jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Xuan Wang | Akang Chen | Xiaopeng Wu | Jiatao Zhang | Jichen Dong | Leining Zhang
Source: Nano-Micro Letters (review, 2024-03-28)
Field: Materials Science — Materials Chemistry

Synthesis and Modulation of Low-Dimensional Transition Metal Chalcogenide Materials via Atomic Substitution In recent years, low-dimensional transition metal chalcogenide (TMC) materials have garnered growing research attention due to their superior electronic, optical, and catalytic properties compared to their bulk counterparts. The controllable synthesis and manipulation of these materials are crucial for tailoring their properties and unlocking their full potential in various applications. In this context, the atomic substitution method has emerged as a favorable approach. It involves the replacement of specific atoms within TMC structures with other elements and possesses the capability to regulate the compositions finely, crystal structures, and inherent properties of the resulting materials. In this review, we present a comprehensive overview on various strategies of atomic substitution employed in the synthesis of zero-dimensional, one-dimensional and two-dimensional TMC materials. The effects of substituting elements, substitution ratios, and substitution positions on the structures and morphologies of resulting material are discussed. The enhanced electrocatalytic performance and photovoltaic properties of the obtained materials are also provided, emphasizing the role of atomic substitution in achieving these advancements. Finally, challenges and future prospects in the field of atomic substitution for fabricating low-dimensional TMC materials are summarized. Chalcogenide ; Substitution (logic) ; Materials science ; Transition metal ; Nanotechnology ; Context (archaeology) ; Catalysis ; Optoelectronics ; Chemistry ; Computer science ; Organic chemistry 2D Materials and Applications ; Perovskite Materials and Applications ; Chalcogenide Semiconductor Thin Films
Authors: H. B. Duc Tran | Clara Vazquez‐Martel | Samantha O. Catt | Yixuan Jia | Manuel Tsotsalas | Christoph A. Spiegel | Eva Blasco
Source: Advanced Functional Materials (article, 2024-01-18)
Field: Chemistry — Organic Chemistry

4D Printing of Adaptable “Living” Materials Based on Alkoxyamine Chemistry Abstract 4D printing has emerged as a powerful strategy capable of revolutionizing additive manufacturing by enabling objects to dynamically transform overtime on demand. Despite significant progress, the full potential remains unrealized, particularly in the utilization of dynamic covalent chemistry. This study introduces a new approach using a multifunctional cross‐linker with alkoxyamine functionalities for 4D printing. Digital light processing (DLP) is employed for high‐resolution printing of complex objects. Leveraging alkoxyamine bonds' dynamic and living characteristics, the printed structures can be further modified through nitroxide‐mediated polymerization (NMP) using styrene and nitroxide exchange reactions (NER). The resulting “living” printed structures exhibit the unique ability to undergo both “growth” and “degrowth”, dynamically adapting their size as well as the reduced Young's Modulus across a wide range (770 kPa–1.2 GPa). The chain extension by NMP and softening by NER are carefully characterized by IR and EPR spectroscopy. The presented approach opens avenues for the development of 4D printed structures with complex adaptive systems, showcasing enormous potential in a wide range of fields. Nanotechnology ; Materials science ; 3D printing ; Dynamic covalent chemistry ; Covalent bond ; Click chemistry ; Polymer science ; Computer science ; Chemistry ; Polymer chemistry ; Organic chemistry ; Molecule ; Composite material Advanced Polymer Synthesis and Characterization ; Polymer Surface Interaction Studies ; Advanced Sensor and Energy Harvesting Materials
Authors: Mukhtar Alipuly | Dana Kanzhigitova | Aizada Bexeitova | Perizat Askar | Damira Kanayeva | Salimgerey Adilov | Nurxat Nuraje
Source: Advanced Composites and Hybrid Materials (article, 2024-12-18)
Field: Materials Science — Polymers and Plastics

