jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Smita Sisodiya | Mousumi Debnath | Devendra Jain | Surinder Singh Shekhawat
Source: Scientific Reports (article, 2025-05-28)
Field: Chemistry — Organic Chemistry

An integrated approach using morphological, biochemical, and RAPD markers to assess the genetic diversity of Olive (Olea Europaea L.) cultivars in India Olive cultivation in Rajasthan, India, has recently gained significant attention. To understand the genetic diversity among the cultivars of olive (Olea europaea L.) grown in Rajasthan, India, a cumulative approach using Random Amplified Polymorphic DNA (RAPD) markers was employed. This approach was based on morphological, biochemical, nutritional, and associated markers like antimicrobial and antioxidant potential linked to the functional activity of the primary and secondary metabolites. Eight primers (OPA-1, OPA2, OPA3, OPA4; OPB1, OPB2, OPB3, and OPB4) were used to differentiate the seven cultivars of olive. The results indicated 51 reproducible bands showing 45 polymorphism bands (88.23%). The disparity among the cultivars was evident, encompassing their morphological characteristics, biochemical features (protein, fat, carbohydrate, total phenolics, flavonoid, DPPH IC50, radical scavenging activity), and their inhibitory responses against different microorganisms. The genetic analysis using an unweighted pair group method with arithmetic averaging revealed diversity, and the dendrogram formed four distinct groups. These results demonstrate the molecular variance allowing the genetic diversity between the seven exotic cultivars imported from Israel. Considering the aforementioned phylogenetic study, this innovative method can potentially aid in understanding the future crop breeding initiatives in India and the improvement of olives. Olea ; RAPD ; Cultivar ; Genetic diversity ; Biology ; Botany ; Oleaceae ; Genetic marker ; Biotechnology ; Genetics ; Gene ; Medicine Edible Oils Quality and Analysis ; Essential Oils and Antimicrobial Activity ; Plant chemical constituents analysis
Authors: Shaohua Hu | Xuhong Zhang | Manli Wu | Zeliang Liu | Shiwei Tang | Shuifa Ke
Source: CLEAN - Soil Air Water (article, 2025-08-01)
Field: Materials Science — Biomaterials

Bioremediation of Petroleum‐Contaminated Soils With Pyrolysis Biochar: TPH Removal, Multifunctionality, and Microecological Restoration ABSTRACT This study evaluated the remediation effects of six biochars produced from spent mushroom substrate (SMS), wheat straw (WS), and cattle dung (CD) at pyrolysis temperatures of 300°C and 500°C on petroleum‐contaminated soil. As the pyrolysis temperature increased from 300°C to 500°C, the biochar yield decreased from 56.10%–64.81% to 30.63%–41.98%, whereas the pH value and available phosphorus content of biochars increased from 6.49–8.94 and 175.6–347.4 to 9.84–10.19 and 350.4–3660.8 mg/kg, respectively. Biochar amendments significantly enhanced total petroleum hydrocarbon (TPH) degradation, with the 300°C‐pyrolyzed CD biochar (CDA) showing the best performance (33.01% of TPH degradation rate, 76.43% higher than the control group, CK). Biochar application significantly enhanced soil multifunctionality (MF), with the highest improvement observed in CDA (MF = 0.79) and CDB (MF = 0.48) treatments, which were substantially higher than that of the control (CK, 0.28). Biolog EcoPlate analysis revealed that biochar treatments exhibited significantly higher microbial metabolic activity and diversity index and enhanced the utilization of carbohydrates, amines, and carboxylic acids. These findings indicated that agricultural waste‐derived biochars can effectively facilitate the ecological remediation of petroleum‐contaminated soils through synergistic improvements in soil physicochemical properties and microbial community functions, providing a feasible solution for agricultural waste recycling and contaminated soil remediation. Biochar ; Environmental remediation ; Bioremediation ; Pyrolysis ; Environmental chemistry ; Total petroleum hydrocarbon ; Chemistry ; Soil water ; Contamination ; Phosphorus ; Straw ; Soil contamination ; Waste management ; Pulp and paper industry ; Environmental science ; Biology ; Organic chemistry ; Ecology ; Soil science Clay minerals and soil interactions ; Coal and Its By-products ; Heavy metals in environment
Authors: Joe Stradling | Cássio Lima | Rudi Grosman | Igor Barsukov | Yun Xu | Ernest Teye | Christopher T. Elliott | Howbeer Muhamadali | Royston Goodacre
Source: npj Science of Food (article, 2025-06-05)
Field: Chemistry — Organic Chemistry

