jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yulin Wu | Yixing Wang | Dezhu Zhang | Fang Xu | Liheng Dai | Kai Qu | Hongyan Cao | Yu Xia | Siyao Li | Kang Huang | Zhi Xu
Source: Angewandte Chemie International Edition (article, 2023-10-27)
Field: Materials Science — Materials Chemistry

Crystallizing Self‐Standing Covalent Organic Framework Membranes for Ultrafast Proton Transport in Flow Batteries Covalent organic frameworks (COFs) display great potential to be assembled into proton conductive membranes for their uniform and controllable pore structure, yet constructing self-standing COF membrane with high crystallinity to fully exploit their ordered crystalline channels for efficient ionic conduction remains a great challenge. Here, a macromolecular-mediated crystallization strategy is designed to manipulate the crystallization of self-standing COF membrane, where the -SO<sub>3</sub> H groups in introduced sulfonated macromolecule chains function as the sites to interact with the precursors of COF and thus offer long-range ordered template for membrane crystallization. The optimized self-standing COF membrane composed of highly-ordered nanopores exhibits high proton conductivity (75 mS cm<sup>-1</sup> at 100 % relative humidity and 20 °C) and excellent flow battery performance, outperforming Nafion 212 and reported membranes. Meanwhile, the long-term run of membrane is achieved with the help of the anchoring effect of flexible macromolecule chains. Our work provides inspiration to design self-standing COF membranes with ordered channels for permselective application. Membrane ; Crystallization ; Macromolecule ; Materials science ; Crystallinity ; Covalent bond ; Chemical engineering ; Ionic bonding ; Nanopore ; Self-assembly ; Nanotechnology ; Chemistry ; Organic chemistry ; Composite material ; Ion Covalent Organic Framework Applications ; Metal-Organic Frameworks: Synthesis and Applications ; Fuel Cells and Related Materials
Authors: Maddalen Ayala | Marianne Thomsen | Massimo Pizzol
Source: Algal Research (article, 2023-03-05)
Field: Materials Science — Biomaterials

Life Cycle Assessment of pilot scale production of seaweed-based bioplastic The use of seaweed as a bioresource for plastic production is gaining momentum. However, the environmental impacts of the production of this novel bioplastic are still unknown. In this research, we assess the environmental impacts of the production of a bioplastic film at an experimental pilot scale using Life Cycle Assessment (LCA). The system boundaries chosen for this analysis include seaweed cultivation accounting for its carbon uptake, alginate extraction, production of bioplastic film at the pilot scale and different end-of-life pathways. The recirculation of different seaweed co-products from the alginate extraction step into the bioplastic film production is also assessed using scenario modelling and the analysis is completed with a carbon balance and an uncertainty analysis. The results show the main hotspot at the pilot scale is the last step in the production, film fabrication, mainly due to the glycerol in this process. The results also vary significantly depending on the end-of-life of the bioplastic, composting reduces the impacts by 30 % compared to incineration. Bioplastic ; Life-cycle assessment ; Environmental science ; Production (economics) ; Engineering ; Waste management biodegradable polymer synthesis and properties ; Microplastics and Plastic Pollution ; Sustainable Supply Chain Management
Authors: Kaihang Shi | Zhao Li | Dylan M. Anstine | Dai Tang | Coray M. Colina | David S. Sholl | J. Ilja Siepmann | Randall Q. Snurr
Source: Journal of Chemical Theory and Computation (article, 2023-02-03)
Field: Chemistry — Inorganic Chemistry

