jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Shuo Sun | Jin Li | Xiaoxun Li | Xianyu Zhao | Kun Li | Liang Chen
Source: Microsystems & Nanoengineering (article, 2024-12-24)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Dynamic 3D metasurface holography via cascaded polymer dispersed liquid crystal Metasurface with natural static structure limits the development of dynamic metasurface holographic display with rapid response and broadband. Currently, liquid crystal (LC) was integrated onto the metasurface to convert the passive metasuface into an active one. But, majority of LC-assisted active metasurfaces often exhibit trade-offs among degree of freedom (DoF, typically less than 2), information capacity, response speed, and crosstalk. Herein, at first time, we experimentally demonstrate a cascaded device with polymer dispersed liquid crystal (PDLC) and broadband metasurface, enabling dynamic three-dimensional (3D) holographic display with ultra-high contrast, rapid response and continuous regulation. The PDLC droplets enable modulation of scattering state of incident light by high-speed dynamic control system for electric scanning. Based on self-addressing, rapid response and multi-channel PDLC-metasurface device, the dynamic holographic effect of monochrome holographic images switching and color-changing holographic display with broadband, low-crosstalk and high contrast, has been achieved. Our approach offers a novel perspective on dynamic metasurface. Holography ; Liquid crystal ; Broadband ; Materials science ; Optics ; Optoelectronics ; Crosstalk ; Liquid-crystal display ; Scattering ; Holographic display ; Physics Metamaterials and Metasurfaces Applications ; Liquid Crystal Research Advancements ; Advanced Antenna and Metasurface Technologies
Authors: Peiyuan Li | Tao Shen | Li Li | Yuanzhong Wang
Source: Plant Methods (article, 2024-03-16)
Field: Chemistry — Analytical Chemistry

Optimization of the selection of suitable harvesting periods for medicinal plants: taking Dendrobium officinale as an example These results show that the ResNet model possesses a fast and accurate recognition ability, and at the same time can provide a scientific basis for the processing of a large number of sample data; the PLSR model with MSC and SG preprocessing can predict the dry matter content of Dendrobium stems and leaves, respectively; The suitable harvesting period for D. officinale is from November to April of the following year, with the best harvesting period being December. During this period, it is necessary to ensure sufficient water supply between 7:00 and 10:00 every day and to provide a certain degree of light blocking between 14:00 and 17:00. Dendrobium ; Mathematics ; Partial least squares regression ; Dry matter ; Chemometrics ; Statistics ; Computer science ; Botany ; Biology ; Machine learning Spectroscopy and Chemometric Analyses ; Biological and pharmacological studies of plants ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Laurent Proville | Anshuman Choudhury
Source: Nature Materials (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Unravelling the jerky glide of dislocations in body-centred cubic crystals Dislocation ; Nucleation ; Materials science ; Condensed matter physics ; Atomic units ; Cubic crystal system ; Deformation (meteorology) ; Partial dislocations ; Crystal (programming language) ; Thermal ; Plasticity ; Crystallography ; Physics ; Composite material ; Thermodynamics ; Chemistry Microstructure and mechanical properties ; Electromagnetic Effects on Materials ; Force Microscopy Techniques and Applications
Authors: Alexandru A. Moldovan | Andrew G. P. Maloney
Source: Crystal Growth & Design (article, 2024-05-01)
Field: Materials Science — Materials Chemistry

Surface Analysis─From Crystal Structures to Particle Properties Understanding the surface properties of particles is crucial for optimizing the performance of formulated products in various industries. However, acquiring this understanding often requires expensive trial-and-error studies. Here, we present advanced surface analysis tools that enable the visualization and quantification of chemical and topological information derived from crystallographic data. By employing functional group analysis, roughness calculations, and statistical interaction data, we facilitate direct comparisons of surfaces. We further demonstrate the practicality of our approach by correlating the sticking propensity of distinct ibuprofen morphologies with surface and particle descriptors calculated from a single crystal structure. Our findings support and expand upon previous work, demonstrating that the presence of a carboxylic acid group on the {011} facet leads to significant differences in particle properties and explains the higher electrostatic potential observed in the block-like morphology. While our surface analysis tools are not intended to replace the importance of chemical intuition and expertise, they provide valuable insights for formulators and particle engineers, facilitating informed, data-driven decisions to mitigate formulation risks. This research represents a significant step toward a comprehensive understanding of particle surfaces and their impact on products. Particle (ecology) ; Surface roughness ; Visualization ; Nanotechnology ; Surface (topology) ; Intuition ; Chemical physics ; Computer science ; Materials science ; Statistical physics ; Chemistry ; Biological system ; Data mining ; Physics ; Mathematics Crystallization and Solubility Studies ; Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Robert Haigh | Malindu Sandanayake | Soorya Sasi | Ehsan Yaghoubi | Paul Joseph | Zora Vrcelj
Source: Journal of Building Engineering (review, 2024-02-06)
Field: Materials Science — Polymers and Plastics

