jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yunzhe Zhou | Cheng Chen | Zhenyu Ji | Rajamani Krishna | Zhengyi Di | Daqiang Yuan | Mingyan Wu
Source: ACS Materials Letters (article, 2024-03-11)
Field: Chemistry — Inorganic Chemistry

A Layered Hydrogen-Bonded Organic Framework with C<sub>3</sub>H<sub>6</sub>-Preferred Pores for Efficient One-Step Purification of Methanol-to-Olefins (MTO) Products Separation of methanol-to-olefins (MTO) products to obtain high-purity C2H4 and C3H6 is of great importance, since it provides an alternative process for such important industrial raw materials. However, developing adsorbents with high C2H4/C3H6 selectivity and productivity remains challenging. Herein, we report an ultrastable layered hydrogen-bonded framework (HOF-NBDA), which features C3H6 preferred pore with multiple interlaminar interactions with C3H6. It delivers an ultrahigh C3H6 uptake (3.6 mmol g–1) at 10 kPa, while absorbs only 0.3 mmol g–1 C2H4 and shows a high C2H4/C3H6 selectivity (11.1). Experimental breakthroughs show that HOF-NBDA can efficiently separate C3H6/C2H4 (50/50, v/v) at different flow rates, temperatures, as well as relative humidities and provides a high C2H4 productivity (5.7 mol kg–1, purity ≥99.95%). Meanwhile, 4.5 mol kg–1 C3H6 (purity ≥99.5%) can also be collected during the desorption process. Additionally, thanks to ultrahigh stability, good scalability, and easy regeneration, HOF-NBDA is considered as a promising material for one-step energy-efficient separation of MTO products. Methanol ; Raw material ; Desorption ; Materials science ; Chemical engineering ; Hydrogen ; Selectivity ; Adsorption ; Catalysis ; Chemistry ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Membrane Separation and Gas Transport
Authors: Jinjin Pei | Yuqiang Yan | Chella Perumal Palanisamy | Selvaraj Jayaraman | Prabhu Manickam Natarajan | Vidhya Rekha Umapathy | Sridevi Gopathy | Jeane Rebecca Roy | Janaki Coimbatore Sadagopan | Dwarakesh Thalamati | Monica Mironescu
Source: Green Processing and Synthesis (article, 2024-01-01)
Field: Materials Science — Biomaterials

Materials-based drug delivery approaches: Recent advances and future perspectives Abstract Materials-based drug delivery approaches have garnered substantial attention in recent years due to their potential to revolutionize pharmaceutical interventions. This abstract provides a concise overview of recent advancements and future prospects in this rapidly evolving field. Materials such as nanoparticles, liposomes, polymers, and hydrogels have emerged as versatile carriers for drug delivery. These materials facilitate precise control over drug release kinetics, enabling targeted and sustained therapeutic effects. Smart materials with responsiveness to external stimuli or physiological conditions have further enhanced drug delivery precision. Personalized medicine approaches are gaining traction, tailoring drug delivery systems to individual patient profiles and needs. The horizon for materials-based drug delivery is bright. Ongoing research is focused on refining material design, streamlining production processes, and ensuring safety profiles. Collaborative efforts among researchers, clinicians, and industry stakeholders are crucial for translating these advancements into clinical practice. Additionally, the convergence of drug delivery with diagnostics and imaging holds immense potential for personalized and efficient healthcare solutions. As materials-based drug delivery continues to evolve, it stands poised to reshape the landscape of pharmaceuticals, offering the promise of more effective and patient-centered therapies for a wide range of medical conditions. Drug delivery ; Nanotechnology ; Personalized medicine ; Biopharmaceutical ; Computer science ; Risk analysis (engineering) ; Management science ; Medicine ; Engineering ; Materials science ; Biotechnology ; Bioinformatics Nanoparticle-Based Drug Delivery ; Graphene and Nanomaterials Applications ; Advanced Drug Delivery Systems
Authors: Priyanka Thakur | Navdeep Sharma | Dinesh Pathak | Pankaj Sharma | Kamal Kishore | Shashi Dhar | Madan Lal
Source: Emergent Materials (article, 2024-02-26)
Field: Materials Science — Electronic, Optical and Magnetic Materials