Two-Dimensional Energy Histograms as Features for Machine Learning to Predict Adsorption in Diverse Nanoporous Materials A major obstacle for machine learning (ML) in chemical science is the lack of physically informed feature representations that provide both accurate prediction and easy interpretability of the ML model. In this work, we describe adsorption systems using novel two-dimensional energy histogram (2D-EH) features, which are obtained from the probe-adsorbent energies and energy gradients at grid points located throughout the adsorbent. The 2D-EH features encode both energetic and structural information of the material and lead to highly accurate ML models (coefficient of determination <i>R</i><sup>2</sup> ∼ 0.94-0.99) for predicting single-component adsorption capacity in metal-organic frameworks (MOFs). We consider the adsorption of spherical molecules (Kr and Xe), linear alkanes with a wide range of aspect ratios (ethane, propane, <i>n</i>-butane, and <i>n</i>-hexane), and a branched alkane (2,2-dimethylbutane) over a wide range of temperatures and pressures. The interpretable 2D-EH features enable the ML model to learn the basic physics of adsorption in pores from the training data. We show that these MOF-data-trained ML models are transferrable to different families of amorphous nanoporous materials. We also identify several adsorption systems where capillary condensation occurs, and ML predictions are more challenging. Nevertheless, our 2D-EH features still outperform structural features including those derived from persistent homology. The novel 2D-EH features may help accelerate the discovery and design of advanced nanoporous materials using ML for gas storage and separation in the future. Nanoporous ; Adsorption ; Interpretability ; Histogram ; Materials science ; Computer science ; Biological system ; Chemistry ; Nanotechnology ; Artificial intelligence ; Physical chemistry Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Covalent Organic Framework Applications
Authors: Kihoon Bang | Doosun Hong | Youngtae Park | Dong-Hun Kim | Sang Soo Han | Hyuck Mo Lee
Source: Nature Communications (article, 2023-05-25)
Field: Materials Science — Materials Chemistry

Machine learning-enabled exploration of the electrochemical stability of real-scale metallic nanoparticles Surface Pourbaix diagrams are critical to understanding the stability of nanomaterials in electrochemical environments. Their construction based on density functional theory is, however, prohibitively expensive for real-scale systems, such as several nanometer-size nanoparticles (NPs). Herein, with the aim of accelerating the accurate prediction of adsorption energies, we developed a bond-type embedded crystal graph convolutional neural network (BE-CGCNN) model in which four bonding types were treated differently. Owing to the enhanced accuracy of the bond-type embedding approach, we demonstrate the construction of reliable Pourbaix diagrams for very large-size NPs involving up to 6525 atoms (approximately 4.8 nm in diameter), which enables the exploration of electrochemical stability over various NP sizes and shapes. BE-CGCNN-based Pourbaix diagrams well reproduce the experimental observations with increasing NP size. This work suggests a method for accelerated Pourbaix diagram construction for real-scale and arbitrarily shaped NPs, which would significantly open up an avenue for electrochemical stability studies. Pourbaix diagram ; Materials science ; Stability (learning theory) ; Electrochemistry ; Nanomaterials ; Nanometre ; Scale (ratio) ; Nanoparticle ; Nanotechnology ; Computer science ; Machine learning ; Physical chemistry ; Chemistry ; Physics ; Composite material ; Electrode Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; CO2 Reduction Techniques and Catalysts
Authors: Feiyang Wang | Hong‐Hui Wu | Linshuo Dong | Guangfei Pan | Xiaoye Zhou | Shuize Wang | Ruiqiang Guo | Guilin Wu | Junheng Gao | Fu‐Zhi Dai | Xinping Mao
Source: Journal of Material Science and Technology (review, 2023-06-08)
Field: Materials Science — Materials Chemistry

Atomic-scale simulations in multi-component alloys and compounds: A review on advances in interatomic potential Component (thermodynamics) ; Interatomic potential ; Atomic units ; Reliability (semiconductor) ; Materials science ; Scale (ratio) ; Range (aeronautics) ; Computer science ; Nanotechnology ; Molecular dynamics ; Thermodynamics ; Physics ; Computational chemistry ; Chemistry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electron and X-Ray Spectroscopy Techniques
Authors: Jisheng Yang | Yawen Feng | Bingzhen Wang | Jianshuo Miao | Shihai Wei | Han Li | Liuting Mo | Zhiyong Qin
Source: Chemical Engineering Journal (article, 2023-08-19)
Field: Materials Science — Polymers and Plastics

Tough, multifunctional, and green double-network binary solvent eutectogel with in-situ generation of lignin nanoparticles based on one-step dual phase separations for wearable flexible strain sensors Materials science ; Deep eutectic solvent ; Polyvinyl alcohol ; Biocompatibility ; Chemical engineering ; Self-healing hydrogels ; Gauge factor ; Polymer ; Nanoparticle ; Nanotechnology ; Composite material ; Polymer chemistry ; Eutectic system Conducting polymers and applications ; Advanced Sensor and Energy Harvesting Materials ; Lignin and Wood Chemistry
Authors: Muhammad Abdullah Adil | A.G. Olabi | Mohammad Ali Abdelkareem | Hussain Alawadhi | Ahmed Bahaa | Khaled Elsaid | Cristina Rodríguez
Source: Journal of Energy Storage (article, 2023-06-07)
Field: Materials Science — Materials Chemistry