Microstructural attributes and physiochemical behaviours of concrete incorporating various synthetic textile and cardboard fibres: A comparative review The excessive use of virgin materials for manufacturing cementitious materials in the building and construction industry creates detrimental environmental effects. The integration of waste materials in composites promote sustainable opportunities within the industry. This paper presents a systematic review of the use of synthetic textile and cardboard waste fibres in concrete in conjunction with infrequently used additives such as gypsum, metakaolin, alumina and zinc oxide. The synthetic nature of polyester and nylon fibres facilitates their integration into high alkaline environments. Kraft fibres, derived from cardboard, exhibit high tensile strength but also absorb water, limiting their use in construction. However, by employing coating applications, these limitations can be mitigated. Gypsum prolongs setting times, improves workability, and enhances the sulphate resistance of concrete. Metakaolin's reactivity with calcium hydroxide results in improved strength, density, and durability. Alumina oxide can enhance the mechanical and thermal characteristics. Whereas zinc oxide, known for its UV protection and antimicrobial properties has a retarder effect and can improve the materials longevity in extreme environments. Scanning electron microscope images of the synthetic textile fibres demonstrate sustained durability features in the composite materials whereas, kraft fibres exhibited deterioration properties. Fibre bonding and deterioration aspects of the material varied within each matrix environment. This corresponded to a varied microstructural response with the fibrous materials. The findings from this review demonstrate the sustainability opportunities when integrating additive and waste materials in composite designs. Materials science ; Durability ; cardboard ; Ultimate tensile strength ; Composite material ; Cementitious ; Metakaolin ; Gypsum ; Textile ; Coating ; Raw material ; Cement ; Environmentally friendly Natural Fiber Reinforced Composites ; Innovative concrete reinforcement materials ; Innovations in Concrete and Construction Materials
Authors: Puja Patel | Vadanasundari Vedarethinam | Maame A. Korsah | Michael K. Danquah | Jaison Jeevanandam
Source: Applied Sciences (article, 2024-02-22)
Field: Materials Science — Biomaterials

Exploring the Potential of Nanoparticles in the Treatment of Breast Cancer: Current Applications and Future Directions Breast cancer (BC) ranks among the most diagnosed solid tumors worldwide. For decades, significant research efforts have been dedicated to finding selective treatments for these solid tumors. Currently, the primary treatment method for BC involves surgery, with the subsequent utilization of radiotherapy and chemotherapy. However, these subsequent treatments often fall short of effectively treating BC due to their side effects and harm to healthy tissues. Today, a range of nanoparticles are being developed to target BC cells without affecting the surrounding healthy tissues. This in-depth review, based on studies, seeks to shed light on these specially designed nanoparticles and their potential in BC treatment. Typically, therapeutic drugs or naturally occurring bioactive compounds are incorporated into precisely crafted nanoparticles. This enhances their solubility, longevity in the bloodstream, and distribution in the body while also minimizing side effects and immune reactions. Nanoparticles have been designed to address the shortcomings of standalone therapeutics and traverse various biological obstacles spanning the systemic, microenvironmental, and cellular that differ among patients and diseases. We prioritize breakthroughs in nanoparticle design to surpass diverse delivery obstacles and believe that smart nanoparticle engineering not only enhances effectiveness for general delivery but also allows customized solutions for specific needs, ultimately leading to better outcomes for patients. Breast cancer ; Nanotechnology ; Medicine ; Radiation therapy ; Drug delivery ; Cancer ; Materials science ; Internal medicine Nanoparticle-Based Drug Delivery ; Nanoplatforms for cancer theranostics ; Graphene and Nanomaterials Applications
Authors: Shengxi Jiang | Yujia Zheng | Hao Xia | Zexin Liu | Shuquan Rao | Yingbo Wang | Hongyu Sun | Xiong Lu | Chaoming Xie
Source: Advanced NanoBiomed Research (article, 2024-03-31)
Field: Materials Science — Biomaterials