State-of-art review on smart perovskites materials: properties and applications Abstract Materials science has played a very crucial role in developing new technologies so far that could probably address the challenges of economic and ecological sustainability soon. In the field of advanced materials, perovskite oxides stand out to provide a clean environment, ensure enough clean energy, and sanitized water, and provide resources for industrial and growing populations. Perovskite oxides are green and eco-friendly smart materials whose properties might be significantly altered under controlled conditions. In this regard, lead-free perovskite ceramics and their composites such as BaTiO 3 , K 0.5 Na 0.5 NbO 3 , Bi 0.5 Na 0.5 TiO 3 , LaMnO 3 , BaMnO 3 , LaFeO 3 , K 0.5 Na 0.5 NbO 3 -BiFeO 3 , BaTiO 3 -K0 .5 Na 0.5 NbO 3 , BaTiO 3 -Bi 0.5 Na 0.5 TiO 3 , and many more are gaining attention due to their simple stoichiometry, cost-effectiveness, easy synthesis, eco-friendly behavior, and world-wide applications. In this review, a brief overview is given of the crystal structure and piezoelectric, ferroelectric, magnetic, and multiferroic properties of perovskite oxides. An attempt has been made to cover the progress of selective perovskite oxide and its composites. The recent advances of these perovskite oxides and applications in energy storage, energy scavenging applications via multi-layer ceramic capacitors, supercapacitors, solid-oxide fuel cells, piezoelectric actuators, transducers, sensors, and spintronics are also highlighted. Moreover, recent industrial developments based on these selective perovskite oxides are discussed as well. At the end of this review, future perspectives on current developments of perovskite oxides are also evaluated. Perovskite (structure) ; Materials science ; Piezoelectricity ; Nanotechnology ; Ceramic ; Ferroelectricity ; Smart material ; Multiferroics ; Supercapacitor ; Engineering physics ; Composite material ; Optoelectronics ; Capacitance ; Chemical engineering ; Engineering ; Chemistry ; Dielectric Multiferroics and related materials ; Ferroelectric and Piezoelectric Materials ; Dielectric properties of ceramics
Authors: Brandon K. Phan | Kuan-Hsuan Shen | Rishi Gurnani | Tran Doan Huan | Ryan P. Lively | Rampi Ramprasad
Source: npj Computational Materials (article, 2024-08-15)
Field: Materials Science — Materials Chemistry

Gas permeability, diffusivity, and solubility in polymers: Simulation-experiment data fusion and multi-task machine learning Machine learning (ML) models for predicting gas permeability through polymers have traditionally relied on experimental data. While these models exhibit robustness within familiar chemical domains, reliability wanes when applied to new spaces. To address this challenge, we present a multi-tiered multi-task learning framework empowered with advanced machine-crafted polymer fingerprinting algorithms and data fusion techniques. This framework combines scarce “high-fidelity” experimental data with abundant diverse “low-fidelity” simulation or synthetic data, resulting in predictive models that display a high level of generalizability across novel chemical spaces. Additionally, this multi-task scheme capitalizes on known physics and interrelated properties, such as gas diffusivity and solubility, both of which are closely tied to permeability. By amalgamating high throughput generated simulation data with available experimental data for gas permeability, diffusivity, and solubility for various gases, we construct multi-task deep learning models. These models can simultaneously predict all three properties for all gases under consideration, with markedly enhanced predictive accuracy, particularly compared to traditional models reliant solely on experimental data for a singular property. This strategy underscores the potential of coupling high-throughput classical simulations with data fusion methodologies to yield state-of-the-art property predictors, especially when experimental data for targeted properties is scarce. Thermal diffusivity ; Solubility ; Fusion ; Permeability (electromagnetism) ; Polymer ; Task (project management) ; Materials science ; Computer science ; Chemical engineering ; Process engineering ; Composite material ; Thermodynamics ; Chemistry ; Engineering ; Organic chemistry ; Physics ; Systems engineering ; Philosophy Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Membrane Separation and Gas Transport
Authors: Harveen Kaur | Flaviano Della Pia | Ilyes Batatia | Xavier R. Advincula | Benjamin X. Shi | Jinggang Lan | Gábor Cśanyi | Angelos Michaelides | Venkat Kapil
Source: Faraday Discussions (article, 2024-08-09)
Field: Materials Science — Materials Chemistry