Raman on the palm: handheld Raman spectroscopy for enhanced traceability of palm oil Determining the geographic origin of palm oil in West Africa is vital for economic, environmental, and health reasons. It enhances traceability, protects local farmers, supports conservation by monitoring deforestation, and reduces food fraud, ensuring quality and regulatory compliance. Portable Raman spectroscopy offers a rapid method to identify the origin of palm oils from West Africa. Using principal component analysis (PCA), distinct clusters in scores plots were observed which reflected the geographic origin of the palm oils, with loadings from the first principal component (PC-1) highlighting β-carotene as a major source of variation among the samples. To quantify β-carotene content, a partial least squares regression (PLS-R) model was developed in coconut oil as the base oil as it is known to be β-carotene free. Once calibrated, PLS-R was used to rank the palm oil from West Africa based on their β-carotene levels. The resulting models in coconut oil demonstrated strong linearity and predictive performance, with R² and Q² values of 0.9848 and 0.9552, respectively, alongside low root mean square errors of cross-validation (0.1282 mM) and prediction (0.0747 mM); moreover, this model allows the palm oils to be ranked based on β-carotene content which was entirely reflective of the oils position in PC-1 from PCA. These findings underscore the potential of Raman spectroscopy as an effective tool for authenticating the geographic origin of palm oil from West Africa. Principal component analysis ; Partial least squares regression ; Traceability ; Palm ; Raman spectroscopy ; Palm oil ; Arecaceae ; Environmental science ; Food science ; Mathematics ; Agricultural engineering ; Statistics ; Chemistry ; Engineering Edible Oils Quality and Analysis ; Oil Palm Production and Sustainability ; Spectroscopy and Chemometric Analyses
Authors: Vincent Braun | Xintian Zhu | Carina Meyenberg | Volker Hahn | Hans Peter Maurer | Tobias Würschum | Patrick Thorwarth
Source: Plant Breeding (article, 2025-02-06)
Field: Chemistry — Analytical Chemistry

Phenomic Prediction Can Be Improved by Optimization of NIRS Preprocessing ABSTRACT In recent years, phenomic prediction has emerged as a new method in plant breeding that has been shown to have great potential. However, there are still many open questions regarding its practical application. For example, in the field of spectroscopy, it is standard practice to optimize the preprocessing of spectra, which so far has only been done to a limited extent for phenomic prediction. In this study, we therefore used three different data sets of soybean, triticale and maize to identify the best combinations of Savitzky–Golay filter parameters for preprocessing near‐infrared spectra for phenomic prediction. We tested 677 combinations of polynomial order, derivative and window size and evaluated them with Monte Carlo cross‐validation. Our results showed that the predictive ability can be improved with the right settings. However, there was no global optimum that gave the best results for all data sets. Even for different traits within the same data set, different combinations of parameters were necessary to achieve the highest predictive ability. Nevertheless, we show that some combinations generally result in a very low predictive ability and should not be used for preprocessing. In addition, we used the normalized discounted cumulative gain to assess whether preprocessing affected the ranking of individuals, which revealed no major changes in the top 1%, 10% or 20% of predicted individuals. Taken together, our results show the potential of preprocessing near‐infrared spectroscopy data to improve the phenomic predictive ability, but there appears to be no global optimum of parameter settings across data sets and traits. Preprocessor ; Computer science ; Ranking (information retrieval) ; Data set ; Set (abstract data type) ; Data pre-processing ; Statistics ; Data mining ; Mathematics ; Artificial intelligence Spectroscopy and Chemometric Analyses ; Remote Sensing in Agriculture ; Genetics and Plant Breeding
Authors: Ziyue Wang | Vadim Farztdinov | Ludwig Sinn | Pinkus Tober-Lau | Daniela Ludwig | Anja Freiwald | Fatma Amari | Kathrin Textoris-Taube | Agathe Niewienda | Anna Sophie Welter | Alan An Jung Wei | Luise Luckau | Florian Kurth | Matthias Selbach | Johannes Hartl | Michael Mülleder | Markus Ralser
Source: Nature Communications (article, 2025-12-22)
Field: Chemistry — Spectroscopy

