jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Hua Yin | Danying Lei | Anping Xiong | Lu Yuan | Minghui Chen | Yilu Xu | Yinglong Wang | Hui Xiao | Quan Wei
Source: Agronomy (article, 2026-01-04)
Field: Chemistry — Analytical Chemistry

A Pipeline for Mushroom Mass Estimation Based on Phenotypic Parameters: A Multiple Oudemansiella raphanipies Model Estimating the mass of Oudemansiella raphanipies quickly and accurately is indispensable in optimizing post-harvest packaging processes. Traditional methods typically involve manual grading followed by weighing with a balance, which is inefficient and labor-intensive. To address the challenges encountered in actual production scenarios, in this work, we developed a novel pipeline for estimating the mass of multiple Oudemansiella raphanipies. To achieve this goal, an enhanced deep learning (DL) algorithm for instance segmentation and a machine learning (ML) model for mass prediction were introduced. On one hand, to segment multiple samples in the same image, a novel instance segmentation network named FinePoint-ORSeg was applied to obtain the finer edges of samples, by integrating an edge attention module to improve the fineness of the edges. On the other hand, for individual samples, a novel cap–stem segmentation approach was applied and 18 phenotypic parameters were obtained. Furthermore, principal component analysis (PCA) was utilized to reduce the redundancy among features. Combining the two aspects mentioned above, the mass was computed by an exponential GPR model with seven principal components. In terms of segmentation performance, our model outperforms the original Mask R-CNN; the AP, AP50, AP75, and APs are improved by 2%, 0.7%, 1.9%, and 0.3%, respectively. Additionally, our model outperforms other networks such as YOLACT, SOLOV2, and Mask R-CNN with Swin. As for mass estimation, the results show that the average coefficient of variation (CV) of a single sample mass in different attitudes is 6.81%. Moreover, the average mean absolute percentage error (MAPE) for multiple samples is 8.53%. Overall, the experimental results indicate that the proposed method is time-saving, non-destructive, and accurate. This can provide a reference for research on post-harvest packaging technology for Oudemansiella raphanipies. Segmentation ; Principal component analysis ; Pipeline (software) ; Pattern recognition (psychology) ; Redundancy (engineering) ; Jaccard index ; Grading (engineering) Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Postharvest Quality and Shelf Life Management
Authors: MASAPOGU KEERTHI | P. YASHMEEN NIKHAT
Source: International Journal of Scientific Development and Research (article, 2026-01-01)
Field: Materials Science — Biomaterials

Recent Advances In Targeted Nanoparticle Based Drug Delivery System For Breast Cancer Therapy: A Comprehensive Review Breast cancer remains a leading cause of cancer-related deaths among women worldwide. Conventional treatments face challenges such as toxicity, low specificity, and drug resistance. Nanotechnology offers innovative solutions through targeted and controlled drug delivery. Recent advances focus on passive and active targeting strategies for enhanced efficacy. Different nano carriers—lipid-based, polymeric, inorganic, and hybrid—are being developed. Stimuli-responsive and multifunctional nanoparticle enable combined therapy and diagnosis. Key challenges include scalability, toxicity and regulatory hurdles in clinical translation. Future trends emphasize precision nano medicine to improve breast cancer treatment outcomes. Breast cancer ; Drug delivery ; Targeted drug delivery ; Drug ; Cancer ; Cancer therapy ; Precision medicine Nanoparticle-Based Drug Delivery ; Cancer Research and Treatment ; Biological Research and Disease Studies
Authors: Dmitry Korogod | Alexander V. Shapeev | Ivan S. Novikov
Source: arXiv (Cornell University) (article, 2026-03-06)
Field: Materials Science — Materials Chemistry