Data-efficient fine-tuning of foundational models for first-principles quality sublimation enthalpies Calculating sublimation enthalpies of molecular crystal polymorphs is relevant to a wide range of technological applications. However, predicting these quantities at first-principles accuracy - even with the aid of machine learning potentials - is a challenge that requires sub-kJ mol<sup>-1</sup> accuracy in the potential energy surface and finite-temperature sampling. We present an accurate and data-efficient protocol for training machine learning interatomic potentials by fine-tuning the foundational MACE-MP-0 model and showcase its capabilities on sublimation enthalpies and physical properties of ice polymorphs. Our approach requires only a few tens of training structures to achieve sub-kJ mol<sup>-1</sup> accuracy in the sublimation enthalpies and sub-1% error in densities at finite temperature and pressure. Exploiting this data efficiency, we perform preliminary <i>NPT</i> simulations of hexagonal ice at the random phase approximation level and demonstrate a good agreement with experiments. Our results show promise for finite-temperature modelling of molecular crystals with the accuracy of correlated electronic structure theory methods. Sublimation (psychology) ; Materials science ; Chemistry ; Thermodynamics ; Physics ; Psychology Machine Learning in Materials Science ; Chemical Thermodynamics and Molecular Structure ; Thermal and Kinetic Analysis
Authors: Ivan Malashin | D. A. Martysyuk | В С Тынченко | Andrei Gantimurov | А.В. Семиколенов | Vladimir Nelyub | А. С. Бородулин
Source: Polymers (review, 2024-11-29)
Field: Materials Science — Materials Chemistry

Machine Learning-Based Process Optimization in Biopolymer Manufacturing: A Review The integration of machine learning (ML) into material manufacturing has driven advancements in optimizing biopolymer production processes. ML techniques, applied across various stages of biopolymer production, enable the analysis of complex data generated throughout production, identifying patterns and insights not easily observed through traditional methods. As sustainable alternatives to petrochemical-based plastics, biopolymers present unique challenges due to their reliance on variable bio-based feedstocks and complex processing conditions. This review systematically summarizes the current applications of ML techniques in biopolymer production, aiming to provide a comprehensive reference for future research while highlighting the potential of ML to enhance efficiency, reduce costs, and improve product quality. This review also shows the role of ML algorithms, including supervised, unsupervised, and deep learning algorithms, in optimizing biopolymer manufacturing processes. Biopolymer ; Production (economics) ; Process (computing) ; Computer science ; Biochemical engineering ; Artificial intelligence ; Petrochemical ; Quality (philosophy) ; Machine learning ; Process engineering ; Manufacturing engineering ; Engineering ; Materials science ; Waste management Machine Learning in Materials Science ; Injection Molding Process and Properties ; Computational Drug Discovery Methods
Authors: Ilya Navitski | Agne Ramanaviciute | Simonas Ramanavičius | Maksym Pogorielov | Arūnas Ramanavičius
Source: Nanomaterials (article, 2024-02-28)
Field: Materials Science — Materials Chemistry

MXene-Based Chemo-Sensors and Other Sensing Devices MXenes have received worldwide attention across various scientific and technological fields since the first report of the synthesis of Ti<sub>3</sub>C<sub>2</sub> nanostructures in 2011. The unique characteristics of MXenes, such as superior mechanical strength and flexibility, liquid-phase processability, tunable surface functionality, high electrical conductivity, and the ability to customize their properties, have led to the widespread development and exploration of their applications in energy storage, electronics, biomedicine, catalysis, and environmental technologies. The significant growth in publications related to MXenes over the past decade highlights the extensive research interest in this material. One area that has a great potential for improvement through the integration of MXenes is sensor design. Strain sensors, temperature sensors, pressure sensors, biosensors (both optical and electrochemical), gas sensors, and environmental pollution sensors targeted at volatile organic compounds (VOCs) could all gain numerous improvements from the inclusion of MXenes. This report delves into the current research landscape, exploring the advancements in MXene-based chemo-sensor technologies and examining potential future applications across diverse sensor types. MXenes ; Nanotechnology ; Electronics ; Flexibility (engineering) ; Materials science ; Computer science ; Systems engineering ; Engineering ; Electrical engineering MXene and MAX Phase Materials ; Gas Sensing Nanomaterials and Sensors ; Advanced Memory and Neural Computing
Authors: Kalathur Mohan Ganesh | Seemesh Bhaskar | Vijay Sai Krishna Cheerala | Prajwal Battampara | Roopa Reddy | S. Neelakantan | Narendra Reddy | Sai Sathish Ramamurthy
Source: Nanomaterials (article, 2024-01-02)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Review of Gold Nanoparticles in Surface Plasmon-Coupled Emission Technology: Effect of Shape, Hollow Nanostructures, Nano-Assembly, Metal–Dielectric and Heterometallic Nanohybrids Point-of-care (POC) diagnostic platforms are globally employed in modern smart technologies to detect events or changes in the analyte concentration and provide qualitative and quantitative information in biosensing. Surface plasmon-coupled emission (SPCE) technology has emerged as an effective POC diagnostic tool for developing robust biosensing frameworks. The simplicity, robustness and relevance of the technology has attracted researchers in physical, chemical and biological milieu on account of its unique attributes such as high specificity, sensitivity, low background noise, highly polarized, sharply directional, excellent spectral resolution capabilities. In the past decade, numerous nano-fabrication methods have been developed for augmenting the performance of the conventional SPCE technology. Among them the utility of plasmonic gold nanoparticles (AuNPs) has enabled the demonstration of plethora of reliable biosensing platforms. Here, we review the nano-engineering and biosensing applications of AuNPs based on the shape, hollow morphology, metal-dielectric, nano-assembly and heterometallic nanohybrids under optical as well as biosensing competencies. The current review emphasizes the recent past and evaluates the latest advancements in the field to comprehend the futuristic scope and perspectives of exploiting Au nano-antennas for plasmonic hotspot generation in SPCE technology. Biosensor ; Nanotechnology ; Plasmon ; Materials science ; Colloidal gold ; Nanoparticle ; Nano- ; Fabrication ; Nanostructure ; Dielectric ; Surface plasmon ; Optoelectronics Gold and Silver Nanoparticles Synthesis and Applications ; Advanced biosensing and bioanalysis techniques ; Plasmonic and Surface Plasmon Research
Authors: Yubo Wang | Lu Zhang | Chen Liu | Yiming Luo | Dengyue Chen
Source: Pharmaceutics (article, 2024-02-06)
Field: Materials Science — Biomaterials

