jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Shivalingayya Gaddimath | Shivanand Payamalle | Keshavananada Prabhu Channabasavana Hundi Puttaningaiah | Jaehyun Hur
Source: Journal of Composites Science (article, 2024-01-11)
Field: Materials Science — Biomaterials

Recent Advances in pH and Redox Responsive Polymer Nanocomposites for Cancer Therapy Cancer therapy currently focuses on personalized targeted treatments. A promising approach uses stimuli-responsive biomaterials for site-specific drug release, such as pH- and redox-triggered polymer nanocomposites. These materials respond to the tumor microenvironment, enhance efficacy, and reduce off-target effects. Cancer cells with anomalous properties such as acidic cytosolic pH and elevated redox potential are targeted by these biomaterials. An imbalance in ions and biological thiols in the cytoplasm contributes to tumor growth. Functionalized polymer nanocomposites with large surface areas and specific targeting outperform conventional small-molecule materials. To overcome problems such as low bioavailability, uncontrolled drug release, and poor cell penetration, multifunctional nanomaterials make it easier for drugs to enter certain cellular or subcellular systems. High therapeutic efficacy is achieved through surface functionalization, site-specific targeting, and the use of stimuli-responsive components. In particular, pH and redox dual-stimuli-based polymeric nanocomposites for cancer therapeutics have scarcely been reported. This article provides recent progress in pH- and redox-responsive polymer nanocomposites for site-specific drug delivery in cancer therapy. It explores the design principles, fabrication methods, mechanisms of action, and prospects of these dual-stimuli-responsive biomaterials. Cancer therapy ; Nanotechnology ; Drug delivery ; Surface modification ; Redox ; Cancer cell ; Nanocomposite ; Tumor microenvironment ; Chemistry ; Biophysics ; Materials science ; Cancer ; Cancer research ; Tumor cells ; Medicine ; Biology ; Organic chemistry Nanoparticle-Based Drug Delivery ; Nanoplatforms for cancer theranostics ; Graphene and Nanomaterials Applications
Authors: Rajeev Gupta
Source: Discover Electronics (review, 2024-11-14)
Field: Materials Science — Electronic, Optical and Magnetic Materials

A review of functionalized nanomaterials for supercapacitor and hybrid capacitor technologies Functionalized nanomaterials (FN) have gained significant global attention due to their unique nanoscale properties and promising applications in clean energy storage, especially in supercapacitors (SCs). SCs, known for their rapid charging, high power density, and long cycle life, store energy through electrical double layer capacitance (EDLC) and pseudocapacitance mechanisms. FN enhance SC performance by combining various constituents, leveraging effects such as improved electronic, optical, and mechanical properties. Techniques like non-covalent coupling, pore tuning, doping, and compositing are employed to enhance electrochemical properties by creating abundant active surfaces and minimizing ion transport pathways. To address energy density limitations, hybrid capacitors, also called asymmetric SCs, combine battery-type materials (e.g., metal oxides, conducting polymers) with carbon-based electrode materials to utilize redox processes and electrical double layers. These innovations aim to overcome the lower energy density seen in traditional secondary batteries, such as lithium-ion batteries. This review highlights advancements from the past decade in the application of functionalized nanomaterials, including carbon, conducting polymers, and metal oxides, in three main types of SCs: EDLCs, pseudocapacitors, and hybrid capacitors. It covers their composition, structure, device fabrication, properties, and electrochemical performance, emphasizing their potential to revolutionize energy storage technologies. Supercapacitor ; Nanomaterials ; Nanotechnology ; Materials science ; Capacitor ; Business ; Engineering ; Electrical engineering ; Chemistry ; Capacitance ; Electrode ; Voltage Supercapacitor Materials and Fabrication ; Advancements in Battery Materials ; Conducting polymers and applications
Authors: Xiaohe Tian | Li Cao | Keming Zhang | Rui Zhang | Xueqin Li | Chongshan Yin | Shaofei Wang
Source: Angewandte Chemie International Edition (article, 2024-10-08)
Field: Materials Science — Materials Chemistry