Long-range machine-learning potentials with environment-dependent charges enable predicting LO-TO splitting and dielectric constants We present two models with explicit long-range electrostatics in the form of Coulomb interactions. Both models include point charges depending on their local atomic environments, and the second model also conserves a total charge of an atomic system. We combine the proposed long-range models with local Moment Tensor Potential and demonstrate that they reduce the training errors of the MTP models fitted on the same training sets including the CH$_3$COO$^-$+4-methylphenol and CH$_3$COO$^-$+4-methylimidazole organic dimers (non-periodic systems) and the NaCl crystal (periodic system). For the organic dimers, the proposed models also give qualitatively correct predictions of the binding curves. Furthermore, in this study we introduce a method for calculating phonon spectra of isotropic materials only via these long-range models fitted to energies, forces, and stresses. The developed long-range model with point charges dependent on atomic environments and conserving total charge is capable of predicting the correct value of the LO-TO splitting in the $Γ$-point in the isotropic NaCl. For this system, we also predict dielectric constant from dipole moment fluctuations calculated with molecular dynamics simulations conducted with the developed long-range model. The calculated dielectric constant is in good agreement with experiment. Finally, we demonstrate the broader applicability of the introduced approach by computing the phonon spectrum of uniaxial tetragonal PbTiO$_3$. Although the method is formally derived for isotropic materials, we show that it is also perspective for uniaxial materials (e.g., PbTiO$_3$) as the spectrum obtained with our long-range interatomic potential corresponds to the one calculated with density functional theory. Isotropy ; Dielectric ; Dipole ; Phonon ; Moment (physics) ; Tensor (intrinsic definition) ; Charge (physics) ; Electrostatics ; Coulomb Machine Learning in Materials Science ; Advanced Physical and Chemical Molecular Interactions ; Material Dynamics and Properties
Authors: Abdirahman Ali Yussuf | Mohammad A. Al‐Saleh | Jacob Samuel | Rashed Al‐Zufairi | Tahani Al‐Shammari | A. Al-Banna
Source: Journal of Applied Polymer Science (article, 2026-02-08)
Field: Materials Science — Polymers and Plastics

The Role of the Graphene Nanoplatelets and Aluminum Hydroxide Reinforced With <scp>MMT</scp> Nanoclay on the Mechanical, Thermal, Flame Retardancy, and Rheological Properties of <scp>HDPE</scp> / <scp>LLDPE</scp> Blend Nanocomposite ABSTRACT A blend of high‐density polyethylene (HDPE) and linear low‐density polyethylene (LLDPE) was incorporated with different content of graphene nanoplatelets (GNP) and aluminum hydroxide (ATH) together reinforced with montmorillonite (MMT) and prepared through melt compounding. The influence of the GNP loading (2, 3, 4 phr) and ATH (10, 15, 20 wt%) filler content on the mechanical, thermal, flame retardancy, morphological, and rheological properties were studied and reported. For mechanical properties, a slight decrease in tensile strength was observed for all HDPE/LLDPE nanocomposite samples compared to the neat HDPE/LLDPE blend. However, a drastic drop in elongation at break was found for all HDPE/LLDPE nanocomposites compared to the neat HDPE/LLDPE blend sample; while the Young's modulus of those samples with higher GNP and ATH filler content was increased considerably compared to the neat HDPE/LLDPE sample. The thermogravimetric analysis (TGA) results have shown that the initial decomposition onset temperature was decreased for HDPE/LLDPE nanocomposite samples containing GNP and ATH compared to the neat HDPE/LLDPE sample. However, the maximum decomposition temperature increase was observed for all HDPE/LLDPE nanocomposite samples containing higher GNP and ATH filler loading together with MMT nanoclay. Furthermore, the differential scanning calorimetry (DSC) results have shown no significant changes in melting and crystallization temperatures for HDPE/LLDPE nanocomposite samples containing ATH and GNP compared to the neat HDPE/LLDPE blend. However, a decrease in percent crystallinity was observed for HDPE/LLDPE nanocomposite samples compared to the neat blend. Both the flammability test, UL‐94, and scanning electron microscope (SEM) results have revealed that the flame retardancy properties of neat HDPE/LLDPE blend have enhanced considerably due to the higher GNP and ATH filler content loading together with MMT nanoclay into the neat blend matrix. All rheological properties have shown a similar pattern of increase, at the lower frequency region, with increasing GNP and ATH filler loading content. X‐ray diffraction (XRD) results have shown the decreased 2‐theta and increased interlayer d spacing slightly for samples containing GNP and ATH fillers, which could be due to intercalated polymer nanocomposite. Nanocomposite ; Differential scanning calorimetry ; Thermogravimetric analysis ; Crystallinity ; Ultimate tensile strength ; Polyethylene ; Montmorillonite ; Crystallization ; Scanning electron microscope Flame retardant materials and properties ; Polymer crystallization and properties ; Polymer Nanocomposites and Properties