Peptide-Mediated Nanocarriers for Targeted Drug Delivery: Developments and Strategies Effective drug delivery is essential for cancer treatment. Drug delivery systems, which can be tailored to targeted transport and integrated tumor therapy, are vital in improving the efficiency of cancer treatment. Peptides play a significant role in various biological and physiological functions and offer high design flexibility, excellent biocompatibility, adjustable morphology, and biodegradability, making them promising candidates for drug delivery. This paper reviews peptide-mediated drug delivery systems, focusing on self-assembled peptides and peptide-drug conjugates. It discusses the mechanisms and structural control of self-assembled peptides, the varieties and roles of peptide-drug conjugates, and strategies to augment peptide stability. The review concludes by addressing challenges and future directions. Nanocarriers ; Drug delivery ; Drug ; Targeted drug delivery ; Biocompatibility ; Peptide ; Pharmacology ; Nanotechnology ; Cancer therapy ; Medicine ; Chemistry ; Cancer ; Materials science ; Biochemistry ; Internal medicine Supramolecular Self-Assembly in Materials ; RNA Interference and Gene Delivery ; Advanced biosensing and bioanalysis techniques
Authors: Yunpeng Zheng | Qinghua Zhang | Caijuan Shi | Zhifang Zhou | Yang Lu | Jian Han | Hetian Chen | Yunpeng Ma | Yujun Zhang | Changpeng Lin | Wei Xu | Weigang Ma | Qian Li | Yueyang Yang | Bin Wei | Bingbing Yang | Mingchu Zou | Wenyu Zhang | Chang Liu | Lvye Dou | Dongliang Yang | Jinle Lan | Di Yi | Xing Zhang | Lin Gu | Ce‐Wen Nan | Yuanhua Lin
Source: Nature Communications (article, 2024-09-03)
Field: Materials Science — Materials Chemistry