Cross-platform clinical proteomics using the Charité open standard for plasma proteomics (OSPP) The role of plasma and serum proteomics in characterizing human disease, identifying biomarkers, and advancing diagnostic technologies is rapidly increasing. However, there is an ongoing need to improve proteomic workflows in terms of accuracy, reproducibility, and cost-effectiveness, and to achieve cross-platform transferability. Based on large serum and plasma proteome studies, we generate the Charité Open Peptide Standard for Plasma Proteomics (OSPP), an open, versatile peptide internal standard for targeted and untargeted mass spectrometry-based proteomic studies. The OSPP includes 211 concentration-matched stable-isotope-labeled peptides selected for consistent quantification across a large number of plasma and serum proteome studies, and synthetic accessibility. We show they are consistently quantified across serum and EDTA, citrate, and heparin plasma using multiple LC-MS platforms. Despite being selected for technical parameters, the OSPP peptides represent proteins that function in a wide range of biological processes, are used in routine clinical tests, or are targets of FDA-approved drugs, making OSPP able to serve as an expandable clinical marker panel. We demonstrate the utility of OSPP in a COVID-19 inpatient cohort study for improving analytical performances, for cross-platform alignment of proteomic data, disease stratification, and biomarker discovery. Proteomics ; Proteome ; Biomarker ; Human plasma ; Blood proteins ; Biomarker discovery ; Peptide Advanced Proteomics Techniques and Applications ; vaccines and immunoinformatics approaches ; Advanced Biosensing Techniques and Applications
Authors: Nonia Vaquero-Sabater | Abel Carreras | David Casanova
Source: Journal of Chemical Theory and Computation (article, 2025-09-06)
Field: Materials Science — Materials Chemistry

Pruned-ADAPT-VQE: Compacting Molecular Ansätze by Removing Irrelevant Operators The adaptive derivative-assembled problem-tailored variational quantum eigensolver (ADAPT-VQE) is one of the most widely used algorithms for electronic structure calculations in quantum computers. It adaptively selects operators based on their gradient, constructing ansätze that continuously evolve to match the energy landscape, helping avoid local traps and barren plateaus. However, this flexibility in reoptimization can lead to the inclusion of redundant or inefficient operators that have almost zero parameter value, barely contributing to the ansatz. We identify three phenomena responsible for the appearance of these operators: poor operator selection, operator reordering, and fading operators. In this work, we propose an automated, cost-free refinement method that removes unnecessary operators from the ansatz without disrupting convergence. Our approach evaluates each operator after ADAPT-VQE optimization by using a function that considers both its parameter value and position in the ansatz, striking a balance between eliminating low-coefficient operators while preserving the natural reduction of coefficients as the ansatz grows. Additionally, a dynamic threshold based on the parameters of recent operators enables efficient convergence. We apply this method to several molecular systems and find that it reduces ansatz size and accelerates convergence, particularly in cases with flat energy landscapes. The refinement process incurs, at most, a small additional computational cost and consistently improves or maintains ADAPT-VQE performance. Computer science ; Nanotechnology ; Chemistry ; Materials science Machine Learning in Materials Science ; Quantum Computing Algorithms and Architecture ; Computational Drug Discovery Methods
Authors: Mayank Vashishtha | K. Vasanth Kumar
Source: CrystEngComm (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Nucleation rate and Gibbs free energy of nucleation of APIs, large molecule, amino acid and inorganic materials in solution at different cooling rates A new model successfully predicts the nucleation rate MSZW obtained at different cooling rates. Nucleation ; Gibbs free energy ; Molecule ; Thermodynamics ; Materials science ; Physical chemistry ; Chemistry ; Chemical physics ; Organic chemistry ; Physics Crystallization and Solubility Studies ; nanoparticles nucleation surface interactions ; Chemical Thermodynamics and Molecular Structure
Authors: Lorenza Tuccio | Sonia Cacini | Giulia Arati | Carmelo Distefano | Silvia Traversari | Giacomo Fontanelli | Gina Rosalinda De Nicola | Paolo Matteini
Source: Frontiers in Plant Science (article, 2025-07-18)
Field: Chemistry — Analytical Chemistry