Molecular Weaving Towards Flexible Covalent Organic Framework Membranes for Efficient Gas Separations Covalent organic frameworks (COFs) exhibit considerable potential in gas separations owing to their remarkable stability and tunable pore structures. Nevertheless, their application as gas separation membranes is hindered by limited size-sieving capabilities and poor processability. In this study, we propose a novel molecular weaving strategy that combines hydroxyl polymers and 2D TpPa-SO<sub>3</sub>H COF nanosheets, achieving high gas separation efficiency. Driven by the strong electrostatic interactions, the hydroxyl chains thread through the COF pores, effectively weaving and assembling the composites to achieve exceptional flexibility and high mechanical strength. The penetrated chains also reduce the effective pore size of COFs, and combined with the "secondary confinement effect" stemming from abundant CO<sub>2</sub> sorption sites in the channels, the PVA@TpPa-SO<sub>3</sub>H membrane demonstrates a remarkable H<sub>2</sub> permeance of 1267.3 GPU and an H<sub>2</sub>/CO<sub>2</sub> selectivity of 43, surpassing the 2008 Robson upper bound limit. This facile strategy holds promise for the manufacture of large-area COF-based membranes for small-sized gas separations. Permeance ; Membrane ; Covalent bond ; Gas separation ; Weaving ; Polymer ; Covalent organic framework ; Sorption ; Selectivity ; Materials science ; Nanotechnology ; Chemical engineering ; Chemistry ; Composite material ; Organic chemistry ; Engineering ; Catalysis ; Adsorption Covalent Organic Framework Applications ; Metal-Organic Frameworks: Synthesis and Applications ; Membrane Separation and Gas Transport
Authors: Farhad Ali Mohammed | Tangxin Xiao | Leyong Wang | Robert B. P. Elmes
Source: Chemical Communications (review, 2024-01-01)
Field: Chemistry — Spectroscopy

Macrocyclic receptors for anion recognition Macrocyclic receptors have emerged as versatile and efficient molecular tools for the recognition and sensing of anions, playing a pivotal role in molecular recognition and supramolecular chemistry. The following review provides an overview of the recent advances in the design, synthesis, and applications of macrocyclic receptors specifically tailored for anion recognition. The unique structural features of macrocycles, such as their well-defined structures and pre-organised binding sites, contribute to their exceptional anion-binding capabilities that have led to their application across a broad range of the chemical and biological sciences. Receptor ; Chemistry ; Ion ; Combinatorial chemistry ; Biochemistry ; Organic chemistry Molecular Sensors and Ion Detection ; Analytical Chemistry and Sensors ; Chemical Synthesis and Analysis
Authors: Xitong Zhu | Xiao Luo | Yunzhou Deng | Huan Wei | Feng Peng | Lei Ying | Fei Huang | Yuanyuan Hu | Yizheng Jin
Source: Science Advances (article, 2024-08-16)
Field: Materials Science — Materials Chemistry

Doping bilayer hole-transport polymer strategy stabilizing solution-processed green quantum-dot light-emitting diodes Quantum-dot light-emitting diodes (QLEDs) are solution-processed electroluminescence devices with great potential as energy-saving, large-area, and low-cost display and lighting technologies. Ideally, the organic hole-transport layers (HTLs) in QLEDs should simultaneously deliver efficient hole injection and transport, effective electron blocking, and robust electrochemical stability. However, it is still challenging for a single HTL to fulfill all these stringent criteria. Here, we demonstrate a general design of doping-bilayer polymer-HTL architecture for stabilizing high-efficiency QLEDs. We show that the bilayer HTLs combining the electrochemical-stable polymer and the electron-blocking polymer unexpectedly increase the hole injection barrier. We mitigated the problem by p-doping of the underlying sublayer of the bilayer HTLs. Consequently, green QLEDs with an unprecedented maximum luminance of 1,340,000 cd m<sup>-2</sup> and a record-long operational lifetime (<i>T</i><sub>95</sub> lifetime at an initial luminance of 1000 cd m<sup>-2</sup> is 17,700 hours) were achieved. The universality of the strategy is examined in various polymer-HTL systems, providing a general route toward high-performance solution-processed QLEDs. Electroluminescence ; Bilayer ; Optoelectronics ; Light-emitting diode ; Materials science ; Quantum dot ; Diode ; Doping ; Luminance ; Polymer ; Nanotechnology ; Optics ; Chemistry ; Physics ; Layer (electronics) ; Membrane ; Composite material Quantum Dots Synthesis And Properties ; Organic Light-Emitting Diodes Research ; Perovskite Materials and Applications
Authors: Wei Wang | Yichong Chen | Pingyun Feng | Xianhui Bu
Source: Advanced Materials (article, 2024-05-08)
Field: Chemistry — Inorganic Chemistry