Stable conductive PANI-based hydrogels with antibacterial activity Hydrogels have been utilized in various medical applications, including drug delivery, tissue repair, biosensors, wound dressing, and antimicrobial activity. Electrically conducting hydrogels are particularly promising due to their unique features, such as high-water content, biocompatibility, and adjustable mechanical and electrical properties. In this study, we developed novel conductive polyaniline-based hydrogel systems with enhanced antibacterial and mechanical properties. We specifically investigated the contributions of polyacrylamide, chitosan, phytic acid, and polyaniline to the hydrogel’s electrical sensitivity and stability under strain. Phytic acid and polyaniline were found to significantly improve the hydrogel’s electrical sensitivity and mechanical stability. Phytic acid, in the presence of calcium ions, further enhanced the mechanical properties, while polyaniline increased the electrical conductivity of the hydrogel by approximately sevenfold and also improved its mechanical properties. The newly developed conductive hydrogel system shows great potential for biomedical applications, including wearable sensors. Self-healing hydrogels ; Antibacterial activity ; Electrical conductor ; Materials science ; Polymer chemistry ; Composite material ; Bacteria ; Biology Conducting polymers and applications ; Hydrogels: synthesis, properties, applications ; Advanced Sensor and Energy Harvesting Materials
Authors: Yi Zhang | Fred Tutt | Guy N. Evans | Prachi Sharma | Greg Haugstad | B. Kaiser | Justin Ramberger | Samuel Bayliff | Yu Tao | Michael Manno | Javier García‐Barriocanal | Vipul Chaturvedi | Rafael M. Fernandes | Turan Birol | William E. Seyfried | Chris Leighton
Source: Nature Communications (article, 2024-02-15)
Field: Materials Science — Materials Chemistry

Crystal-chemical origins of the ultrahigh conductivity of metallic delafossites Despite their highly anisotropic complex-oxidic nature, certain delafossite compounds (e.g., PdCoO<sub>2</sub>, PtCoO<sub>2</sub>) are the most conductive oxides known, for reasons that remain poorly understood. Their room-temperature conductivity can exceed that of Au, while their low-temperature electronic mean-free-paths reach an astonishing 20 μm. It is widely accepted that these materials must be ultrapure to achieve this, although the methods for their growth (which produce only small crystals) are not typically capable of such. Here, we report a different approach to PdCoO<sub>2</sub> crystal growth, using chemical vapor transport methods to achieve order-of-magnitude gains in size, the highest structural qualities yet reported, and record residual resistivity ratios ( > 440). Nevertheless, detailed mass spectrometry measurements on these materials reveal that they are not ultrapure in a general sense, typically harboring 100s-of-parts-per-million impurity levels. Through quantitative crystal-chemical analyses, we resolve this apparent dichotomy, showing that the vast majority of impurities are forced to reside in the Co-O octahedral layers, leaving the conductive Pd sheets highly pure (∼1 ppm impurity concentrations). These purities are shown to be in quantitative agreement with measured residual resistivities. We thus conclude that a sublattice purification mechanism is essential to the ultrahigh low-temperature conductivity and mean-free-path of metallic delafossites. Delafossite ; Impurity ; Conductivity ; Electrical resistivity and conductivity ; Crystal (programming language) ; Materials science ; Anisotropy ; Metal ; Residual resistivity ; Electrical conductor ; Chemical physics ; Analytical Chemistry (journal) ; Chemistry ; Physical chemistry ; Metallurgy ; Physics ; Optics ; Oxide Copper-based nanomaterials and applications ; Electronic and Structural Properties of Oxides ; ZnO doping and properties
Authors: Kai‐Wen Ho | Yen-Ling Liu | Tzu‐Yi Liao | En‐Shuo Liu | Tian-Lu Cheng
Source: International Journal of Nanomedicine (review, 2024-10-01)
Field: Materials Science — Biomaterials

