jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Olivia Marshall | Andrew Sutherland
Source: Chemical Science (review, 2025-01-01)
Field: Chemistry — Organic Chemistry

Expanding the fluorescence toolkit: molecular design, synthesis and biological application of unnatural amino acids Fluorescence imaging has become an indispensable tool in modern biology, enabling the visualisation of dynamic molecular processes with spatial and temporal precision. Traditional strategies rely heavily on the conjugation of large, extrinsic fluorophores, such as green fluorscent protein or organic dyes, through linkers to proteins or peptides of interest. While sometimes effective, these bulky labels can interfere with native protein structure, function, and interactions, limiting their utility in studying sensitive or compact biological systems. In contrast, fluorescent unnatural amino acids that can be site-specifically incorporated into proteins with minimal perturbation, provide high-resolution insights into molecular behaviour while preserving biological integrity. This perspective focuses on recent advances in the synthesis and application of fluorescent unnatural amino acids, particularly through the structural modification of natural aromatic-containing residues such as phenylalanine, tyrosine, and tryptophan. Additionally, the incorporation of small-molecule fluorophores as amino acid side chains to create versatile probes is also discussed. The utility of these fluorescent amino acids in biological chemistry is highlighted through their application in imaging protein dynamics, measuring enzyme activity and studying biomolecular interactions. Together, these advances establish fluorescent amino acids as a valuable and evolving toolkit for minimally invasive biological imaging. Fluorescence ; Computer science ; Fluorescent protein ; Computational biology ; Visualization ; Nanotechnology ; Chemistry ; Combinatorial chemistry ; Human–computer interaction ; Biological system ; Biochemical engineering ; Biology ; Artificial intelligence ; Biochemistry ; Engineering ; Green fluorescent protein ; Materials science ; Physics Click Chemistry and Applications ; bioluminescence and chemiluminescence research ; Microbial Natural Products and Biosynthesis
Authors: Victor Akpe | Ian Edwin Cock
Source: Journal of Nanotheranostics (article, 2025-10-23)
Field: Materials Science — Biomaterials

Advances in Cancer Treatment Through Nanotheranostics and Emerging Therapies The integration of nanotheranostics into cancer treatment represents a transformative shift in oncology, combining precision diagnostics with targeted therapeutic interventions. This manuscript explores the advancements in nanotechnology-driven cancer therapies, highlighting the role of engineered nanoparticles, such as liposomes, dendrimers, polymeric micelles, and virus-like particles, in enhancing drug delivery, real-time imaging, and tumor-specific targeting. Additionally, emerging therapies, including immunotherapy, gene editing, and chromophore-assisted light inactivation (CALI), are discussed in the context of personalized medicine. The convergence of these strategies is poised to redefine cancer treatment paradigms, improving therapeutic efficacy while minimizing systemic toxicity. This review outlines the key challenges, current limitations, and future directions in nanotheranostic applications, emphasizing the need for interdisciplinary collaboration to optimize their clinical translation. Cancer treatment ; Context (archaeology) ; Precision medicine ; Transformative learning ; Cancer ; Personalized medicine Nanoparticle-Based Drug Delivery ; Nanoplatforms for cancer theranostics ; RNA Interference and Gene Delivery
Authors: Masoumeh Nazari | Yaser Mike Banad | Safura Sharifi
Source: Scientific Reports (article, 2025-05-21)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Inverse designed aperiodic multilayer perfect absorbers for mid infrared enable tunability switchability and angular robustness We present a class of inverse-designed, aperiodic multilayer graphene-based perfect absorbers operating in the mid-infrared spectrum (3-5 μm), a range vital for atmospheric transparency and advanced sensing. Our design leverages a fixed material sequence-graphene, PPSU dielectric spacers, and PbSe layers on a gold substrate-while achieving precise spectral tunability solely through layer thickness variation, enabling absorption peak control in 0.25 μm steps without any change in material composition. This physical tunability allows scalable fabrication of wavelength-specific devices using a single manufacturing process. We further demonstrate electrical switchability by dynamically modulating graphene's chemical potential (µc from 0 eV to 1 eV), enabling absorption amplitude control and wavelength redshifting without structural alteration. The proposed absorber achieves > 99.9% efficiency using only five graphene layers in a compact ~ 2 μm stack, offering significant advantages in size, weight, power, and cost. Our hybrid micro-genetic inverse design algorithm enables this performance while preserving > 90% absorption at incidence angles up to 52°, supporting broad angular robustness. Extensive simulation and field distribution analyses confirm the role of plasmonic confinement and impedance matching. Additionally, we validate the design's fabrication tolerance and benchmark its performance against recent state-of-the-art absorbers. By combining advanced inverse design with nanophotonic structures, our work advances the field of mid-infrared absorbers, providing a scalable and efficient platform for next-generation optical devices. Aperiodic graph ; Robustness (evolution) ; Inverse ; Infrared ; Materials science ; Inverse problem ; Computer science ; Optoelectronics ; Physics ; Optics ; Mathematics ; Mathematical analysis ; Biology ; Combinatorics ; Gene ; Geometry Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Antenna Design and Optimization
Authors: Md Wadud Ahmed | S. Sharar Alam | Alin Khaliduzzaman | J.L. Emmert | Mohammed Kamruzzaman
Source: Journal of the Science of Food and Agriculture (article, 2025-04-17)
Field: Chemistry — Analytical Chemistry

