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Assessment of microfungal contamination and enzymatic activity in ethnographic textile artifacts and museum environment

Ethnographic textile artifacts are highly susceptible to fungal biodeterioration due to their organic composition and continuous exposure to microfungi in museum environments. This study aimed to assess the extent of microfungal contamination in the exhibition and storage areas of the Ege University Ethnography Museum and to evaluate the enzymatic activities (cellulase and protease) of the isolated fungal species to determine their biodeterioration potential. Air and surface samples were collected from display halls, storage rooms, and outdoor reference points during two seasons (spring and autumn) using a portable air sampler on DG-18, PCA, and MEA media. Fungal isolates were identified through macroscopic and microscopic examination supported by standard mycological keys, and their enzymatic activity was assessed using CMC agar for cellulase and skim milk agar for protease production. Microclimatic influences and seasonal differences were statistically evaluated using a two-sample independent t-test. Fungal load ranged from 120–450 CFU/m³ on DG-18 and 300–1000 CFU/m³ on PCA, with the highest values recorded inside display cases and storage zones. A total of 58 fungal isolates were obtained, predominantly belonging to Aspergillus, Penicillium , Cladosporium, Alternaria , and Rhizopus . Enzymatic assays showed that several isolates exhibited strong cellulase and protease activities, particularly A. sydowii, P. citrinum, A. flavus , and P. chrysogenum , indicating a high biodeterioration risk for cellulose- and protein-based textiles. Seasonal differences were statistically insignificant, highlighting the greater importance of microclimatic conditions and ventilation patterns. These findings underscore the need for integrated biological risk management and continuous microbial monitoring to protect ethnographic textile heritage from fungal deterioration.

A multifidelity Monte Carlo approach for simulating the diffusion coefficient of water. I. Forward problem

A multifidelity Monte Carlo framework for molecular dynamics simulations of the diffusion coefficient of liquid water is presented. The model hierarchy is constructed based on the size of the simulation box, taking advantage of the well-known size effects that simulations of the diffusion coefficient suffer from. Water is represented as a rigid three-point model, and uncertainties in the non-bonded force field parameters, i.e., the Lennard-Jones parameters and partial charges, are propagated forward through the simulations. A large-scale study exploring the capabilities of the multifidelity Monte Carlo framework is conducted, involving six computational models, three different calibrations, cubic and tetragonal simulation box shapes, and two sets of perturbed force field parameters. Results reveal that significant computational speed-ups of up to one order of magnitude can be achieved if successive models have small differences in correlations to the high-fidelity model. It is also found that the combination of high- and low-fidelity models provides a computational speed-up compared to exclusively relying on a single high-fidelity model in almost all cases.

Placebo and nocebo in clinical practice: An online cross-sectional survey of healthcare professionals from European countries on views, practices and training needs

Background Placebo and nocebo effects significantly influence health outcomes, yet healthcare professionals receive limited training and guidance on their mechanisms and clinical application, creating a gap in education and practical understanding. Conducted within the European PANACEA Consortium, this study evaluated healthcare professionals’ knowledge, attitudes, and practices regarding placebo and nocebo effects, and assessed their needs in further education. Methods An online cross-sectional survey among a European multi-country convenience sample of healthcare professionals collected data assessing participants’ knowledge, perceptions, and experiences regarding placebo and nocebo effects; their application and ethical considerations in clinical practice; and investigated educational needs and interest in further training. Quantitative data were analyzed using descriptive statistics, and thematic analysis was applied to the free-text responses. Results Amongst 807 participants, 71.7% reported taking advantage of placebo effects in their practice, and over half of participants (55.8%) observing nocebo effects. Participants reported feeling somewhat confident (53.3%) in harnessing placebo effects with 47.5% feeling confident in preventing nocebo effects. The majority of respondents had not received formal training on placebo and nocebo effects, with most expressing an interest in further training in areas such as healthcare education, emphasizing communication skills to enhance placebo effects, and knowledge to recognize and reduce nocebo effects. Conclusions There is a significant need for more comprehensive training on placebo and nocebo effects, particularly in early health professional education. These findings informed the development of educational resources and best practice recommendations developed as part of the outcomes from the PANACEA Consortium, improving the understanding and application of these effects among healthcare professionals across Europe.