Strategies for Non-Covalent Attachment of Antibodies to PEGylated Nanoparticles for Targeted Drug Delivery Polyethylene glycol (PEG)-modified nanoparticles (NPs) often struggle with reduced effectiveness against metastasis and liquid tumors due to limited tumor cell uptake and therapeutic efficacy. To address this, actively targeted liposomes with enhanced tumor selectivity and internalization are being developed to improve uptake and treatment outcomes. Using bi-functional proteins to functionalize PEGylated NPs and enhance targeted drug delivery through non-covalent attachment methods has emerged as a promising approach. Among these, the one-step and two-step targeting strategies stand out for their simplicity, efficiency, and versatility. The one-step strategy integrates streptavidin-tagged antibodies or bispecific antibodies (bsAbs: PEG/DIG × marker) directly into PEGylated NPs. This method uses the natural interactions between antibodies and PEG for stable, specific binding, allowing the modification of biotin/Fc-binding molecules like protein A, G, or anti-Fc peptide. Simply mixing bsAbs with PEGylated NPs improves tumor targeting and internalization. The two-step strategy involves first accumulating bsAbs (PEG/biotin × tumor marker) on the tumor cell surface, triggering an initial attack via antibody-dependent and complement-dependent cytotoxicity. These bsAbs then capture PEGylated NPs, initiating a second wave of internalization and cytotoxicity. Both strategies aim to enhance the targeting capabilities of PEGylated NPs by enabling specific recognition and binding to disease-specific markers or receptors. This review provides potential pathways for accelerating clinical translation in the development of targeted nanomedicine. Drug delivery ; Nanoparticle ; Drug ; Nanotechnology ; Materials science ; Covalent bond ; Targeted drug delivery ; Pharmacology ; Chemistry ; Medicine Nanoparticle-Based Drug Delivery ; Nanoplatforms for cancer theranostics ; Graphene and Nanomaterials Applications
Authors: Shaohua Xie | Liping Liu | Yuejin Li | Kailong Ye | Daekun Kim | Xing Zhang | Hongliang Xin | Lu Ma | Steven N. Ehrlich | Fudong Liu
Source: Nature Communications (article, 2024-09-12)
Field: Materials Science — Materials Chemistry

Zeolite-promoted platinum catalyst for efficient reduction of nitrogen oxides with hydrogen Internal combustion engine fueled by carbon-free hydrogen (H<sub>2</sub>-ICE) offers a promising alternative for sustainable transportation. Herein, we report a facile and universal strategy through the physical mixing of Pt catalyst with zeolites to significantly improve the catalytic performance in the selective catalytic reduction of nitrogen oxides (NO<sub>x</sub>) with H<sub>2</sub> (H<sub>2</sub>-SCR), a process aiming at NO<sub>x</sub> removal from H<sub>2</sub>-ICE. Via the physical mixing of Pt/TiO<sub>2</sub> with Y zeolite (Pt/TiO<sub>2</sub> + Y), a remarkable enhancement of NO<sub>x</sub> reduction activity and N<sub>2</sub> selectivity was simultaneously achieved. The incorporation of Y zeolite effectively captured the in-situ generated water, fostering a water-rich environment surrounding the Pt active sites. This environment weakened the NO adsorption while concurrently promoting the H<sub>2</sub> activation, leading to the strikingly elevated H<sub>2</sub>-SCR activity and N<sub>2</sub> selectivity on Pt/TiO<sub>2</sub> + Y catalyst. This study provides a unique, easy and sustainable physical mixing approach to achieve proficient heterogeneous catalysis for environmental applications. Catalysis ; Hydrogen ; Platinum ; Carbon fibers ; Zeolite ; Combustion ; Nitrogen ; Inorganic chemistry ; Chemistry ; Nitrogen oxides ; Reduction (mathematics) ; Materials science ; Chemical engineering ; Waste management ; Organic chemistry ; Engineering ; Composite material Catalytic Processes in Materials Science ; Catalysts for Methane Reforming ; Catalysis and Oxidation Reactions
Authors: Yahan Shan | Jinyu Sheng | Qi Zhang | Marc C. A. Stuart | Da‐Hui Qu | Ben L. Feringa
Source: Aggregate (article, 2024-05-14)
Field: Chemistry — Organic Chemistry

Multi‐state photoluminescent properties of an overcrowded alkene‐based molecular motor in aggregates Abstract Photoisomerization and photoluminescence are two distinct energy dissipation pathways in light‐driven molecular motors. The photoisomerization properties of discrete molecular motors have been well established in solution, but their photoluminescent properties have been rarely reported—especially in aggregates. Here, it is shown that an overcrowded alkene‐based molecular motor exhibits distinct dynamic properties in solution and aggregate states, for example, gel and solid states. Despite the poor emissive properties of molecular motors in solution, a bright emission is observed in the aggregate states, including in gel and the crystalline solid. The emission wavelength is highly dependent on the nature of the supramolecular packing and order in the aggregates. As a result, the fluorescent color can be readily tuned reversibly via mechanical grinding and vapor fuming, which provides a new platform for developing multi‐stimuli functional materials. Alkene ; Photoluminescence ; Molecular motor ; Materials science ; Chemistry ; Nanotechnology ; Optoelectronics ; Organic chemistry Supramolecular Chemistry and Complexes ; Luminescence and Fluorescent Materials ; Porphyrin and Phthalocyanine Chemistry
Authors: Jiang Wang | Can Fu | Mengting Jiang | Yi Hu | Yuanda Liu | Menglei Zhu | Jie Yu | Jichao Fu | Ronghui Lin | Di Wu | Zackaria Mahfoud | Sim Ai Jia | Feng‐Xia Liang | Li Li | Jinghua Teng | Lin‐Bao Luo
Source: Advanced Optical Materials (article, 2024-06-21)
Field: Materials Science — Materials Chemistry