Oxygen‐Releasing Hydrogels for Tissue Regeneration Hydrogels have emerged as a focal point of research in the biomedical field due to their applications in tissue repair. However, the majority of hydrogels lack the capability to release oxygen, constraining their therapeutic outcomes in environments with hypoxic tissues. In recent years, oxygen‐releasing hydrogels have garnered extensive attention in the field of tissue engineering, owing to their ability to modulate oxygen release and meet the diverse oxygenation requirements of various tissues. These hydrogels can enhance repair efficiency and promote tissue regeneration in hypoxic tissue environments. The design of oxygen‐releasing hydrogels primarily involves the utilization of diverse oxygen sources, such as algae, perfluorocarbons, and peroxides, to achieve optimal tissue oxygenation. This review provides a comprehensive summary of the design and fabrication strategies of oxygen‐releasing hydrogels, discusses deeply into their underlying oxygen‐releasing mechanisms, and their myriad applications in tissue repair along with the prospective challenges. Self-healing hydrogels ; Regeneration (biology) ; Oxygen ; Chemistry ; Biophysics ; Cell biology ; Biology ; Polymer chemistry ; Organic chemistry Electrospun Nanofibers in Biomedical Applications ; Tissue Engineering and Regenerative Medicine ; 3D Printing in Biomedical Research
Authors: Vittoria Bisutti | Lúcio Flávio Macêdo Mota | Diana Giannuzzi | Alessandro Toscano | Nicolò Amalfitano | Stefano Schiavon | Sara Pegolo | Alessio Cecchinato
Source: Food Chemistry (article, 2024-08-08)
Field: Chemistry — Analytical Chemistry

Infrared spectroscopy coupled with machine learning algorithms for predicting the detailed milk mineral profile in dairy cattle Milk minerals are not only essential components for human health, but they can be informative for milk quality and cow's health. Herein, we investigated the feasibility of Fourier Transformed mid Infrared (FTIR) spectroscopy for the prediction of a detailed panel of 17 macro, trace, and environmental elements in bovine milk, using partial least squares regression (PLS) and machine learning approaches. The automatic machine learning significantly outperformed the PLS regression in terms of prediction performances of the mineral elements. For macrominerals, the R<sup>2</sup> ranged from 0.59 to 0.78. Promising predictability was achieved for Cu and B (R<sup>2</sup> = 0.66 and 0.74, respectively) and more moderate ones for Fe, Mn, Zn, and Al (R<sup>2</sup> from 0.48 to 0.58). These results provide a reliable basis for a rapid and cost-effective quantification of these traits, serving as a resource for dairy farmers seeking to enhance the quality of milk production and optimize cheese properties. Algorithm ; Dairy cattle ; Spectroscopy ; Chemistry ; Computer science ; Machine learning ; Animal science ; Biology ; Physics Spectroscopy and Chemometric Analyses ; Meat and Animal Product Quality ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Maria Luís Paz | Clara Sousa
Source: Molecules (article, 2024-08-02)
Field: Chemistry — Analytical Chemistry