Non‐destructive measurement of eggshell strength using <scp>NIR</scp> spectroscopy and explainable artificial intelligence This study demonstrated that NIR spectroscopy, integrated with explainable AI, is a robust, non-destructive and environmentally sustainable approach for eggshell strength prediction. This innovative method holds significant potential for optimizing resource utilization and enhancing quality control in the egg industry. © 2025 The Author(s). Journal of the Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry. Partial least squares regression ; Eggshell ; Interpretability ; Multivariate statistics ; Artificial intelligence ; Mean squared error ; Linear discriminant analysis ; Chemometrics ; Principal component analysis ; Computer science ; Machine learning ; Principal component regression ; Random forest ; Mathematics ; Statistics ; Ecology ; Biology Spectroscopy and Chemometric Analyses ; Meat and Animal Product Quality ; Water Quality Monitoring and Analysis
Authors: Sandra Rodríguez-Blázquez | Esther Gómez-Mejía | Noelia Rosales‐Conrado | María Eugenia León‐González
Source: Foods (review, 2025-06-26)
Field: Chemistry — Organic Chemistry

Recent Insights into Eco-Friendly Extraction Techniques for Obtaining Bioactive Compounds from Fruit Seed Oils The valorization of agri-food waste has emerged as a global priority. In this context, fruit seed waste is being investigated for oil extraction due to its richness in bioactive compounds with remarkable health benefits. This review (2020-2025) focuses on the current state of eco-friendly extraction techniques for obtaining high-yield oils enriched with compounds such as tocopherols, polyphenols, fatty acids, phytosterols, and carotenoids. A comparison of the present method with conventional extraction techniques reveals several notable distinctions. Conventional methods are generally characterized by prolonged extraction times, elevated temperatures, and high amounts of solvents and/or energy. The findings of this review suggest that the extraction methodologies employed exerts a substantial influence on the yield and bioactive composition of the oil, which in turn affects its health-promoting properties. Furthermore, the results have demonstrated that alternative methodologies (microwave-assisted extraction, ultrasound-assisted extraction, pressurized liquid extraction, electric pulse extraction, enzyme-assisted extraction, subcritical extraction, and combinations thereof) have analogous oil yields in comparison with conventional methods. In addition, these oils present a superior bioactive profile with feasible potential in industrial and health applications. The novelty of this work lies in its emphasis on the valorization of fruit seed waste, as well as its sustainable approach. This sustainable approach utilizes experimental design strategies, the implementation of developments that employ comprehensive ecological metrics, and the latest trends in the application of artificial intelligence. Extraction (chemistry) ; Context (archaeology) ; Environmentally friendly ; Biochemical engineering ; Health benefits ; Polyphenol ; Pulp and paper industry ; Yield (engineering) ; Environmental science ; Process engineering ; Biotechnology ; Chemistry ; Materials science ; Engineering ; Chromatography ; Biology ; Organic chemistry ; Traditional medicine Edible Oils Quality and Analysis ; Phytochemicals and Antioxidant Activities ; Essential Oils and Antimicrobial Activity
Authors: Anna Gonzàlez‐Rosell | Vanessa Rück | Mikkel Baldtzer Liisberg | Cecilia Cerretani | Villads R. M. Nielsen | Tom Vosch | Stacy M. Copp
Source: Advanced Optical Materials (article, 2025-01-16)
Field: Materials Science — Materials Chemistry