Carrier-phonon decoupling in perovskite thermoelectrics via entropy engineering Thermoelectrics converting heat and electricity directly attract broad attentions. To enhance the thermoelectric figure of merit, zT, one of the key points is to decouple the carrier-phonon transport. Here, we propose an entropy engineering strategy to realize the carrier-phonon decoupling in the typical SrTiO<sub>3</sub>-based perovskite thermoelectrics. By high-entropy design, the lattice thermal conductivity could be reduced nearly to the amorphous limit, 1.25 W m<sup>-1</sup> K<sup>-1</sup>. Simultaneously, entropy engineering can tune the Ti displacement, improving the weighted mobility to 65 cm<sup>2</sup> V<sup>-1</sup> s<sup>-1</sup>. Such carrier-phonon decoupling behaviors enable the greatly enhanced μ<sub>W</sub>/κ<sub>L</sub> of ~5.2 × 10<sup>3</sup> cm<sup>3</sup> K J<sup>-1</sup> V<sup>-1</sup>. The measured maximum zT of 0.24 at 488 K and the estimated zT of ~0.8 at 1173 K in (Sr<sub>0.2</sub>Ba<sub>0.2</sub>Ca<sub>0.2</sub>Pb<sub>0.2</sub>La<sub>0.2</sub>)TiO<sub>3</sub> film are among the best of n-type thermoelectric oxides. These results reveal that the entropy engineering may be a promising strategy to decouple the carrier-phonon transport and achieve higher zT in thermoelectrics. Phonon ; Thermoelectric materials ; Thermoelectric effect ; Condensed matter physics ; Decoupling (probability) ; Materials science ; Figure of merit ; Thermal conductivity ; Optoelectronics ; Physics ; Thermodynamics ; Engineering Advanced Thermoelectric Materials and Devices ; Thermal properties of materials ; Thermal Radiation and Cooling Technologies
Authors: Mehrad Nikzadfar | Mahdi Rashvand | Hongwei Zhang | Alex Shenfield | Francesco Genovese | Giuseppe Altieri | Attilio Matera | Iolanda Tornese | Sabina Laveglia | Giuliana Paterna | Carmela Lovallo | Orkhan Mammadov | Burcu Aykanat | Giovanni Carlo Di Renzo
Source: Applied Sciences (article, 2024-10-27)
Field: Chemistry — Analytical Chemistry

Hyperspectral Imaging Aiding Artificial Intelligence: A Reliable Approach for Food Qualification and Safety Hyperspectral imaging (HSI) is one of the non-destructive quality assessment methods providing both spatial and spectral information. HSI in food quality and safety can detect the presence of contaminants, adulterants, and quality attributes, such as moisture, ripeness, and microbial spoilage, in a non-destructive manner by analyzing spectral signatures of food components in a wide range of wavelengths with speed and accuracy. However, analyzing HSI data can be quite complicated and time consuming, in addition to needing some special expertise. Artificial intelligence (AI) has shown immense promise in HSI for the assessment of food quality because it is so powerful at coping with irrelevant information, extracting key features, and building calibration models. This review has shown various machine learning (ML) approaches applied to HSI for quality and safety control of foods. It covers the basic concepts of HSI, advanced preprocessing methods, and strategies for wavelength selection and machine learning methods. The application of HSI to AI increases the speed with which food safety and quality can be inspected. This happens through automation in contaminant detection, classification, and prediction of food quality attributes. So, it can enable decisions in real-time by reducing human error at food inspection. This paper outlines their benefits, challenges, and potential improvements while again assessing the validity and practical usability of HSI technologies in developing reliable calibration models for food quality and safety monitoring. The review concludes that HSI integrated with state-of-the-art AI techniques has good potential to significantly improve the assessment of food quality and safety, and that various ML algorithms have their strengths, and contexts in which they are best applied. Hyperspectral imaging ; Computer science ; Artificial intelligence Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced Chemical Sensor Technologies
Authors: Ewan Cruickshank
Source: ChemPlusChem (article, 2024-03-07)
Field: Materials Science — Electronic, Optical and Magnetic Materials

The Emergence of a Polar Nematic Phase: A Chemist's Insight into the Ferroelectric Nematic Phase The discovery of a new polar nematic phase; the ferroelectric nematic, has generated a great deal of excitement in the field of liquid crystals. To date there have been around 150 materials reported exhibiting the ferroelectric nematic phase, in general, following three key archetypal structures with these compounds known as RM734, DIO and UUQU-4N. In this review, the relationship between the molecular structure and the stability of the ferroelectric nematic, N<sub>F</sub>, phase will be described from a chemist's perspective. This will look to highlight the wide variety of functionalities which have been incorporated into these archetypal structures and how these changes influence the transition temperatures of the mesophases present. The N<sub>F</sub> phase appears to be stabilised particularly by reducing the length of terminal alkyl chains present and adding fluorines laterally along the length of the molecular backbone. This review will look to introduce the background of the ferroelectric nematic phase before then showing the molecular structures of a range of materials which exhibit the phase, describing their structure-property relationships and therefore giving an up-to-date account of the literature for this fascinating new mesophase. Liquid crystal ; Polar ; Ferroelectricity ; Phase (matter) ; Materials science ; Chemical physics ; Chemistry ; Organic chemistry ; Physics ; Optoelectronics Liquid Crystal Research Advancements ; Cephalopods and Marine Biology ; Photoreceptor and optogenetics research