jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jubaira Mammoo | P. Malin Bruntha | A. K. Saha
Source:  (article, 2025-01-07)
Field: Chemistry — Analytical Chemistry

Detecting Pesticide Contamination in Fruits and Vegetables with Deep Learning Models Pesticides are frequently mixed with various perishable fruits and vegetables in the market. Dishonest vendors often use these harmful chemicals to artificially preserve fruits for extended periods. Such adulterants are pretty hard to detect they do not change the outer appearance of the fruit. Therefore, a more sensitive and robust detection technique needs to be invented to handle such an issue efficiently. Therefore, in order to overcome this challenge, we propose a comprehensive deep-based detecting learning framework for fruits. For this experiment, different varieties of apples were tested in both contaminated states. The experiment was tested fresh and chemically toxic compounds present in fruit evaluation on pre-trained deep models and introduced a novel model for the purpose, which was ChemNet architecture-based. Firstly, the proposed ChemNet-based model's effectiveness was to be evaluated against existing deep learning models. The comparison was done in terms of accuracy of predicting a substance as a toxic one. The precision was found to be extremely low according to the outputs for GoogleNet and VGG-16, whereas YOLOv8 achieved a preciser 89.45. Surprisingly, the ChemNet-based architecture presented obtained better results than all other architectures with an accuracy rate of 97. 36 % in. These ChemN et are a resilient andsolution for safety in the market. Contamination ; Pesticide ; Computer science ; Environmental science ; Biology ; Ecology Spectroscopy and Chemometric Analyses ; Pesticide Residue Analysis and Safety
Authors: R.B. Parsons | Suvrat Jain | Abu Reza Md. Towfiqul Islam | Mark R. Walluk | Michael Thurston
Source: Journal of Advanced Manufacturing and Processing (article, 2025-08-17)
Field: Materials Science — Polymers and Plastics

Contaminant Investigation and Pre‐Processing Opportunities for Textile‐To‐Textile Recycling ABSTRACT Millions of metric tons of textiles are landfilled or incinerated each year in the United States, with less than 1% of textiles recycled into new clothing or fabrics. To counter this trend, a growing number of companies and researchers are exploring how a circular economy can be applied to support textile‐to‐textile recycling. A significant barrier they face comes down to quickly and efficiently extracting pure feedstock material from post‐consumer garments that feature a mix of natural and synthetic fibers. Textile recyclers prefer pure feedstocks, as working with mixed sources typically means lower throughput, higher risk of equipment failure, and diminished business margins. To facilitate a circular economy for textiles, methods, and technologies are needed that can efficiently separate out materials and contaminants from end‐of‐life textiles to increase the flow of pure feedstocks to recyclers. This paper summarizes findings from interviews with a cross section of textile recyclers and from a review of literature to define basic feedstock requirements. In addition to our qualitative research, we deconstruct a bale of post‐consumer textiles and analyze them using computer‐vision imaging, Fourier transform infrared spectroscopy (FTIR), and machine learning. The resulting data are used to set system‐level design inputs for an automated contaminant removal system to process post‐consumer clothing into appropriate feedstocks for recycling. To set the system's levels for automated real‐time near‐infrared analysis, we identify the minimum percentage of primary material that any single garment in a load of used clothing must contain for the average of the full output stream to meet the target purity levels of recyclers. The envisioned automated system can also address undesirable trace materials that might contaminate the processed stream by using imaging cameras coupled with artificial intelligence to identify sections of clothing for de‐trimming. Proof‐of‐concept machine learning algorithms are evaluated to locate and identify trims or garment areas with hidden contaminant materials. Integrating these methods into automated textile cutting systems can provide a cost‐effective means for increasing feedstock purity from used clothing, which can advance circularity for textiles by helping recyclers to reach production volumes and quality targets that were not possible solely with manual dismantling operations. Textile ; Business ; Environmental science ; Waste management ; Pulp and paper industry ; Engineering ; Materials science ; Composite material Textile materials and evaluations ; Dyeing and Modifying Textile Fibers
Authors: Said Byadi | P. K. Hashim | Pavel Sidorov
Source: Journal of Cheminformatics (article, 2025-04-01)
Field: Materials Science — Materials Chemistry