In-situ grown bimetallic FeCu MOF-MXene composite for solid-state asymmetric supercapacitors Materials science ; Bimetallic strip ; Supercapacitor ; MXenes ; Composite number ; Metal-organic framework ; Crystallinity ; Specific surface area ; Nanotechnology ; Electrochemistry ; Nanocomposite ; Chemical engineering ; Electrode ; Metal ; Composite material ; Catalysis ; Chemistry ; Metallurgy MXene and MAX Phase Materials ; Supercapacitor Materials and Fabrication ; Advancements in Battery Materials
Authors: Ziqing Zhou | Yinghui Shang | Xiaodi Liu | Yong Yang
Source: npj Computational Materials (article, 2023-01-23)
Field: Materials Science — Materials Chemistry

A generative deep learning framework for inverse design of compositionally complex bulk metallic glasses Abstract The design of bulk metallic glasses (BMGs) via machine learning (ML) has been a topic of active research recently. However, the prior ML models were mostly built upon supervised learning algorithms with human inputs to navigate the high dimensional compositional space, which becomes inefficient with the increasing compositional complexity in BMGs. Here, we develop a generative deep-learning framework to directly generate compositionally complex BMGs, such as high entropy BMGs. Our framework is built on the unsupervised Generative Adversarial Network (GAN) algorithm for data generation and the supervised Boosted Trees algorithm for data evaluation. We studied systematically the confounding effect of various data descriptors and the literature data on the effectiveness of our framework both numerically and experimentally. Most importantly, we demonstrate that our generative deep learning framework is capable of producing composition-property mappings, therefore paving the way for the inverse design of BMGs. Generative grammar ; Computer science ; Artificial intelligence ; Deep learning ; Inverse ; Positive definiteness ; Generative model ; Machine learning ; Mathematics Phase-change materials and chalcogenides ; Metallic Glasses and Amorphous Alloys ; Glass properties and applications
Authors: Ying Li | Xiaomin Xia | Wenxue Hou | Hanlin Lv | Jie Liu | Xue Li
Source: International Journal of Nanomedicine (review, 2023-03-01)
Field: Materials Science — Materials Chemistry

How Effective are Metal Nanotherapeutic Platforms Against Bacterial Infections? A Comprehensive Review of Literature The emergence of multidrug-resistant bacteria has been deemed a global crisis that affects humans worldwide. Novel anti-infection strategies are desperately needed because of the limitations of conventional antibiotics. However, the increasing gap between clinical demand and antimicrobial treatment innovation, as well as the membrane permeability obstacle especially in gram-negative bacteria fearfully restrict the reformation of antibacterial strategy. Metal-organic frameworks (MOFs) have the advantages of adjustable apertures, high drug-loading rates, tailorable structures, and superior biocompatibilities, enabling their utilization as drug delivery carriers in biotherapy applications. Additionally, the metal elements in MOFs are usually bactericidal. This article provides a review of the state-of-The-art design, the underlying antibacterial mechanisms and antibacterial applications of MOF- and MOF-based drug-loading materials. In addition, the existing problems and future perspectives of MOF- and MOF-based drug-loading materials are also discussed. Nanotechnology ; Drug delivery ; Antimicrobial ; Drug ; Antibiotics ; Materials science ; Metal-organic framework ; Membrane permeability ; Nanomaterials ; Biochemical engineering ; Chemistry ; Medicine ; Membrane ; Microbiology ; Pharmacology ; Biology ; Engineering Nanoparticles: synthesis and applications ; Gut microbiota and health ; SARS-CoV-2 and COVID-19 Research
Authors: Huanhuan Li | Wei Sheng | Suleiman A. Haruna | Md Mehedi Hassan | Quansheng Chen
Source: Comprehensive Reviews in Food Science and Food Safety (review, 2023-08-07)
Field: Materials Science — Materials Chemistry

