathway resulting in PDO formation on the Cu(111) surface. The selectivity of glyceraldehyde towards C-H bond scission over C-OH bond scission explains higher selectivity of 1,2-PDO over 1,3-PDO. Hydrodeoxygenation ; Glycerol ; Chemistry ; Materials science ; Catalysis ; Organic chemistry Machine Learning in Materials Science
Authors: Yuran Xiang | Haiteng Zhao | Chang Ma | Zhi‐Hong Deng
Source: arXiv (Cornell University) (preprint, 2024-08-19)
Field: Chemistry — Physical and Theoretical Chemistry

Instruction-Based Molecular Graph Generation with Unified Text-Graph Diffusion Model Recent advancements in computational chemistry have increasingly focused on synthesizing molecules based on textual instructions. Integrating graph generation with these instructions is complex, leading most current methods to use molecular sequences with pre-trained large language models. In response to this challenge, we propose a novel framework, named $\textbf{UTGDiff (Unified Text-Graph Diffusion Model)}$, which utilizes language models for discrete graph diffusion to generate molecular graphs from instructions. UTGDiff features a unified text-graph transformer as the denoising network, derived from pre-trained language models and minimally modified to process graph data through attention bias. Our experimental results demonstrate that UTGDiff consistently outperforms sequence-based baselines in tasks involving instruction-based molecule generation and editing, achieving superior performance with fewer parameters given an equivalent level of pretraining corpus. Our code is availble at https://github.com/ran1812/UTGDiff. Computer science ; Graph ; Theoretical computer science Various Chemistry Research Topics ; Genetics, Bioinformatics, and Biomedical Research
Authors: Yifeng Liu | Hanwen Xu | Tangqi Fang | Haocheng Xi | Zixuan Liu | Sheng Zhang | Hoifung Poon | Sheng Wang
Source: arXiv (Cornell University) (preprint, 2024-01-26)
Field: Materials Science — Materials Chemistry

T-Rex: Text-assisted Retrosynthesis Prediction As a fundamental task in computational chemistry, retrosynthesis prediction aims to identify a set of reactants to synthesize a target molecule. Existing template-free approaches only consider the graph structures of the target molecule, which often cannot generalize well to rare reaction types and large molecules. Here, we propose T-Rex, a text-assisted retrosynthesis prediction approach that exploits pre-trained text language models, such as ChatGPT, to assist the generation of reactants. T-Rex first exploits ChatGPT to generate a description for the target molecule and rank candidate reaction centers based both the description and the molecular graph. It then re-ranks these candidates by querying the descriptions for each reactants and examines which group of reactants can best synthesize the target molecule. We observed that T-Rex substantially outperformed graph-based state-of-the-art approaches on two datasets, indicating the effectiveness of considering text information. We further found that T-Rex outperformed the variant that only use ChatGPT-based description without the re-ranking step, demonstrate how our framework outperformed a straightforward integration of ChatGPT and graph information. Collectively, we show that text generated by pre-trained language models can substantially improve retrosynthesis prediction, opening up new avenues for exploiting ChatGPT to advance computational chemistry. And the codes can be found at https://github.com/lauyikfung/T-Rex. Retrosynthetic analysis ; Natural language processing ; Computer science ; Artificial intelligence ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; CO2 Reduction Techniques and Catalysts
Authors: Rasha Hosam Saleh
Source: مجلة العلوم الطبيعية و الحياتية والتطبيقية (article, 2024-06-30)
Field: Chemistry — Organic Chemistry

Thin Film Biosynthesis of Zinc Oxide Nanoparticles Using Thymus Serpyllum Plant Extract Thin films of zinc oxide nanoparticles were made in this work with a Thymus serpyllum plant ex-tract. After the plant was gathered and thoroughly cleaned, a set of procedures was followed to make the plant extract. Next, one of the zinc compounds and the plant extract were used in a pro-cess that was shown to produce zinc oxide nanoparticles. Using infrared radiation, scanning elec-tron microscopy, and X-ray diffraction, the resultant nanocomposite was discovered and de-scribed. The findings demonstrated that the nanoparticles were layered and ranged in size from 20.29 to 65.30 nm. Zinc ; Nanoparticle ; Nanocomposite ; Materials science ; Nuclear chemistry ; Nanotechnology ; Chemistry ; Chemical engineering ; Metallurgy ; Engineering Polydiacetylene-based materials and applications ; Liquid Crystal Research Advancements ; Characterization and Applications of Magnetic Nanoparticles