A Dual‐Emissive DNA‐Templated Silver Nanocluster with Near‐Infrared I and II Emission Abstract DNA‐stabilized silver nanoclusters (DNA‐Ag N ) are atomically precise and sequence‐tuned nanomaterials with potential applications for deep tissue biomedical imaging. Here, a dual‐emissive DNA‐Ag N is presented with fluorescence in the first near‐infrared (NIR‐I) spectral window and microsecond‐lived photoluminescence in the second near‐infrared (NIR‐II) spectral window. High‐resolution electrospray ionization mass spectrometry showed that the emitter has the molecular formula (DNA) 2 [Ag 17 ] 11+ . The crystallization of (DNA) 2 [Ag 17 ] 11+ was unsuccessful, which prevented the use of X‐ray diffraction to determine its structure. However, sequence variations of the templating DNA oligomer provided insights into nucleobases that are critical for stabilizing the Ag 17 11+ . Moreover, addition of an adenosine or thymidine at the 5′‐end of the stabilizing DNA strand maintained the composition and photophysical properties of the (DNA) 2 [Ag 17 ] 11+ , suggesting a potential site for conjugation with biomolecules to enable targeted labeling in future bioimaging applications. Materials science ; Infrared ; Nanotechnology ; Dual (grammatical number) ; Optoelectronics ; Optics Nanocluster Synthesis and Applications ; Gold and Silver Nanoparticles Synthesis and Applications ; Advanced Nanomaterials in Catalysis
Authors: Yidan Wang | Jiazhu Sun | Jiuhong Yi | Ruijie Fu | Ben Liu | Yunlei Xianyu
Source: Advanced Science (article, 2025-01-15)
Field: Materials Science — Materials Chemistry

“Three‐in‐one” Analysis of Proteinuria for Disease Diagnosis through Multifunctional Nanoparticles and Machine Learning Urinalysis is one of the predominant tools for clinical testing owing to the abundant composition, sufficient volume, and non-invasive acquisition of urine. As a critical component of routine urinalysis, urine protein testing measures the levels and types of proteins, enabling the early diagnosis of diseases. Traditional methods require three separate steps including strip testing, protein/creatinine ratio measurement, and electrophoresis respectively to achieve qualitative, quantitative, and classification analyses of proteins in urine with long time and cumbersome operations. Herein, this work demonstrates a "three-in-one" protocol to analyze the urine composition by combining multifunctional nanoparticles with machine learning. This work constructs a sensor array to analyze proteinuria by employing nanoparticles with unique optical properties, outstanding catalytic activity, diverse composition, and tunable structure as probes. Different proteins interacted with nanoprobes differently and are classified by this sensor array based on their physicochemical heterogeneities. With the aid of machine learning, the urine composition is precisely detected for the diagnosis of bladder cancer. This protocol enables quantification and classification of 5 proteinuria in 10 min without any tedious pretreatment, showing proimise for the comprehensive analysis of body fluid for early disease diagnosis. Proteinuria ; Nanoparticle ; Disease ; Nanotechnology ; Computer science ; Materials science ; Medicine ; Internal medicine Advanced Nanomaterials in Catalysis ; Electrochemical sensors and biosensors ; Biosensors and Analytical Detection
Authors: Eva Kröll | Lara Devecioglu | Andrei N. Salak | Lukas Piotrowsky | Vladimir V. Shvartsman | Doru C. Lupascu
Source: Journal of the American Ceramic Society (article, 2025-02-06)
Field: Materials Science — Materials Chemistry