Recent advances in rare earth ion‐doped upconversion nanomaterials: From design to their applications in food safety analysis The misuse of chemicals in agricultural systems and food production leads to an increase in contaminants in food, which ultimately has adverse effects on human health. This situation has prompted a demand for sophisticated detection technologies with rapid and sensitive features, as concerns over food safety and quality have grown around the globe. The rare earth ion-doped upconversion nanoparticle (UCNP)-based sensor has emerged as an innovative and promising approach for detecting and analyzing food contaminants due to its superior photophysical properties, including low autofluorescence background, deep penetration of light, low toxicity, and minimal photodamage to the biological samples. The aim of this review was to discuss an outline of the applications of UCNPs to detect contaminants in food matrices, with particular attention on the determination of heavy metals, pesticides, pathogenic bacteria, mycotoxins, and antibiotics. The review briefly discusses the mechanism of upconversion (UC) luminescence, the synthesis, modification, functionality of UCNPs, as well as the detection principles for the design of UC biosensors. Furthermore, because current UCNP research on food safety detection is still at an early stage, this review identifies several bottlenecks that must be overcome in UCNPs and discusses the future prospects for its application in the field of food analysis. Food safety ; Nanotechnology ; Biochemical engineering ; Human health ; Environmental science ; Computer science ; Risk analysis (engineering) ; Biotechnology ; Materials science ; Chemistry ; Business ; Biology ; Food science ; Engineering ; Medicine ; Environmental health Luminescence Properties of Advanced Materials ; Luminescence and Fluorescent Materials ; Mercury impact and mitigation studies
Authors: Milad Ranjbar | Mohammad Hossein Azizi Tabrizzad | Gholamhassan Asadi | Hamed Ahari
Source: Heliyon (article, 2023-08-01)
Field: Materials Science — Biomaterials

Investigating the microbial properties of sodium alginate/chitosan edible film containing red beetroot anthocyanin extract for smart packaging in chicken fillet as a pH indicator The current trend in the production of smart films involves the use of pH-responsive color indicators derived from natural sources. In line with this trend, the aim of this research is to produce edible films from sodium alginate (A) and chitosan (Ch) incorporating red beet anthocyanin (Ac) extract, and to assess the properties of these films and their use as coatings for chicken fillets. The study employed a factorial design to evaluate the effects of treatments C (control), A25%-ch75% (films consisting of 25% sodium alginate and 75% chitosan), and A25%-ch75%-Ac (films consisting of 25% sodium alginate, 75% chitosan, and red beet anthocyanin). The findings indicate that the inclusion of red beet anthocyanin extract did not result in any discernible differences in the FTIR spectra of the film samples. Analysis of the XRD results revealed that the addition of the extract led to a reduction in the crystal structure of the film. Moreover, SEM results demonstrated that the extract caused alterations in the polymer chains and an increase in the porosity of the film matrix. With regard to the chicken fillet samples coated with the film, over time, there was an increase in microbial analysis (total microorganism count and Staphylococcus aureus coagulase-positive) and chemical properties (pH, peroxide, thiobarbituric acid, and nitrogen compounds) for all samples. However, this trend was significantly lower in the samples coated with the Ac extract (P < 0.05). Texture analysis results revealed that the hardness parameter of all samples decreased over the storage period, while the samples containing the Ac extract demonstrated a significant increase in this parameter (P < 0.05). Additionally, the color changes of the pH sensor corresponded to the anthocyanin structure. Based on the results, the smart film composed of sodium alginate/chitosan incorporating red beet anthocyanin extract has the potential to enhance the quality, prolong the shelf life, and decrease the microbial load of chicken fillet when used as a coating. Furthermore, red beet anthocyanin can serve as a suitable indicator for spoilage changes in packaged food products. Anthocyanin ; Chitosan ; Food science ; Chemistry ; Red cabbage ; Food packaging ; Materials science ; Biochemistry Nanocomposite Films for Food Packaging ; Botanical Research and Applications ; Medicinal Plants and Neuroprotection
Authors: Yating Hu | Benxue Ma | Huting Wang | Yuanjia Zhang | Yujie Li | Guowei Yu
Source: Frontiers in Plant Science (article, 2023-05-08)
Field: Chemistry — Analytical Chemistry