Integrating Raman spectroscopy and optical meters for nitrogen management in broccoli seedlings Raman spectroscopy enables non-destructive detection of nitrates and other nitrogen-related biochemical markers, including chlorophyll and polyphenols, with unparalleled specificity and sensitivity. Integrating Raman spectroscopy with proximal optical sensors, such as Dualex (Dx) and Multiplex (Mx), offers a transformative approach to precision nitrogen management in broccoli seedlings, complementing their ability to rapidly estimate nitrogen balance indices and key vegetation compounds. The integration demonstrated strong correlations between Raman spectral bands, optical indices, and biochemical parameters across varying nitrogen levels, enhancing the precision of nitrogen status assessment, resulting in a robust, scalable, and information-rich system. By combining molecular-level detail with practical field applications, this hybrid strategy represents a significant advancement in sustainable agriculture. Future research will explore the applicability of this integrated methodology to other plant species. Raman spectroscopy ; Nitrogen ; Chlorophyll ; Precision agriculture ; Environmental science ; Spectroscopy ; Remote sensing ; Computer science ; Chemistry ; Biological system ; Materials science ; Environmental chemistry ; Agriculture ; Botany ; Optics ; Biology ; Physics ; Ecology Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Water Quality Monitoring and Analysis
Authors: André Bülau | Daniela Walter | Karl-Peter Fritz
Source: Magnetism (article, 2025-08-01)
Field: Materials Science — Materials Chemistry

Components for an Inexpensive CW-ODMR NV-Based Magnetometer Quantum sensing based on NV-centers in diamonds has been demonstrated many times in multiple publications. The majority of publications use lasers in free space or lasers with fiber optics, expensive optical components such as dichroic mirrors, or beam splitters with dichroic filters and expensive detectors, such as Avalanche photodiodes or single photon detectors, overall, leading to custom and expensive setups. In order to provide an inexpensive NV-based magnetometer setup for educational use in schools, to teach the three topics, fluorescence, optically detected magnetic resonance, and Zeeman splitting, inexpensive, miniaturized, off-the-shelf components with high reliability have to be used. The cheaper such a setup, the more setups a school can afford. Hence, in this work, we investigated LEDs as light sources, considered different diamonds for our setup, tested different color filters, proposed an inexpensive microwave resonator, and used a cheap photodiode with an appropriate transimpedance amplifier as the basis for our quantum magnetometer. As a result, we identified cheap and functional components and present a setup and show that it can demonstrate the three topics mentioned at a hardware cost &lt;EUR 100. Magnetometer ; Physics ; Medicine ; Optics ; Materials science ; Magnetic field ; Quantum mechanics Diamond and Carbon-based Materials Research ; Mechanical and Optical Resonators ; Carbon Nanotubes in Composites
Authors: Ved Parkash | John L. Snider | Christopher M. Montes | Elizabeth A. Ainsworth | Álvaro Sanz‐Sáez
Source: The Plant Phenome Journal (article, 2025-09-25)
Field: Chemistry — Analytical Chemistry

Hyperspectral reflectance‐based partial least squares regression models for predicting cotton leaf physiological traits Abstract Alterations in the mechanistic drivers of photosynthesis have the potential to improve crop productivity, but their measurement is inherently time‐consuming using traditional methods. High‐throughput approaches to estimate photosynthesis using hyperspectral reflectance could be developed by leveraging variation in cotton ( Gossypium hirsutum L.) leaf traits generated through nitrogen (N) management, synthetic growth regulation strategies, and leaf position within the canopy. Currently, no such models exist for cotton, and interactions among the aforementioned factors are relatively unexplored for cotton leaf traits. This study aimed to (1) evaluate the effects of N application rate, mepiquat chloride (MC) management, and leaf position within the canopy on photosynthesis and its components, and (2) develop and validate hyperspectral reflectance‐based partial least squares regression (PLSR) models for predicting cotton leaf physiological traits. N rate and leaf position interacted to affect net photosynthetic rate ( A N ), electron transport rate, chlorophyll a, and chlorophyll b, while MC influenced only leaf pigments and specific leaf weight (SLW). A N reductions under N deficiency were driven by declines in the maximum rate of Rubisco carboxylation ( V c,max ) and ribulose‐1,5‐bisphosphate regeneration ( J max ), whereas high N had no effect on A N . PLSR models exhibited good to high predictive accuracy, with R 2 values ranging from 0.62 to 0.87 for most traits, except for SLW. These findings enhance our understanding of the physiological responses to N rate, MC strategy, and leaf position and highlight the potential of hyperspectral reflectance‐based PLSR as a high‐throughput tool for predicting leaf physiological traits to improve photosynthetic efficiency in cotton. Hyperspectral imaging ; Partial least squares regression ; Photosynthesis ; Canopy ; Chlorophyll ; Leaf area index ; Specific leaf area ; Photosynthetic pigment ; RuBisCO Spectroscopy and Chemometric Analyses ; Leaf Properties and Growth Measurement ; Remote Sensing in Agriculture
Authors: Maxime Louzon | Thomas Bertoncini | Noah Casañas | Yves Perrette | Gaël Plassart | Marine Quiers | Tanguy Wallet | Mohamed Kamel | Lotfi Aleya
Source: Biosensors (article, 2025-07-14)
Field: Chemistry — Spectroscopy