Weakly‐coupled barium titanate stannate‐based relaxors as energy storage materials Abstract Lead‐free relaxor ferroelectrics are promising materials for energy storage applications. To find new material systems for electrostatic capacitors, many researches have dedicated their work to formulate new ecologically safe compositions. In our study, we have prepared (1− x )Ba(Ti 0.93 Sn 0.07 )O 3 − x BiYO 3 (BTS7‐BY) solid solutions via the solid‐state method. The parent composition, Ba(Ti 0.93 Sn 0.07 )O 3 is a ferroelectric‐relaxor crossover material. It reduces its remanent polarization while still maintaining a high maximum polarization. Moderate substitution with BiYO 3 further lowers the remanent polarization, resulting in slimmer polarization loops and increasing the energy storage efficiency to 90 %. Samples with x ≥ 0.06 show a true relaxor behavior with linear‐like polarization loops with character of weakly‐coupled relaxors. Breakdown strength measurements reveal a reduced breakdown strength with increasing BY content. Further investigations of the electrostriction, the band gap, and the electrical microstructure link the homogeneity and grain size of the microstructure to the resulting breakdown strength. This highlights the importance of a homogenous and small‐grained microstructure for enhancing the energy storage properties. Samples with x = 0.06 exhibit the best energy storage properties with a recoverable energy density of 1.11 J/cm 3 at 189 kV/cm with an energy storage efficiency of 74%. Stannate ; Barium titanate ; Barium ; Materials science ; Titanate ; Energy storage ; Mineralogy ; Ceramic ; Chemistry ; Metallurgy ; Physics ; Thermodynamics Ferroelectric and Piezoelectric Materials ; Electronic and Structural Properties of Oxides ; Magnetic and transport properties of perovskites and related materials
Authors: Terence G. Langdon
Source: Journal of Materials Science (review, 2025-05-22)
Field: Materials Science — Materials Chemistry

Review: developments in the creep of materials over a period of more than a century Abstract Studies of creep may be traced back for more than 100 years and this extensive experimentation has produced a comprehensive understanding of the various flow mechanisms occurring in the steady-state or secondary stage of creep. These mechanisms range from diffusional creep and Harper-Dorn creep at low stresses to dislocation processes such as glide and climb at higher stresses and to grain boundary sliding where the rate is dependent primarily upon the grain size. This review examines the nature and characteristics of these flow processes and then demonstrates that the theoretical predictions are generally in good agreement with the experimental data. Finally, two examples are presented, in the fields of structural engineering and glaciology, to illustrate the potential for making significant new contributions to the understanding of the creep processes. Graphical abstract Creep ; Materials science ; Solid mechanics ; Period (music) ; Forensic engineering ; Composite material ; Metallurgy ; Engineering physics ; Engineering ; Art Microstructure and mechanical properties ; High Temperature Alloys and Creep ; Rock Mechanics and Modeling
Authors: Fei Tan | Xiuwen Gao | Hao Cang | Nianyi Wu | Ruoyu Di | Jing‐Kun Yan | Chengkai Li | Pan Gao | Xin Lv
Source: Agronomy (article, 2025-01-16)
Field: Chemistry — Analytical Chemistry