A machine learning and explainable AI framework for adsorption energy prediction via effective representation of the adsorbate chemical environment

Adsorption energies, which capture the interactions between adsorbates and solid surfaces, are central to heterogeneous catalysis. Machine learning (ML) offers a powerful approach for rapidly and accurately predicting adsorption energies from computational data, thereby accelerating catalyst screening. The effectiveness of ML models depends on accurately representing the chemical environments of atoms, incorporating both geometric and electronic properties that influence adsorbate–surface interactions. In this study, we present an ML framework that leverages advanced electronic structure descriptors via Gaussian Multipole (GMP) featurization. GMP approximates electron density using Gaussian basis functions, providing a novel representation of elemental identity. Combined with robust geometric features, our model predicts CO and H binding energies (ΔECO∗ and ΔEH∗) on multimetallic alloys, achieving mean absolute errors of 0.07 eV for ΔECO∗ and 0.06 eV for ΔEH∗. To interpret the model’s predictions, we applied Shapley additive explanations, a post hoc explainable artificial intelligence (XAI) method. The analysis revealed that GMP features associated with adsorbates and their first-nearest neighbors (FNNs) played the most significant role in determining binding energies, while features from second-nearest neighbors had minimal influence. In addition, broader elemental properties such as boiling point, group number, and atomic number were found to be more predictive of adsorption behavior than conventional features, such as electronegativity. Clustering and t-SNE analyses showed that similar FNN environments yield consistent binding energies, supporting the model’s ability to generalize. Overall, this work demonstrates that integrating electronic structure features with explainable AI improves both predictive accuracy and interpretability, offering a powerful strategy for accelerated catalyst screening and rational catalyst design.

Integrating text mining and knowledge graph to enhance biopharmaceutical process optimization

To guarantee consistent quality of therapeutic proteins, the relationship between manufacturing process parameters and glycosylation profiles must be investigated and understood. The most important manufacturing step to investigate is the cell culture unit operation, where glycoprotein structure is highly dependent on raw materials, cell line genetics, and process control ranges. Because of the critical role glycosylation plays in certain drug mechanisms of action, the relationship between specific process inputs and glycosylation have been documented extensively. However, despite the extensive body of published work, general relationships between different cell culture conditions and glycosylation profiles remain fragmented across diverse studies, hindering systematic analysis and data-driven decision-making. To better elucidate these general relationships from published research, we introduce an innovative framework that leverages text mining and knowledge graph technologies to automatically extract, integrate, and visualize complex relationships from scientific literature, enabling actionable insights for biopharmaceutical process (bioprocess) development. Our methodology centers on the design and development of a specialized text-mining pipeline to extract and quantify relationships between cell culture conditions (raw materials, cell line genetics, and process control ranges) and glycosylation profiles from unstructured scientific literature. To enhance precision, we implement a dual normalization strategy: 1) dictionary-based concept standardization to reconcile term variants, and 2) ontological classification to organize entities into hierarchically structured categories. These curated relationships are then systematically integrated into a knowledge graph, which not only captures direct parameter-outcome associations but also reveals higher-order indirect connection through graph, providing a comprehensive view of bioprocess interactions. We present an intuitive web-based interface that enables researchers to dynamically explore and visualize complex bioprocess relationships through interactive queries. The system demonstrates robust performance with an 88% F1-score in relation extraction, effectively revealing hidden relationships between process parameters and glycan attributes. By combining scalable knowledge graph technology with interpretable analytics, our solution empowers pharmaceutical researchers to optimize therapeutic glycan profiles and accelerate manufacturing process development. This advancement represents a significant step forward in data-driven bioprocess optimization.