Tailorable Multi‐Modular Pore‐Space‐Partitioned Vanadium Metal‐Organic Frameworks for Gas Separation Currently, few porous vanadium metal-organic frameworks (V-MOFs) are known and even fewer are obtainable as single crystals, resulting in limited information on their structures and properties. Here this work demonstrates remarkable promise of V-MOFs by presenting an extensible family of V-MOFs with tailorable pore geometry and properties. The synthesis leverages inter-modular synergy on a tri-modular pore-partitioned platform. New V-MOFs show a broad range of structural features and sorption properties suitable for gas storage and separation applications for C<sub>2</sub>H<sub>2</sub>/CO<sub>2</sub>, C<sub>2</sub>H<sub>6</sub>/C<sub>2</sub>H<sub>4</sub>, and C<sub>3</sub>H<sub>8</sub>/C<sub>3</sub>H<sub>6</sub>. The c/a ratio of the hexagonal cell, a measure of pore shape, is tunable from 0.612 to 1.258. Other tunable properties include pore size from 5.0 to 10.9 Å and surface area from 820 to 2964 m<sup>2</sup> g<sup>-1</sup>. With C<sub>2</sub>H<sub>2</sub>/CO<sub>2</sub> selectivity from 3.3 to 11 and high uptake capacity for C<sub>2</sub>H<sub>2</sub> from 65.2 to 182 cm<sup>3</sup> g<sup>-1</sup> (298K, 1 bar), an efficient separation is confirmed by breakthrough experiments. The near-record high uptake for C<sub>2</sub>H<sub>6</sub> (166.8 cm<sup>3</sup> g<sup>-1</sup>) contributes to the promise for C<sub>2</sub>H<sub>6</sub>-selective separation of C<sub>2</sub>H<sub>6</sub>/C<sub>2</sub>H<sub>4</sub>. The multi-module pore expansion enables transition from C<sub>3</sub>H<sub>6</sub>-selective to more desirable C<sub>3</sub>H<sub>8</sub>-selective separation with extraordinarily high C<sub>3</sub>H<sub>8</sub> uptake (254.9 cm<sup>3</sup> g<sup>-1</sup>) and high separation potential (1.25 mmol g<sup>-1</sup>) for C<sub>3</sub>H<sub>8</sub>/C<sub>3</sub>H<sub>6</sub> (50:50 v/v) mixture. Metal-organic framework ; Materials science ; Vanadium ; Selectivity ; Porosity ; Gas separation ; Mesoporous material ; Sorption ; Chemical engineering ; Nanotechnology ; Characterisation of pore space in soil ; Metal ; Crystallography ; Physical chemistry ; Organic chemistry ; Adsorption ; Chemistry ; Composite material ; Catalysis ; Membrane Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Membrane Separation and Gas Transport
Authors: Joren Van Herck | M.V. Gil | Kevin Maik Jablonka | Alex Abrudan | Andy S. Anker | Mehrdad Asgari | Ben Blaiszik | Antonio Buffo | Leander Choudhury | Clémence Corminbœuf | Hilal Daglar | Amir Mohammad Elahi | Ian Foster | Susana García | Matthew Garvin | Guillaume Godin | Lydia L. Good | Jianan Gu | N.‐X. Hu | Xin Jin | Tanja Junkers | Seda Keskın | Tuomas P. J. Knowles | Rubén Laplaza | Michele Lessona | Sauradeep Majumdar | Hossein Mashhadimoslem | Ruaraidh D. McIntosh | Seyed Mohamad Moosavi | Beatriz Mouriño | Francesca Nerli | C. Pevida | Neda Poudineh | Mahyar Rajabi Kochi | Kadi L. Saar | Fahimeh Hooriabad Saboor | Morteza Sagharichiha | K. J. Schmidt | Jiale Shi | Elena Simone | Dennis Svatunek | Marco Taddei | Igor V. Tetko | D. Tolnai | Sahar Vahdatifar | Jonathan K. Whitmer | D. C. Florian Wieland | Regine Willumeit‐Römer | Andreas Züttel | Berend Smit
Source: Chemical Science (article, 2024-11-22)
Field: Materials Science — Materials Chemistry