Early Detection of Verticillium Wilt in Cotton by Using Hyperspectral Imaging Combined with Recurrence Plots Cotton is susceptible to Verticillium wilt (VW) during its growth. Early and accurate detection of VW can facilitate targeted pesticide treatment and reduce the potential spread of the disease. However, accurately detecting VW in cotton before symptoms appear (the asymptomatic period) after infection by Verticillium dahliae remains challenging. This study proposes an early detection method for cotton wilt disease using hyperspectral imaging and recurrence plots (RP) combined with machine learning techniques. First, spectral curves were collected and analyzed under three conditions of cotton plants: healthy, asymptomatic, and symptomatic. Then, the one-dimensional spectral curve was transformed into two-dimensional recurrence plots to enhance the detail differences in the original spectral curve of cotton plants in various states. Hyperspectral recurrence plots contain rich texture information; fifteen texture features were extracted from the spectral recurrence plots using the Gray-Level Gradient Co-occurrence Matrix (GLGCM). Eleven of these texture features showed a strong correlation with the class labels of the cotton plants. In order to reduce redundant information between features, principal component analysis (PCA) was used to extract the first five principal components, which explained 99.02% of the information from the 11 features. The final principal component dataset was then input into KNN, SVM, ELM, and XGBoost classifiers to assess the accuracy of early detection of VW in cotton. The results showed that the XGBoost model, based on the first five principal components obtained from the texture features, achieved accuracy, precision, recall, and F1-score of 96.3%, 95.6%, 96%, and 95.8%, demonstrating a high classification capability. The results of this study confirm the feasibility of converting spectral curves into recurrence plots and extracting image texture features for the accurate identification of VW in cotton during the asymptomatic period. This method also provides a new strategy for early disease detection of cotton and other plants in the future. Hyperspectral imaging ; Verticillium wilt ; Agronomy ; Horticulture ; Biology ; Remote sensing ; Geography Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Remote Sensing in Agriculture
Authors: Lizhi Jiang | Javier Rodriguez-Sanchez | John L. Snider | Peng W. Chee | Longsheng Fu | Changying Li
Source: Plant Methods (article, 2025-05-20)
Field: Materials Science — Polymers and Plastics

Mapping of cotton bolls and branches with high-granularity through point cloud segmentation High resolution three-dimensional (3D) point clouds enable the mapping of cotton boll spatial distribution, aiding breeders in better understanding the correlation between boll positions on branches and overall yield and fiber quality. This study developed a segmentation workflow for point clouds of 18 cotton genotypes to map the spatial distribution of bolls on the plants. The data processing workflow includes two independent approaches to map the vertical and horizontal distribution of cotton bolls. The vertical distribution was mapped by segmenting bolls using PointNet++ and identifying individual instances through Euclidean clustering. For horizontal distribution, TreeQSM segmented the plant into the main stem and individual branches. PointNet++ and Euclidean clustering were then used to achieve cotton boll instance segmentation. The horizontal distribution was determined by calculating the Euclidean distance of each cotton boll relative to the main stem. Additionally, branch types were classified using point cloud meshing completion and the Dijkstra shortest path algorithm. The results highlight that the accuracy and mean intersection over union (mIoU) of the 2-class segmentation based on PointNet++ reached 0.954 and 0.896 on the whole plant dataset, and 0.968 and 0.897 on the branch dataset, respectively. The coefficient of determination (R<sup>2</sup>) for the boll counting was 0.99 with a root mean squared error (RMSE) of 5.4. For the first time, this study accomplished high-granularity spatial mapping of cotton bolls and branches, but directly predicting fiber quality from 3D point clouds remains a challenge. This method provides a promising tool for 3D cotton plant mapping of different genotypes, which potentially could accelerate plant physiological studies and breeding programs. Granularity ; Segmentation ; Point cloud ; Point (geometry) ; Cloud computing ; Computer science ; Mathematics ; Artificial intelligence ; Geometry Textile materials and evaluations ; 3D Shape Modeling and Analysis ; Remote Sensing and LiDAR Applications
Authors: Victor Dudarev | Lars Banko | Alfred Ludwig
Source: npj Computational Materials (article, 2025-05-03)
Field: Materials Science — Materials Chemistry

An extensible open-source solution for research digitalisation in materials science Abstract Information technology and data science development stimulate transformation in many fields of scientific knowledge. In recent years, a large number of specialised systems for information and knowledge management have been created in materials science. However, the development and deployment of open adaptive systems for research support in materials science based on the acquisition, storage, and processing of different types of information remains unsolved. We propose MatInf —an extensible, open-source solution for research digitalisation in materials science based on an adaptive, flexible information management system for heterogeneous data sources. MatInf can be easily adapted to any materials science laboratory and is especially useful for collaborative projects between several labs. As an example, we demonstrate its application in high-throughput experimentation. Open source ; Extensibility ; Open science ; Computer science ; World Wide Web ; Physics ; Programming language ; Software Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; Electron and X-Ray Spectroscopy Techniques