PtSe<sub>2</sub>/InP Mixed‐Dimensional Schottky Junction for High‐Performance Self‐Powered Near‐Infrared Photodetection Abstract Self‐powered near‐infrared (NIR) photodetectors utilizing low‐dimensional materials are promising owing to their low‐power‐consumption and superior photoresponse performance. The strong light‐matter interaction and other intriguing physical mechanisms (such as high mobility, dangling‐bond‐free surface) in 2D semiconductor materials, combined with the flexible fabrication of device structures, create new opportunities for the optoelectronic devices. Here, a self‐powered NIR Schottky junction photodetector is demonstrated by vertically stacking 2D PtSe 2 film atop an InP wafer. The strong built‐in electric field formed at PtSe 2 /InP interface endows the device with self‐powered operation with an ultralow dark current of 45 pA at room temperature under 0 V bias. The responsivity and detectivity at 940 nm illumination reach up to 0.718 A W −1 and 4.37 × 10 12 Jones, respectively. Furthermore, TCAD simulations showed that the significant electric field at the PtSe 2 /InP interface is pivotal for its superior self‐powered detection performance. Remarkably, the device achieves a high I light /I dark ratio exceeding 10 5 and a fast response time of 4.35/5.66 µs, and sensitivity to NIR light polarization. This study provides a new perspective for the integration of hybrid 2D materials with 3D semiconductors in the next‐generation optoelectronic devices and integrated systems. Photodetection ; Materials science ; Optoelectronics ; Infrared ; Schottky diode ; Schottky barrier ; Optics ; Photodetector ; Physics 2D Materials and Applications ; Advanced Semiconductor Detectors and Materials ; Nanowire Synthesis and Applications
Authors: Ali Akbar Ashkarran | Hassan Gharibi | Seyed Majed Modaresi | Amir Ata Saei | Morteza Mahmoudi
Source: Nano Letters (article, 2024-08-03)
Field: Chemistry — Spectroscopy

Standardizing Protein Corona Characterization in Nanomedicine: A Multicenter Study to Enhance Reproducibility and Data Homogeneity We recently revealed significant variability in protein corona characterization across various proteomics facilities, indicating that data sets are not comparable between independent studies. This heterogeneity mainly arises from differences in sample preparation protocols, mass spectrometry workflows, and raw data processing. To address this issue, we developed standardized protocols and unified sample preparation workflows, distributing uniform protein corona digests to several top-performing proteomics centers from our previous study. We also examined the influence of using similar mass spectrometry instruments on data homogeneity and standardized database search parameters and data processing workflows. Our findings reveal a remarkable stepwise improvement in protein corona data uniformity, increasing overlaps in protein identification from 11% to 40% across facilities using similar instruments and through a uniform database search. We identify the key parameters behind data heterogeneity and provide recommendations for designing experiments. Our findings should significantly advance the robustness of protein corona analysis for diagnostic and therapeutics applications. Homogeneity (statistics) ; Workflow ; Proteomics ; Nanomedicine ; Computer science ; Mass spectrometry ; Robustness (evolution) ; Raw data ; Data mining ; Nanotechnology ; Materials science ; Database ; Chemistry ; Chromatography ; Nanoparticle ; Machine learning Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Nanoparticle-Based Drug Delivery
Authors: Santi Bardeeniz | Chanin Panjapornpon | Chalermpan Fongsamut | Pailin Ngaotrakanwiwat | M.A. Hussain
Source: Applied Thermal Engineering (article, 2024-01-21)
Field: Materials Science — Materials Chemistry