Detecting different pesticide residues on Hami melon surface using hyperspectral imaging combined with 1D-CNN and information fusion Efficient, rapid, and non-destructive detection of pesticide residues in fruits and vegetables is essential for food safety. The visible/near infrared (VNIR) and short-wave infrared (SWIR) hyperspectral imaging (HSI) systems were used to detect different types of pesticide residues on the surface of Hami melon. Taking four pesticides commonly used in Hami melon as the object, the effectiveness of single-band spectral range and information fusion in the classification of different pesticides was compared. The results showed that the classification effect of pesticide residues was better by using the spectral range after information fusion. Then, a custom multi-branch one-dimensional convolutional neural network (1D-CNN) model with the attention mechanism was proposed and compared with the traditional machine learning classification model K-nearest neighbor (KNN) algorithm and random forest (RF). The traditional machine learning classification model accuracy of both models was over 80.00%. However, the classification results using the proposed 1D-CNN were more satisfactory. After the full spectrum data was fused, it was input into the 1D-CNN model, and its accuracy, precision, recall, and F1-score value were 94.00%, 94.06%, 94.00%, and 0.9396, respectively. This study showed that both VNIR and SWIR hyperspectral imaging combined with a classification model could non-destructively detect different pesticide residues on the surface of Hami melon. The classification result using the SWIR spectrum was better than that using the VNIR spectrum, and the classification result using the information fusion spectrum was better than that using SWIR. This study can provide a valuable reference for the non-destructive detection of pesticide residues on the surface of other large, thick-skinned fruits. VNIR ; Hyperspectral imaging ; Artificial intelligence ; Convolutional neural network ; Pattern recognition (psychology) ; Support vector machine ; Pesticide residue ; Computer science ; Electromagnetic spectrum ; Remote sensing ; Pesticide ; Physics ; Agronomy ; Optics ; Geography Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Advanced Chemical Sensor Technologies
Authors: Yijing Liu | Rankun Zhang | Le Lin | Yichao Wang | Changping Liu | Rentao Mu | Qiang Fu
Source: Nature Communications (article, 2023-02-04)
Field: Materials Science — Materials Chemistry

Direct observation of accelerating hydrogen spillover via surface-lattice-confinement effect Uncovering how hydrogen transfers and what factors control hydrogen conductivity on solid surface is essential for enhancing catalytic performance of H-involving reactions, which is however hampered due to the structural complexity of powder catalysts, in particular, for oxide catalysts. Here, we construct stripe-like MnO(001) and grid-like Mn<sub>3</sub>O<sub>4</sub>(001) monolayers on Pt(111) substrate and investigate hydrogen spillover atop. Atomic-scale visualization demonstrates that hydrogen species from Pt diffuse unidirectionally along the stripes on MnO(001), whereas it exhibits an isotropic pathway on Mn<sub>3</sub>O<sub>4</sub>(001). Dynamic surface imaging in H<sub>2</sub> atmosphere reveals that hydrogen diffuses 4 times more rapidly on MnO than the case on Mn<sub>3</sub>O<sub>4</sub>, which is promoted by one-dimension surface-lattice-confinement effect. Theoretical calculations indicate that a uniform and medium O-O distance favors hydrogen diffusion while low-coordinate surface O atom inhibits it. Our work illustrates the surface-lattice-confinement effect of oxide catalysts on hydrogen spillover and provides a promising route to improve the hydrogen spillover efficiency. Hydrogen spillover ; Catalysis ; Hydrogen ; Materials science ; Lattice (music) ; Oxide ; Chemical physics ; Spillover effect ; Chemical engineering ; Conductivity ; Nanotechnology ; Chemistry ; Physical chemistry ; Physics ; Metallurgy Catalytic Processes in Materials Science ; Ammonia Synthesis and Nitrogen Reduction ; Advanced Photocatalysis Techniques
Authors: Xuanyi Wang | Shuang Liu | Jing Yan | Junping Zhang | Qiuyu Zhang | Yi Yan
Source: Materials (review, 2023-04-07)
Field: Materials Science — Materials Chemistry

Recent Progress of Polymeric Corrosion Inhibitor: Structure and Application An anti-corrosion inhibitor is one of the most useful methods to prevent metal corrosion toward different media. In comparison with small molecular inhibitors, a polymeric inhibitor can integrate more adsorption groups and generate a synergetic effect, which has been widely used in industry and become a hot topic in academic research. Generally, both natural polymer-based inhibitors and synthetic polymeric inhibitors have been developed. Herein, we summarize the recent progress of polymeric inhibitors during the last decade, especially the structure design and application of synthetic polymeric inhibitor and related hybrid/composite. Corrosion inhibitor ; Corrosion ; Materials science ; Polymer ; Nanotechnology ; Metallurgy ; Composite material Corrosion Behavior and Inhibition ; Concrete Corrosion and Durability ; Polymer composites and self-healing