Predictive modeling of visible-light azo-photoswitches’ properties using structural features In this manuscript we present the strategy for modeling photoswitch properties (maximum absorption wavelength and thermal half-life of photoisomers) of visible-light azo-photoswitches using structural data. We compile a comprehensive data set from literature sources and perform a rigorous benchmark to select the best feature type and modeling approach. The fragment counts have demonstrated the best performance in the benchmark for both properties. We validate the models in cross-validation and on an external set. The predictions of absorption wavelengths for this set are highly accurate; on the other hand, the model for thermal half-life is less reliable, likely due to the modest size of the data set related to half-life of photoisomers, although consensus modeling approach allows to improve the predictivity. We also provide an interpretation of the modeling results using ColorAtom approach and the insights into the chemical space covered by the data set.Scientific contribution The paper provides a machine learning approach based only on structural features to predict two important photoswitch properties. Unlike previous studies, we do not use any quantum chemical features which accelerates the modeling procedure, while the accuracy of models remains high. Moreover, the fragment counts offer unique approach to model interpretation that is useful for rational design of photoswitches with desired properties. Computer science ; Visible spectrum ; Materials science ; Optoelectronics Photochromic and Fluorescence Chemistry ; Photoreceptor and optogenetics research ; Supramolecular Self-Assembly in Materials
Authors: Chenglin Li | Sixing Chen | Chuan Liu | Hui‐Jiuan Chen | Songjia Han
Source: Electronics (article, 2025-03-04)
Field: Materials Science — Polymers and Plastics

A Flexible Multi-Ion Detection System Based on Organic Electrochemical Transistors for Physiological Monitoring The continuous and real-time monitoring of physiological indicators is essential for early disease detection, prevention, and clinical diagnosis. In response to the growing demand for precise physiological parameter assessment, this study presents a flexible, organic electrochemical transistor (OECT)-based multi-ion sensing system designed to monitor key electrolyte concentrations—sodium (Na+), potassium (K+), and calcium (Ca2+)—in human biofluids. The system features a highly adaptable sensor array with a detection range tailored to physiological conditions, ensuring high selectivity and stability in complex biological environments. Our sensor demonstrated a sensitivity exceeding 1 mA/decade. To enhance measurement accuracy and mitigate cross-interference among ions, we integrate advanced machine learning algorithms, which optimize signal processing and significantly improve the system’s reliability. Additionally, we have developed a fully integrated hardware–software platform comprising customized signal acquisition circuitry and dedicated data analysis software, specifically tailored for OECT-based sensing applications. This comprehensive framework not only refines real-time ion detection but also paves the way for the broader clinical translation of OECT technology. The proposed system holds great promise for real-time physiological monitoring and point-of-care diagnostics, offering a potential paradigm shift in non-invasive, on-demand health assessment. Electrochemistry ; Transistor ; Ion ; Materials science ; Nanotechnology ; Optoelectronics ; Computer science ; Chemistry ; Electrical engineering ; Electrode ; Engineering ; Voltage Conducting polymers and applications ; Analytical Chemistry and Sensors ; Electrochemical sensors and biosensors
Authors: Aref Sepehr | M. Zaborowicz | Carlo Gabardi | Nicola Gabardi | Elisa Biada | Marco Luzzini | Alessandro Zanchin | Lorenzo Guerrini
Source: Journal of Food Engineering (article, 2025-07-01)
Field: Chemistry — Analytical Chemistry