Laser-Induced Solid-Phase UV Fluorescence Spectroscopy for Rapid Detection of Polycyclic Aromatic Hydrocarbons in the Land Snail Bioindicator, Cantareus aspersus In ecotoxicological risk assessment, current methods for measuring the transfer and bioavailability of organic pollutants like polycyclic aromatic hydrocarbons (PAHs) in bioindicators are often destructive and environmentally unfriendly. These limitations are especially problematic when only small amounts of biological material are available. Here, we present a novel, high-throughput method combining laser-induced UV fluorescence spectroscopy (UV-LIF) and solid-phase spectroscopy (SPS) for rapid, in situ quantification of PAHs in land snails-a key bioindicator species. Using dual excitation wavelengths (266 nm and 355 nm), our method reliably detected pyrene and fluoranthene in snails exposed to varying concentrations, demonstrating clear dose-responses and inter-individual differences in bioaccumulation. The analysis time per sample was under four minutes. This approach allows simultaneous measurement of internal contaminant levels and health biomarkers in individual organisms and aligns with green chemistry principles. These findings establish a new, scalable tool for routine assessment of PAH transfer and bioavailability in diverse ecosystems. Bioindicator ; Fluorescence ; Environmental chemistry ; Fluorescence spectroscopy ; Land snail ; Snail ; Spectroscopy ; Chemistry ; Photochemistry ; Biology ; Ecology ; Optics ; Physics Mass Spectrometry Techniques and Applications
Authors: Amun Jarzembski | Siddharth Nair | Wyatt Hodges | Matthew Jordan | Anthony E. McDonald | Logan Antiporda | Greg Pickrell | Timothy Walsh | Fabio Semperlotti | Jason C. Neely | Luke Yates
Source: Advanced Materials Interfaces (article, 2025-04-10)
Field: Materials Science — Materials Chemistry

Wide‐Field Bond Quality Evaluation Using Frequency Domain Thermoreflectance with Deep Neural Network Feature Reconstruction Abstract Heterogeneous integration of microelectronic components provides a pathway to improve circuit/component performance; however, this comes with assembly challenges, in particular due to complex interfaces via subsurface bump bonds. The ability of these bonds to transmit electrical signals and conduct heat to the carrier substrate limits component performance. In this work, hyperspectral frequency‐domain thermoreflectance (FDTR) imaging is demonstrated as a robust technique for evaluating the quality of subsurface indium bump bonds in a surrogate microelectronic sample. By performing microscale FDTR imaging with coarse motion image stitching, thermal phase maps that cover a 4 mm by 4 mm field‐of‐view with subsurface feature sensitivity at depths greater than 50 µm are obtained. The resulting FDTR hyperspectral data contains more than three million pixels and reveal the quality of subsurface microbump arrays. Wide‐field analysis of bonded versus gap regions is enabled by deep neural network feature reconstruction, that after training, rapidly provides an interpretable representation of bond quality. Utility of noisy higher frequency FDTR phase maps, i.e., near the computationally predicted sensing depth limit, results in an average prediction error of 11%. Taken together, FDTR with neural network‐based analysis demonstrates subsurface bond monitoring at length scales relevant for heterogeneously integrated microelectronics. Materials science ; Artificial neural network ; Feature (linguistics) ; Field (mathematics) ; Frequency domain ; Quality (philosophy) ; Domain (mathematical analysis) ; Deep neural networks ; Artificial intelligence ; Pattern recognition (psychology) ; Computer science ; Computer vision Thermal properties of materials ; Electronic Packaging and Soldering Technologies ; 3D IC and TSV technologies