Authors: Demetrio Fuentes Hernández | Marissa VARGAS-RAMÍREZ | Felipe Legorreta García | Erick Uriel Morales Cruz | Edgar Arturo Chávez-Urbiola | César Yeshua Becerra-Mayorga
Source: Revista Mexicana de Física (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Optimizing energy conversion in a PV-TGS (mSi–Bi₂Te₃) using numerical simulation of semiconductors materials A numerical simulation model based on the energy transfer and conversion by the semiconductor materials of a Photovoltaic-Thermoelectric Generation System (PV-TGS) was developed to determine its theoretical power. The model yielded results that differed from those previously reported by other authors for PV panels and cells by a range of -0.017% to +1.74%. In thermoelectric production, the results deviated by a range of -2.27% to -2.80%. The model was formulated based on the constitutive equations of the phenomena inherent to the photovoltaic generation of a monocrystalline silicon PV panel (mSi) and the thermoelectric generation of bismuth telluride (Bi2Te3) Thermoelectric Generators (TEG). The primary variables of interest were irradiance (G), panel operating temperature (TPPV), and the cold section of the TEG (Tc), as well as the generated electrical power (P). The research outcomes enabled the identification of the optimal number of TEGs for the design of a PV-TGS. This determination was derived by considering the temperature differential between the TPPV and Tc sections, as well as the generation efficiency of PV-TGS as a function of incident radiation. Consequently, a pair of equations was formulated that establish a direct correlation between the thermoelectric generation rate and the desired output power. Thermoelectric generator ; Energy transformation ; Monocrystalline silicon ; Bismuth telluride ; Thermoelectric effect ; Electricity generation ; Semiconductor ; Energy conversion efficiency ; Photovoltaic system ; Thermoelectric cooling Advanced Thermoelectric Materials and Devices ; Solar Thermal and Photovoltaic Systems ; Chemical and Physical Properties of Materials
Authors: B. Szymański | Cina Fouroutan-Nejad
Source: ChemRxiv (article, 2026-02-09)
Field: Materials Science — Electronic, Optical and Magnetic Materials

H NMR Chemical Shift as an Index of UV-Vis Absorption/Emission Maxima in Aromatic Dyes We have demonstrated that experimental ¹H NMR chemical shifts can serve as direct indicators of absorption and emission maxima within families of structurally related molecules. Notably, predictions of optical maxima based on chemical shifts have proven to be more accurate than excitation energies obtained from time-dependent density functional theory employing long-range corrected functionals such as CAM-B3LYP. This proof of principle has first been established using experimental data for a series of pyrrolo[3,2-b]pyrroles, for which a linear correlation between the absorption maximum and the ¹H NMR chemical shift has been identified for the first time. The scope of the study has then been extended to diketopyrrolopyrroles, additional pyrrolo[3,2-b]pyrroles, and thieno[3,2-b]thiophenes using density functional theory calculations. Across all investigated compound families, the correlation has remained close to linear. We have shown that two key criteria must be fulfilled to preserve this linearity: steric similarity among the compared molecules and full π-conjugation of the molecular framework. These conditions arise from the dependence of magnetically induced ring-current strengths, i.e., the most influential factor on the ¹H NMR chemical shifts of conjugated molecules, on frontier-orbital energy gaps, together with symmetry selection rules formulated by Fowler and Steiner. These findings have provided new insight into the electronic structure of quadrupolar dyes and have established a practical framework for the rapid and efficient design of optoelectronic materials. Maxima ; Density functional theory ; Chemical shift ; Steric effects ; Absorption (acoustics) ; Molecule ; Carbon-13 NMR ; Series (stratigraphy) ; Symmetry (geometry) Nonlinear Optical Materials Research ; Synthesis and Properties of Aromatic Compounds ; Organic Light-Emitting Diodes Research