Machine learning approach to inline monitoring of apple puree consistency through process data and fruit characteristics Consistency is a key attribute for apple purée producers, since it affects the product quality and acceptability, and can be used to regulate the process settings. This study aims to build a statistical model to predict puree consistency using data collected from an industrial production line. The dataset includes 14 variables across 524 samples, comprising process parameters (e.g., pump performance, temperature) and characteristics of the apples. Three predictive models were developed incrementally. Model 1 relied on the mechanical energy conservation law to establish a relationship between a measured pressure drop and the consistency. In this study, we quantify consistency using the Bostwick flow distance, a widely adopted practical proxy for apparent viscosity in the food industry. Model 2 incorporated the machinery-related parameters, and Model 3 further integrated the characteristics of the apples to account for raw material variability. For models training and validation, generalized linear models (GLM), gradient boosting machines (GBM), and deep learning (DL) architectures were compared. The effect of the different input variables on the predictive performance was also assessed. In all models, pressure difference emerged as the most influential variable. Model 3, particularly with GBM, resulted in the highest predictive accuracy (R 2 = 0.78; MAPE = 9.2 %), demonstrating the importance of incorporating ripening information of the apples in the model. The model outperforms traditional laboratory-based viscosity measurements, which typically have greater variability. The model considers the interactions between processing conditions and raw material properties that influence puree rheology, enabling a real-time inline consistency monitoring of apple puree in industrial settings, offering manufacturers operational benefits. These include the potential for reducing manual testing time and decreasing production costs through minimized batch rejections, and the ability to implement timely process adjustments. These advantages collectively contribute to reduced product waste, enhanced quality control, and improved economic outcomes in commercial apple puree production. • Inline apple purée consistency prediction models developed. • Comparison of GLM, GBM, Deep Learning. • Pressure drop is the most influential variable. • Apple data improves prediction accuracy significantly. • Basis for real-time consistency monitoring established. Consistency (knowledge bases) ; Process (computing) ; Food science ; Artificial intelligence ; Computer science ; Chemistry Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Smart Agriculture and AI
Authors: Jiahao Xu | Zemin Ren | Yu Wang | Fufeng Liu | Wenjie Jing
Source: Biosensors and Bioelectronics (article, 2025-05-03)
Field: Materials Science — Materials Chemistry

Histidine-engineered Cu-BTC nanozyme with enhanced laccase-like activity combining the machine learning for precise recognition of Beta-lactam antibiotics Laccase ; Antibiotics ; Chemistry ; Histidine ; BETA (programming language) ; Beta-lactam ; Combinatorial chemistry ; Biochemistry ; Computer science ; Enzyme Advanced Nanomaterials in Catalysis ; Advanced biosensing and bioanalysis techniques ; Electrochemical sensors and biosensors
Authors: Ramasamy Selvi | Ramachandran Murugesan | Jacob Wekalao | Amuthakkannan Rajakannu
Source: Plasmonics (article, 2025-10-28)
Field: Materials Science — Materials Chemistry

Advanced MXene-Gold Hybrid Plasmonic Biosensor for Early Detection of Tuberculosis Biomarkers With Machine learning optimization Biosensor ; Plasmon ; Sensitivity (control systems) ; Mycobacterium tuberculosis ; Figure of merit ; Tuberculosis ; Refractive index MXene and MAX Phase Materials ; 2D Materials and Applications ; Gold and Silver Nanoparticles Synthesis and Applications
Authors: Qiang You | Yongzhi Zhang | Rui Xiong | Haijun Ruan
Source: Journal of The Electrochemical Society (article, 2025-02-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine Learning-Based In Situ Identification of the High-Dimensional Parameter Space for an Equivalent Circuit Model Accurate online identification of equivalent circuit model (ECM) parameters of lithium-ion battery electrochemical impedance spectroscopy (EIS) remains a challenge, particularly with high-dimensional parameter spaces. Here, 11-dimensional adapted Randles ECM (AR-ECM) is reduced to two low-dimensional models, and the EIS frequency ranges for each AR-ECM parameter were determined by distinguishing the frequency bands representing different electrochemical processes. A multi-step parameter in situ identification methodology was developed to minimize onboard training costs by selecting an optimal training set for machine learning based on the Euclidean distance between the collected and generated EIS data. A Gaussian process regression model was constructed by correlating the AR-ECM parameters and EIS to estimate the AR-ECM parameters. Model performance was validated using 12 cells at different temperatures. Experimental results show that a simulated database covering the main EIS and AR-ECM characteristics can be established, whose scale is on the order of 1e^6, much smaller than the order of 1e^11 resulting from each AR-ECM parameter being varied among 10 values. The estimation errors of the key AR-ECM parameters are approximately 5% at different temperatures. The maximum estimation error of all parameters is as low as 9.03%, 13.96% lower than that based on the complex nonlinear least squares method. In situ ; Identification (biology) ; Equivalent circuit ; Parameter space ; Space (punctuation) ; Computer science ; Materials science ; Biological system ; Physics ; Mathematics ; Electrical engineering ; Engineering ; Statistics Magnetic Properties and Applications
Authors: Wang Wang | Kun Song | Shuqi Zou | Qiangfei Xia | Xiaobai Li | Ke Sun | Hongwei Ma
Source: ACS Applied Nano Materials (article, 2025-05-14)
Field: Materials Science — Materials Chemistry