Ultrafast x-ray scattering of photodissociation dynamics in 2-iodothiophene

A time-resolved x-ray scattering (TRXS) investigation of the photodissociation dynamics of gas-phase 2-iodothiophene molecules following 252 nm excitation is presented. Structural evolution of the molecule and dynamical information on the resulting photofragments were captured using femtosecond x-ray free-electron laser pulses. Two dissociation pathways were identified, arising via excitation to ππ* and (n/π)σ* states, respectively, yielding distinct interfragment recoil velocities of ∼6.4 Åps−1 and 17.0 Åps−1. A comparison of asymptotic scattering data with simulated patterns indicates that the thiophene ring remains closed following dissociation at this wavelength. Modeling the experimental data yields a branching ratio of ∼3:1 in favor of the high velocity channel. These findings demonstrate the capability of TRXS to disentangle concurrent ultrafast reaction pathways and provide detailed structural insight into energy redistribution during photoinduced bond fission in complex molecular systems.

Association of vaginal IL-4, IL-6, IL-8, IL-17, IFN-γ, and dietary intake with IBD status and vaginal microbiota in pregnant individuals

Background Pregnant individuals with inflammatory bowel diseases (IBD) exhibit gut inflammation and dysbiosis; however, there is limited knowledge about their vaginal environment. This is important as vaginal inflammation and high vaginal microbiota diversity are associated with adverse pregnancy outcomes. Objectives We aimed to compare vaginal inflammatory markers and microbiota diversity of pregnant individuals with and without IBD in their third trimester of pregnancy and determine the role of diet in the vaginal microbiota diversity. Methods We recruited pregnant individuals who provided vaginal swabs at 27–29 weeks of pregnancy. We characterized the vaginal microbiota by sequencing the V3-V4 region of the 16S rRNA and surveyed nine key pro and anti-inflammatory cytokines by qRT-PCR from the vaginal mucosa. Participants completed three validated interviewer-led nutrition assessments of 24-hour dietary intake around the same time as the collection of vaginal samples. The nutritional assessments were used to estimate dietary quality using the validated Healthy Eating Index (HEI-2015). Results The cohort included 23 pregnant individuals with IBD (18 with Crohn’s disease and 5 with ulcerative colitis) and 25 healthy controls (HC); 56.5% of the IBD cases were in remission. Vaginal microbiota diversity and composition did not differ significantly between individuals with IBD and HC. However, the vaginal mucosa of the IBD individuals showed increased expression of Th17 pro-inflammatory cytokines (i.e., IL-6, IL-8, IL-17) and decreased expression of Th1 (IFN-γ) and Th2 (IL-4) compared to HC. Expression of IL-6 and TNF- α correlated positively with vaginal microbial diversity. The beneficial Lactobacillus crispatus dominated the vaginal microbiota of individuals with either high dietary quality or those consuming more vegetables or low added sugar, regardless of IBD status . In IBD cases, consumption of vegetables and added sugars were associated with reduced expression of the pro-inflammatory IFN-γ and an increased expression of anti-inflammatory IL-4. Conclusion The vaginal microbiome did not differ between individuals with IBD and HC; however, IBD cases exhibit a pro-inflammatory tone in the vagina (high IL-6) that is associated with higher vaginal microbial diversity. Regardless of IBD status, healthier diets are positively associated with an increased abundance of the beneficial L. crispatus in the vagina.