Authors: Niharika Tiwari Tiwari | Mohit Kumar Sahu Sahu | Gitanjali Kashyap Kashyap | Aakansha Pandey Pandey | Vinay Sagar Verma Verma
Source: Journal of Pharmaceutical Research and Integrated Medical Sciences (article, 2026-02-18)
Field: Materials Science — Biomaterials

Systematic Review of Smart Nanoplatforms in Liver, Breast, Kidney, and Brain Cancers: Targeted Delivery, Omics, and Therapy Response Background: Liver, breast, kidney, and brain cancers remain major contributors to global cancer morbidity and mortality. Conventional therapies are limited by systemic toxicity, drug resistance, and tumor heterogeneity. Smart nanoplatforms offer targeted delivery, controlled release, and theranostic capabilities to address these challenges. Objective: This systematic review evaluates the development and clinical translation of smart nanoplatforms between 2019 and 2024, focusing on their design, omics integration, therapy response, and clinical outcomes in liver, breast, kidney, and brain cancers. Methods: Studies published between 2019 and 2024 were systematically analyzed, encompassing preclinical research, clinical trials, and multi-omics-guided nanoparticle strategies. Nanoplatforms were categorized into lipid-based, polymeric, inorganic, and hybrid/bioinspired systems. The review highlights therapy response, biomarker monitoring, and adaptive approaches informed by omics data. Results: Lipid-based and polymeric nanoparticles demonstrated enhanced tumor targeting and reduced systemic toxicity. Inorganic and hybrid/bioinspired platforms enabled imaging-guided therapy and immune evasion. Integration of genomics, transcriptomics, proteomics, and metabolomics with AI-driven analytics facilitated personalized therapy and adaptive treatment strategies. Clinical trials reported improved patient tolerability, quality of life, and preliminary survival benefits, though translational barriers—including tumor heterogeneity, blood–brain barrier penetration, manufacturing, and regulatory hurdles—remain significant. Conclusions: Smart nanoplatforms represent a transformative approach to precision oncology. The combination of targeted delivery, multi-omics guidance, and AI-driven therapy optimization has the potential to enhance treatment efficacy and patient-specific outcomes. Future research should focus on scalable manufacturing, regulatory standardization, and integration of innovative trial designs to accelerate clinical adoption. Clinical trial ; Translational research ; Biomarker ; Cancer therapy ; Personalized medicine ; Precision medicine ; Targeted therapy Nanoparticle-Based Drug Delivery ; Nanoplatforms for cancer theranostics ; RNA Interference and Gene Delivery
Authors: Baojing Hu | Changjin Cai | Ke Li | Weifeng Lu
Source: RSC Advances (article, 2026-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Research on terahertz multitunable multifunctional absorbers based on graphene and indium antimonide This article proposes a dual-band terahertz multi-functional absorber based on graphene and indium antimonide (InSb), which can be electrically, thermally, and magnetically tuned. The results indicate that, in the absence of an external magnetic field, the absorber exhibits two distinct absorption peaks based on the bright-bright mode coupling of cross-shaped and circular InSb arrays, with an average absorption rate of 99.8%. Meanwhile, its physical absorption mechanism can be theoretically analyzed by the radiating two-oscillator (RTO) model and the distribution of electric field at absorption peaks. Secondly, when the external magnetic field is applied along the <i>X</i> direction, the absorption frequency and absorption rate of the absorber can be electrically tuned by changing the chemical potential of graphene, and thermally and magnetically controlled by changing the temperature of InSb and the magnitude of the external magnetic field. Afterwards, the effects of the length and width of the cross-shaped InSb array, the radius of the InSb circle, and the thickness of the dielectric layer on the absorption effect are discussed. Finally, further studies are conducted on the application prospects of the absorber as a refractive index sensor, temperature sensor, and magnetic field sensor. This work provides a theoretical basis for the design of multi-tuned absorbers and sensors. Indium antimonide ; Terahertz radiation ; Graphene ; Antimonide ; Indium ; Terahertz spectroscopy and technology Metamaterials and Metasurfaces Applications ; Thermal Radiation and Cooling Technologies ; Superconducting and THz Device Technology