Discrimination and Quantification of Cotton and Polyester Textile Samples Using Near-Infrared and Mid-Infrared Spectroscopies In the textile industry, cotton and polyester (PES) are among the most used fibres to produce clothes. The correct identification and accurate composition estimate of fibres are mandatory, and environmentally friendly and precise techniques are welcome. In this context, the use of near-infrared (NIR) and mid-infrared (MIR) spectroscopies to distinguish between cotton and PES samples and further estimate the cotton content of blended samples were evaluated. Infrared spectra were acquired and modelled through diverse chemometric models: principal component analysis; partial least squares discriminant analysis; and partial least squares (PLS) regression. Both techniques (NIR and MIR) presented good potential for cotton and PES sample discrimination, although the results obtained with NIR spectroscopy were slightly better. Regarding cotton content estimates, the calibration errors of the PLS models were 3.3% and 6.5% for NIR and MIR spectroscopy, respectively. The PLS models were validated with two different sets of samples: prediction set 1, containing blended cotton + PES samples (like those used in the calibration step), and prediction set 2, containing cotton + PES + distinct fibre samples. Prediction set 2 was included to address one of the biggest known drawbacks of such chemometric models, which is the prediction of sample types that are not used in the calibration. Despite the poorer results obtained for prediction set 2, all the errors were lower than 8%, proving the suitability of the techniques for cotton content estimation. It should be stressed that the textile samples used in this work came from different geographic origins (cotton) and were of distinct presentations (raw, yarn, knitted/woven fabric), which strengthens our findings. Partial least squares regression ; Calibration ; Textile ; Principal component analysis ; Context (archaeology) ; Chemometrics ; Linear discriminant analysis ; Polyester ; Near-infrared spectroscopy ; Mathematics ; Biological system ; Materials science ; Statistics ; Chemistry ; Chromatography ; Composite material ; Optics ; Physics Spectroscopy and Chemometric Analyses ; Industrial Vision Systems and Defect Detection ; Meat and Animal Product Quality
Authors: Rafael Villamil Carreón | Ana G. Rodríguez‐Hernández | Laura Rosa | José Juan Gervacio-Arciniega | Siva Kumar Krishnan
Source: ACS Sensors (article, 2024-10-31)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Mechanically Flexible, Large-Area Fabrication of Three-Dimensional Dendritic Au Films for Reproducible Surface-Enhanced Raman Scattering Detection of Nanoplastics The escalating crisis of nanoplastic pollution in water and food products demands the development of novel methodologies for detection and recycling. Despite various techniques available, surface-enhanced Raman scattering (SERS) is emerging as a highly efficient technique for the trace detection of micro/nanoplastics. However, the development of highly reproducible and stable, flexible SERS substrates that can be used for sensitive detection in environmental medium remains a challenge. Here, we report a fabrication of large-area, three-dimensional (3D), and highly flexible SERS substrate based on porous dendritic Au films onto a flexible indium tin oxide (ITO) substrate via facile, thermal evaporation of Au over the vacuum-compatible deep eutectic solvent (DES)-coated glass substrate and subsequent direct transfer process. The as-fabricated 3D dendritic Au/ITO flexible substrates can be used for ultrasensitive SERS detection of crystal violet (CV) as probe analyte molecules with the limit of detection (LOD) as low as 6.4 × 10<sup>-15</sup> M, with good signal reproducibility (RSD of 11.3%). In addition, the substrate showed excellent sensitivity in detecting nanoplastics such as poly(ethylene terephthalate) (200 nm) and polystyrene (100 nm) with LODs reaching up to 0.051 and 8.2 μg/mL, respectively. This work provides a facile approach for the preparation of highly flexible plasmonic substrates, showing great potential for the SERS detection of a variety of environmental pollutants. Fabrication ; Raman scattering ; Materials science ; Nanotechnology ; Surface (topology) ; Raman spectroscopy ; Scattering ; Optics ; Medicine ; Physics Gold and Silver Nanoparticles Synthesis and Applications ; Nanomaterials and Printing Technologies ; Additive Manufacturing and 3D Printing Technologies
Authors: Dileswar Pradhan | Swarna Jaiswal | Brijesh K. Tiwari | Amit K. Jaiswal
Source: Ultrasonics Sonochemistry (article, 2024-08-30)
Field: Materials Science — Biomaterials