Long-Lifetime and Multicolor Room-Temperature Phosphorescence of Carbon Quantum Dots Derived from Biomass: Applications on Convolutional Neural Network Material longevity and diversity of a broad color gamut undoubtedly play a role as rigid criteria for dynamic information encryption technologies, particularly for those based on room-temperature phosphorescence (RTP). As a typical example, high-security multidimensional discriminative encryption still faces several obstacles brought about from the intrinsic limitations of carbon nitride quantum dots (CNQDs) which should be overcome, e.g. the quenching susceptibility of triplet excitons, limited tunability of colorimetric emission, and metal-doping-induced instability. To achieve tunable room-temperature phosphorescence (RTP) for advanced information encryption, biofriendly CNQDs were synthesized in this work, in terms of boron-mediated lattice defect engineering and hydrogen-bond rigidification, while dual-color RTP emissions were successfully realized with exceptional afterglow durations, lasting 7 s at 503 nm as well as 2 s at 617 nm. Based on emission mechanisms dominated by n-π* transitions, distinct phosphorescent lifetimes were detected from red and cyan-green CNQDs, within 176 and 290 ms, respectively, and then temporal determinants and Rubik’s Cube matrix transformations strategies were established following with their temporal-spectral characteristics and finally applied to convolutional neural networks (CNN). In fact, this work can be regarded as an attempt to provide a creative model for designing sustainable phosphorescent materials with programmable optical properties, devoted to promoting next-generation information encryption technologies. Phosphorescence ; Convolutional neural network ; Quantum dot ; Optoelectronics ; Materials science ; Computer science ; Physics ; Optics ; Artificial intelligence ; Fluorescence Carbon and Quantum Dots Applications ; Luminescence and Fluorescent Materials ; Advanced biosensing and bioanalysis techniques
Authors: Chenkai Gu | Yawei Gu | Rujing Hou | Yao Qin | Jing Zhong | Rongfei Zhou | Yichang Pan | Yiqun Fan | Weihong Xing
Source: Advanced Materials (article, 2025-08-04)
Field: Chemistry — Inorganic Chemistry

AI‐Powered Experimental Discovery of Metal‐Organic Frameworks for n/i‐Butane Separation There are significant challenges in developing efficient adsorbents as alternatives to the energy-intensive distillation processes for n/i-butane separation. Metal-organic frameworks (MOFs) hold great potential in addressing this issue. However, the vast diversity of MOFs makes the discovery of high-performance materials akin to searching for a needle in a haystack. Here, the high-throughput screening based on artificial intelligence (AI) is employed to accelerate the identification of MOFs for n/i-butane separation. An integrated descriptor system, accessible via both experiments and simulations, is proposed and broadly validated, demonstrating better performance over those widely-used descriptors. In addition, an optimization strategy for training dataset is proposed based on similarity, allowing for the efficient model training with only 10% samples from the entire database and thus significantly reducing the costs. Leveraging the integrated descriptors and optimization strategy, MOFs with exceptional n/i-butane separation performance are successfully identified through neural network model. As a proof of concept, SIFSIX-3-Zn is synthesized for validation because it has the largest n-butane capacity among top 20 MOFs. The SIFSIX-3-Zn demonstrates outstanding n/i-butane separation performance with nearly zero uptake of i-butane. This work introduces a novel research paradigm integrating AI, simulation and experiment, and presents an efficient process with broad applicability for material discovery. Butane ; Metal-organic framework ; Artificial neural network ; Haystack ; Materials science ; Computer science ; Process (computing) ; Artificial intelligence ; Nanotechnology ; Process engineering ; Biochemical engineering ; Adsorption ; Engineering ; Catalysis ; Chemistry ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Gas Sensing Nanomaterials and Sensors
Authors: Jinxin Wang | Yajie Dou | Jiahua Zhang | Mingyue Chen | Zhen Song | Quanlin Liu
Source: The Journal of Physical Chemistry C (article, 2025-10-07)
Field: Materials Science — Materials Chemistry