Assessment of fine-tuned large language models for real-world chemistry and material science applications The current generation of large language models (LLMs) has limited chemical knowledge. Recently, it has been shown that these LLMs can learn and predict chemical properties through fine-tuning. Using natural language to train machine learning models opens doors to a wider chemical audience, as field-specific featurization techniques can be omitted. In this work, we explore the potential and limitations of this approach. We studied the performance of fine-tuning three open-source LLMs (GPT-J-6B, Llama-3.1-8B, and Mistral-7B) for a range of different chemical questions. We benchmark their performances against "traditional" machine learning models and find that, in most cases, the fine-tuning approach is superior for a simple classification problem. Depending on the size of the dataset and the type of questions, we also successfully address more sophisticated problems. The most important conclusions of this work are that, for all datasets considered, their conversion into an LLM fine-tuning training set is straightforward and that fine-tuning with even relatively small datasets leads to predictive models. These results suggest that the systematic use of LLMs to guide experiments and simulations will be a powerful technique in any research study, significantly reducing unnecessary experiments or computations. Benchmark (surveying) ; Computer science ; Set (abstract data type) ; Field (mathematics) ; Computation ; Fine-tuning ; Work (physics) ; Simple (philosophy) ; Range (aeronautics) ; Machine learning ; Artificial intelligence ; Engineering ; Algorithm ; Epistemology ; Programming language Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Topic Modeling
Authors: Seokhyun Choung | Wongyu Park | Jinuk Moon | Jeong Woo Han
Source: Chemical Engineering Journal (article, 2024-06-02)
Field: Materials Science — Materials Chemistry

Rise of machine learning potentials in heterogeneous catalysis: Developments, applications, and prospects Renewable energy ; Computer science ; Biochemical engineering ; Computational model ; Computational simulation ; Field (mathematics) ; Artificial intelligence ; Nanotechnology ; Industrial engineering ; Systems engineering ; Computational science ; Engineering ; Materials science Machine Learning in Materials Science ; Topic Modeling ; Electrocatalysts for Energy Conversion
Authors: Minghong Li | Xi He | Chaolin Wu | Li Wang | Xin Zhang | Xiangnan Gong | Xiping Zeng | Yingzhou Huang
Source: ACS Sensors (article, 2024-02-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Deep Learning Enabled SERS Identification of Gaseous Molecules on Flexible Plasmonic MOF Nanowire Films Through the capture of a target molecule at the metal surface with a highly confined electromagnetic field induced by surface plasmon, surface enhanced Raman spectroscopy (SERS) emerges as a spectral analysis technology with high sensitivity. However, accurate SERS identification of a gaseous molecule with low density and high velocity is still a challenge due to its difficulty in capture. In this work, a flexible paper-based plasmonic metal-organic framework (MOF) film consisting of Ag nanowires@ZIF-8 (AgNWs@ZIF-8) is fabricated for SERS detection of gaseous molecules. Benefiting from its micronanopores generated by the nanowire network and ZIF-8 shell, the effective capture of the gaseous molecule is achieved, and its SERS spectrum is obtained in this paper-based flexible plasmonic MOF nanowire film. With optimal structure parameters, spectra of gaseous 4-aminothiophenol, 4-mercaptophenol, and dithiohydroquinone demonstrate that this film has good SERS performance, which could maintain obvious Raman signals within 30 days during reproducible detection. To realize SERS identification of gaseous molecules, deep learning is performed based on the SERS spectra of the mixed gaseous analyte obtained in this flexible porous film. The results point out that an artificial neural network algorithm could identify gaseous aldehydes (gaseous biomarker of colorectal cancer) in simulated exhaled breath with high accuracy at 93.7%. The integration of the flexible paper-based film sensors with deep learning offers a promising new approach for noninvasive colorectal cancer screening. Our work explores SERS applications in gaseous analyte detection and has broad potential in clinical medicine, food safety, environmental monitoring, etc. Plasmon ; Nanowire ; Materials science ; Nanotechnology ; Surface-enhanced Raman spectroscopy ; Analyte ; Raman spectroscopy ; Molecule ; Raman scattering ; Porosity ; Optoelectronics ; Chemistry ; Optics Gold and Silver Nanoparticles Synthesis and Applications ; Advanced Chemical Sensor Technologies ; Biosensors and Analytical Detection
Authors: Keyao Li | Yiming Wang | Dapu Pi | Baoli Li | Haitao Luan | Xinyuan Fang | Peng Chen | Yanqing Lu | Miṅ Gu
Source: Opto-Electronic Advances (article, 2024-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Multi-dimensional multiplexing optical secret sharing framework with cascaded liquid crystal holograms Secret sharing is a promising technology for information encryption by splitting the secret information into different shares. However, the traditional scheme suffers from information leakage in decryption process since the amount of available information channels is limited. Herein, we propose and demonstrate an optical secret sharing framework based on the multi-dimensional multiplexing liquid crystal (LC) holograms. The LC holograms are used as spatially separated shares to carry secret images. The polarization of the incident light and the distance between different shares are served as secret keys, which can significantly improve the information security and capacity. Besides, the decryption condition is also restricted by the applied external voltage due to the variant diffraction efficiency, which further increases the information security. In implementation, an artificial neural network (ANN) model is developed to carefully design the phase distribution of each LC hologram. With the advantage of high security, high capacity and simple configuration, our optical secret sharing framework has great potentials in optical encryption and dynamic holographic display. Holography ; Multiplexing ; Computer science ; Materials science ; Optics ; Optoelectronics ; Physics ; Telecommunications Liquid Crystal Research Advancements ; Advanced Optical Imaging Technologies ; Quantum optics and atomic interactions
Authors: Huiqiang Hu | Yunlong Mei | Yunpeng Wei | Zhenyu Xu | Yuping Zhao | Huaxing Xu | Xiaobo Mao | Luqi Huang
Source: LWT (article, 2024-07-01)
Field: Chemistry — Analytical Chemistry