Authors: Anne Rodallec | Randy Lee | J. Cao | Sophie Marolleau | Julien Nicolas | Sébastien Benzekry
Source: Advanced Healthcare Materials (article, 2026-03-17)
Field: Materials Science — Biomaterials

Model‐Driven Optimization of Subcutaneous Polymer Prodrugs Achieves Cancer Remission in Mice The limitations of chemotherapy (e.g., toxicities, limited efficacy) have led to the development of nanocarriers for drug delivery to improve pharmacokinetics (PK) and therapeutic outcomes. However, optimizing dosing regimens remains challenging. Moreover, since chemotherapy is mainly administered intravenously (IV), this results in patient discomfort and high treatment costs. To address these issues, we used PK/pharmacodynamics (PD) modeling and applied it to subcutaneously (SC) injectable water-soluble polymer prodrug based on paclitaxel (Ptx) and polyacrylamide (PAAm), synthesized by "drug-initiated" controlled radical polymerization. PK/PD studies were performed on MCF-7 tumor-bearing mice. The PK model was developed on IV Ptx and SC Ptx-PAAm data. The PD model was developed on control, IV Ptx, and SC Ptx-PAAm groups (15 mg/kg), and validated on an independent group (SC Ptx-PAAm at 60 mg/kg). A series of optimal dosing regimens identified in silico were then validated in vivo with excellent agreement. Among them, a dosing regimen combining a loading dose and daily injections achieved a 60% complete response rate without added toxicity, outperforming prior results. To our knowledge, this is the first validated PK/PD model for nanocarriers, offering a framework for more effective, cost-efficient, and ethically refined drug development. Prodrug ; Dosing ; Pharmacokinetics ; Paclitaxel ; Chemotherapy ; Nanocarriers ; Regimen Nanoparticle-Based Drug Delivery ; Hydrogels: synthesis, properties, applications ; Advanced Polymer Synthesis and Characterization
Authors: Yunguang Gao | Haohua Jia | Zhipeng Lei | Wenjie Zhang | Junqiang He
Source: IET Science Measurement & Technology (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Partial Discharge Pattern Recognition Based on Time‐Frequency Multi‐Scale Residual Attention Network ABSTRACT Current partial discharge (PD) recognition models are often constrained by limitations such as insufficient recognition accuracy, limited processing speed, and procedural complexity. To mitigate these limitations, time‐frequency multi‐scale residual attention network (TFMRAnet) is designed to analyse PD signal, which comprises a multi‐scale residual attention‐based adaptive denoising network, a frequency‐domain recognition network, and a decision fusion module based on Dempster–Shafer (D–S) evidence theory. Specifically, the multi‐scale residual attention adaptive denoising module is used to extract multi‐scale features by dilated convolutions of different scales and accelerate training convergence by residual connections. Moreover, a GAM attention mechanism and an adaptive soft‐thresholding function are used for denoising, which preserves PD information and amplifies cross‐dimensional global interactions, thereby improving the performance of the network model. In frequency domain recognition networks, frequency domain features are extracted by performing Fourier transforms on PD signals. The recognition results of the time‐domain and frequency‐domain models are deeply combined through the D‐S evidence theory to improve the multidimensional recognition capability for PD patterns. Finally, a PD experimental platform was built to create four representative PD fault models and generate PD datasets corresponding to distinct fault types. Results from comprehensive comparative and ablation experiments demonstrate that the proposed model achieves perfect recognition accuracy (100%) on the collected PD dataset and exhibits the fastest inference speed (0.643 ms). These findings underscore its significant potential for practical engineering applications. Residual ; Pattern recognition (psychology) ; Inference ; Noise reduction ; Convergence (economics) ; Partial discharge ; Feature (linguistics) ; Feature extraction ; Frequency domain High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Machine Fault Diagnosis Techniques