Deciphering the Key Factors Governing Mn<sup>4+</sup> Zero-Phonon Line Characteristics via Machine Learning Decoding of Host–Mn<sup>4+</sup> Interactions Achieving wide color gamut in liquid crystal displays relies critically on narrow-band red-emitting phosphors. Mn4+-activated phosphors are promising candidates due to their sharp emission, yet modulating their zero-phonon line wavelengths remains challenging. This study employs machine learning to decode host–Mn4+ interactions across 65 distinct hosts (42 fluorides, 6 fluoroxides, 17 oxides). By extracting 29 structural descriptors and leveraging a random forest regression model, we identify nine key features governing ZPL wavelengths. Electronegativity-related parameters dominate (77.83% cumulative importance), while geometric factors (bond angles, distances) also contribute significantly. The model achieves high accuracy (test MAE = 4.133 nm, R2 = 0.928), revealing that high electronegativity in secondary-coordination ions enhances Mn–ligand covalency, reducing the Eg → 4A2g transition energy and redshifting the emission peak wavelengths. This work identifies key design principles for Mn4+-activated fluoride, oxide, and oxyfluoride phosphors, enabling a targeted strategy for discovering next-generation narrow-band red emitters. Key (lock) ; Random forest ; Decoding methods ; Line (geometry) ; Gamut ; Bottleneck Machine Learning in Materials Science ; Luminescence Properties of Advanced Materials ; Semiconductor materials and devices
Authors: Yong Zhang | Wentao Li
Source: The Journal of Physical Chemistry A (article, 2025-03-08)
Field: Chemistry — Spectroscopy

Diabatic Potential Energy Surfaces of the H<sub>2</sub>S<sup>+</sup> System and the Dynamics Studies of the S<sup>+</sup> + H<sub>2</sub> (<i>v</i><sub>0</sub> = 2, <i>j</i><sub>0</sub> = 0) Reaction Global diabatic potential energy surfaces (PESs) of the H<sub>2</sub>S<sup>+</sup> system, corresponding to the 1<sup>4</sup>A″ and 2<sup>4</sup>A″ electronic states, were built by using the neural network method. In ab initio calculations, the aug-cc-pVTZ basis set and MRCI-F12 method were adopted. The topographic features of the new diabatic PESs were discussed and compared with the available theoretical and experimental results in detail. The spectroscopic parameters obtained from the diabatic PESs are in good agreement with previous theoretical and experimental results. Based on the newly constructed diabatic PESs, the nonadiabatic dynamics calculations of the S<sup>+</sup> + H<sub>2</sub>(<i>v</i><sub>0</sub> = 2, <i>j</i><sub>0</sub> = 0) → SH<sup>+</sup> + H reaction were carried out using the time-dependent wave packet method. To further understand the nonadiabatic effect, the adiabatic dynamical calculations of the title reaction were also performed based on the adiabatic PES, which was obtained by diagonalizing the diabatic PESs. The deviation between nonadiabatic results and adiabatic values is very obvious. In general, the adiabatic results underestimate the dynamics result at low collision energies and overestimate the dynamics results within high collision energy ranges. Therefore, to obtain accurate dynamics results, the nonadiabatic effect should be included in the calculation. Diabatic ; Physics ; Potential energy ; Energy (signal processing) ; Atomic physics ; Quantum mechanics ; Adiabatic process Spectroscopy and Laser Applications ; Advanced Chemical Physics Studies ; Atmospheric chemistry and aerosols