Nanocellulose separation from barley straw via ultrasound-assisted choline chloride – Formic acid deep eutectic solvent pretreatment and high-intensity ultrasonication The present study aims at investigating the application of ultrasound assisted choline chloride (ChCl) - formic acid (FA) deep eutectic solvent (DES) pretreatment of Barley straw. In addition, the efficiency of a wet grinding followed by high intensity ultrasound (HIUS) treatment for production of cellulose nanofibers (CNF) has been evaluated. The DES (using ChCl: FA at 1:9 M ratio) treatment at 45 kHz ultrasound frequency and 3 h of treatment duration resulted in 84.68 ± 1.02 % and 82.96 ± 0.79 % of lignin and hemicellulose solubilisation, respectively. The purification of DES treated solid residue resulted in cellulose with more than 90 % purity. Further, 10 min of wet grinding followed by 40 min of HIUS treatment resulted in more than 80 % nano-fibrillation efficiency. The produced CNF had diameters less than 100 nm in number size distribution and type I cellulose structure. This study confirmed that the developed process offers a sustainable method for producing nanocellulose from agricultural waste. Choline chloride ; Deep eutectic solvent ; Sonication ; Formic acid ; Solvent ; Chemistry ; Eutectic system ; Chloride ; Nuclear chemistry ; Chromatography ; Materials science ; Organic chemistry Advanced Cellulose Research Studies ; Biofuel production and bioconversion ; Lignin and Wood Chemistry
Authors: Sanjana S. Nalige | Phillip Galonska | Payam Kelich | Linda Sistemich | Christian Herrmann | Lela Vuković | Sebastian Kruss | Martina Havenith
Source: Nature Communications (article, 2024-08-08)
Field: Materials Science — Materials Chemistry

Fluorescence changes in carbon nanotube sensors correlate with THz absorption of hydration Single wall carbon nanotubes (SWCNTs) functionalized with (bio-)polymers such as DNA are soluble in water and sense analytes by analyte-specific changes of their intrinsic fluorescence. Such SWCNT-based (bio-)sensors translate the binding of a molecule (molecular recognition) into a measurable optical signal. This signal transduction is crucial for all types of molecular sensors to achieve high sensitivities. Although there is an increasing number of SWCNT-based sensors, there is yet no molecular understanding of the observed changes in the SWCNT's fluorescence. Here, we report THz experiments that map changes in the local hydration of the solvated SWCNT upon binding of analytes such as the neurotransmitter dopamine or the vitamin riboflavin. The THz amplitude signal serves as a measure of the coupling of charge fluctuations in the SWCNTs to the charge density fluctuations in the hydration layer. We find a linear (inverse) correlation between changes in THz amplitude and the intensity of the change in fluorescence induced by the analytes. Simulations show that the organic corona shapes the local water, which determines the exciton dynamics. Thus, THz signals are a quantitative predictor for signal transduction strength and can be used as a guiding chemical design principle for optimizing fluorescent biosensors. Fluorescence ; Carbon nanotube ; Absorption (acoustics) ; Materials science ; Nanotechnology ; Carbon fibers ; Optoelectronics ; Biophysics ; Optics ; Composite material ; Physics ; Biology Carbon Nanotubes in Composites ; Mechanical and Optical Resonators ; Nanopore and Nanochannel Transport Studies
Authors: Yi Xu | Fu Li | Jianqiang Gu | Zhiwei Bi | Bing Cao | Quanlong Yang | Jiaguang Han | Qinghua Hu | Weili Zhang
Source: Advanced Photonics Nexus (article, 2024-02-19)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Spectral transfer-learning-based metasurface design assisted by complex-valued deep neural network Recently, deep learning has been used to establish the nonlinear and nonintuitive mapping between physical structures and electromagnetic responses of meta-atoms for higher computational efficiency. However, to obtain sufficiently accurate predictions, the conventional deep-learning-based method consumes excessive time to collect the data set, thus hindering its wide application in this interdisciplinary field. We introduce a spectral transfer-learning-based metasurface design method to achieve excellent performance on a small data set with only 1000 samples in the target waveband by utilizing open-source data from another spectral range. We demonstrate three transfer strategies and experimentally quantify their performance, among which the “frozen-none” robustly improves the prediction accuracy by ∼26% compared to direct learning. We propose to use a complex-valued deep neural network during the training process to further improve the spectral predicting precision by ∼30% compared to its real-valued counterparts. We design several typical teraherz metadevices by employing a hybrid inverse model consolidating this trained target network and a global optimization algorithm. The simulated results successfully validate the capability of our approach. Our work provides a universal methodology for efficient and accurate metasurface design in arbitrary wavebands, which will pave the way toward the automated and mass production of metasurfaces. Transfer of learning ; Artificial neural network ; Deep learning ; Computer science ; Transfer (computing) ; Artificial intelligence ; Parallel computing Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Advanced Wireless Communication Technologies