Chemical composition prediction in goji (Lycium barbarum) using hyperspectral imaging and multi-task 1DCNN with attention mechanism The bioactive components of goji berries (Lycium barbarum) are crucial determinants of their nutritional and commercial value. In this study, we combined hyperspectral imaging technology (HSI) with a one-dimensional convolutional neural network (1DCNN) to predict the content of chemical compositions in goji. To enhance the model's ability to focus on relevant information, we introduced the channel attention module (CAM), spectral attention module (SAM) and fused them together, which can focus on output features of the convolution kernels differently and adaptively emphasize more effective spectral bands, respectively. Moreover, considering the limitations of traditional single-task prediction methods and the inherent correlations among different constituents, we employed a multi-task CNN for the simultaneous prediction of various goji constituents. The results indicate that the attention-enhanced 1DCNN model outperforms both the partial least squares regression (PLSR) model and the vanilla 1DCNN. With multi-task learning, the model achieves optimal performance, achieving an average R2 of 0.9435 for the prediction of the three components. Our research develops an efficient and accurate method for predicting the constituents of goji berries, providing a valuable, convenient, and effective tool for assessing and detecting the quality of fruit products in the food industry. Hyperspectral imaging ; Lycium ; Partial least squares regression ; Convolutional neural network ; Computer science ; Artificial intelligence ; Task (project management) ; Biological system ; Focus (optics) ; Pattern recognition (psychology) ; Machine learning ; Mathematics ; Biology ; Engineering ; Physics Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Phytochemicals and Antioxidant Activities
Authors: Wajid Hussain | Salem Algarni | Ghulam Rasool | Hasan Shahzad | Mujahid Abbas | Talal Alqahtani | Kashif Irshad
Source: ACS Omega (review, 2024-02-29)
Field: Materials Science — Materials Chemistry

Advances in Nanoparticle-Enhanced Thermoelectric Materials from Synthesis to Energy Harvesting: A Review This comprehensive review analysis examines the domain of composite thermoelectric materials that integrate nanoparticles, providing a critical assessment of their methods for improving thermoelectric properties and the procedures used for their fabrication. This study examines several approaches to enhance power factor and lattice thermal conductivity, emphasizing the influence of secondary phases and structural alterations. This study investigates the impact of synthesis methods on the electrical characteristics of materials, with a particular focus on novel techniques such as electrodeposition onto carbon nanotubes. The acquired insights provide useful guidance for the creation of new thermoelectric materials. The review also compares and contrasts organic and inorganic thermoelectric materials, with a particular focus on the potential of inorganic materials in the context of waste heat recovery and power production within industries. This analysis highlights the role of inorganic materials in improving energy efficiency and promoting environmental sustainability. Thermoelectric materials ; Thermoelectric effect ; Materials science ; Nanotechnology ; Context (archaeology) ; Carbon nanotube ; Nanoparticle ; Sustainability ; Engineering physics ; Thermal conductivity ; Engineering ; Composite material ; Physics Advanced Thermoelectric Materials and Devices ; Thermal Radiation and Cooling Technologies ; Thermal properties of materials