Digital twin-aided transfer learning for energy efficiency optimization of thermal spray dryers: Leveraging shared drying characteristics across chemicals with limited data Efficient energy use ; Boosting (machine learning) ; Reliability (semiconductor) ; Process (computing) ; Computer science ; Energy consumption ; Process engineering ; Software deployment ; Transfer of learning ; Reliability engineering ; Simulation ; Artificial intelligence ; Engineering High voltage insulation and dielectric phenomena ; Aerosol Filtration and Electrostatic Precipitation ; Building Energy and Comfort Optimization
Authors: Yuan Chiang | Hsieh, Elvis | Chia-Hong Chou | Janosh Riebesell
Source: arXiv (Cornell University) (preprint, 2024-01-30)
Field: Materials Science — Materials Chemistry

LLaMP: Large Language Model Made Powerful for High-fidelity Materials Knowledge Retrieval and Distillation Reducing hallucination of Large Language Models (LLMs) is imperative for use in the sciences, where reliability and reproducibility are crucial. However, LLMs inherently lack long-term memory, making it a nontrivial, ad hoc, and inevitably biased task to fine-tune them on domain-specific literature and data. Here we introduce LLaMP, a multimodal retrieval-augmented generation (RAG) framework of hierarchical reasoning-and-acting (ReAct) agents that can dynamically and recursively interact with computational and experimental data on Materials Project (MP) and run atomistic simulations via high-throughput workflow interface. Without fine-tuning, LLaMP demonstrates strong tool usage ability to comprehend and integrate various modalities of materials science concepts, fetch relevant data stores on the fly, process higher-order data (such as crystal structure and elastic tensor), and streamline complex tasks in computational materials and chemistry. We propose a simple metric combining uncertainty and confidence estimates to evaluate the self-consistency of responses by LLaMP and vanilla LLMs. Our benchmark shows that LLaMP effectively mitigates the intrinsic bias in LLMs, counteracting the errors on bulk moduli, electronic bandgaps, and formation energies that seem to derive from mixed data sources. We also demonstrate LLaMP's capability to edit crystal structures and run annealing molecular dynamics simulations using pre-trained machine-learning force fields. The framework offers an intuitive and nearly hallucination-free approach to exploring and scaling materials informatics, and establishes a pathway for knowledge distillation and fine-tuning other language models. Code and live demo are available at https://github.com/chiang-yuan/llamp Distillation ; Computer science ; High fidelity ; Fidelity ; Natural language processing ; Information retrieval ; Process engineering ; Chemistry ; Engineering ; Chromatography Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; X-ray Diffraction in Crystallography
Authors: Andrew McNaughton | Gautham Krishna Sankar Ramalaxmi | Agustin Kruel | Carter Knutson | Rohith Varikoti | Neeraj Kumar
Source: ACS Omega (article, 2024-10-25)
Field: Materials Science — Materials Chemistry

CACTUS: Chemistry Agent Connecting Tool Usage to Science Large language models (LLMs) have shown remarkable potential in various domains but often lack the ability to access and reason over domain-specific knowledge and tools. In this article, we introduce Chemistry Agent Connecting Tool-Usage to Science (CACTUS), an LLM-based agent that integrates existing cheminformatics tools to enable accurate and advanced reasoning and problem-solving in chemistry and molecular discovery. We evaluate the performance of CACTUS using a diverse set of open-source LLMs, including Gemma-7b, Falcon-7b, MPT-7b, Llama3-8b, and Mistral-7b, on a benchmark of thousands of chemistry questions. Our results demonstrate that CACTUS significantly outperforms baseline LLMs, with the Gemma-7b, Mistral-7b, and Llama3-8b models achieving the highest accuracy regardless of the prompting strategy used. Moreover, we explore the impact of domain-specific prompting and hardware configurations on model performance, highlighting the importance of prompt engineering and the potential for deploying smaller models on consumer-grade hardware without a significant loss in accuracy. By combining the cognitive capabilities of open-source LLMs with widely used domain-specific tools provided by RDKit, CACTUS can assist researchers in tasks such as molecular property prediction, similarity searching, and drug-likeness assessment. Cheminformatics ; Benchmark (surveying) ; Domain (mathematical analysis) ; Computer science ; Cactus ; Set (abstract data type) ; Drug discovery ; Chemistry ; Bioinformatics ; Programming language ; Biology ; Ecology ; Biochemistry ; Mathematics Machine Learning in Materials Science ; Scientific Computing and Data Management ; Topic Modeling