Quantum stereodynamic control of the Ne + H2+ ( <i>v</i> 0 = 0–2, <i>j</i> 0 = 1) proton transfer reaction

This study explores the quantum stereodynamic control of proton transfer in the Ne + H2+ (v0 = 0–2, j0 = 1) → NeH+ + H reaction using the time-dependent wave packet approach. The calculated isotropic integral cross sections for v0 = 1 and 2 are in reasonable agreement with the latest experimental results. Regarding the stereodynamic effects, the parallel configuration enhances reactivity, while the perpendicular configuration suppresses it in the endothermic reactions (v0 = 0–1). For the exothermic v0 = 2 case, the parallel configuration exhibits an inhibitory effect at low collision energies but, together with the perpendicular configuration, enhances the reactivity at higher energies. Moreover, the parallel alignment favors energy transfer into product translation, enhancing forward scattering. In contrast, the perpendicular alignment drives energy into rotation, resulting in increased sideways and backward scattering. These results are expected to shed light on future experimental and theoretical efforts aimed at understanding orientation-controlled proton transfer in rare-gas atom–molecule ion reactions.

Correction to “Synthesis of Collinoketones via Biomimetic [6 + 4] Cycloaddition”

Monitoring the health of wolves (Canis lupus): Integrating conservation and public health

The grey wolf ( Canis lupus ) population is expanding in parts of Europe due to legal protection and favorable ecological conditions. As wolves increasingly move into urban and suburban areas, interactions with domestic dogs become more frequent, raising the risk of pathogen transmission and posing potential threats to both wolf conservation and public health. This study investigated the health status of wolves in the Foreste Casentinesi National Park (Italy) using non-invasive fecal sampling conducted between May 2019 and March 2020. Samples were genetically analyzed to identify individuals and then screened for viral pathogens, Canine Coronavirus and Parvovirus, using PCR, Sanger sequencing, and phylogenetic analysis. Parasitological examinations were performed using flotation techniques on whole samples, and real-time PCR targeting Echinococcus granulosus and E. multilocularis was conducted on selected samples. Of the 260 samples collected, genetic analysis identified 80 individual wolves belonging to 8 packs. Only one sample tested positive for Coronavirus (1.2%), and none for Parvovirus. The detected sequence clustered with strains previously reported in wolves and foxes in Italy. Copromicroscopy revealed a high prevalence of veterinary-relevant endoparasites, including Eucoleus spp. (90.0%), Sarcocystis spp. (42.5%), Taeniids (28.7%), and Ancylostomatids (26.2%). Trichuris vulpis , Toxocara canis , and coccidia showed prevalence rates below 2%. All 104 samples tested for E. granulosus or E. multilocularis were negative. These findings suggest that while wolves in the FCNP commonly harbor several canine parasites, their role in the transmission of zoonotic pathogens appears limited. Although phylogenetic data suggest that coronavirus strains tend to cluster within wildlife species, molecular data on domestic dogs remain scarce. Nonetheless, the high prevalence of shared parasites highlights the need for ongoing surveillance in both wild canids and domestic carnivores. As wolves increasingly inhabit human-dominated landscapes, understanding disease dynamics at the wildlife–domestic interface is essential for effective conservation and public health strategies.

(Almost) Perfect heteronuclear system—NMR relaxation theory vs experiment

1H and 19F spin–lattice relaxation studies were performed for partially deuterated 3-fluoroaniline-2,4,6-d3,ND2 (FC6HD3ND2) in the frequency range from 10 kHz to 20 MHz (referring to 1H resonance frequency) at 208, 218, 228, and 238 K. The molecules contain single 1H and 19F nuclei in geometrically equivalent positions, and therefore, this compound was selected as an example of a heteronuclear (1H, 19F) spin system. The well-known spin relaxation theory developed for a model system including two spins (that can be exemplified by a single molecule of 3-fluoroaniline-2,4,6-d3,ND2) has been adopted (extended) to “real” systems (including many molecules) by taking into account relaxation pathways associated with the intermolecular 1H–1H, 19F–19F, and 1H–19F magnetic dipole–dipole interactions. The theoretical framework was applied to interpret the 1H and 19F spin–lattice relaxation data aiming to assess how accurately the model reproduces the experimental results. The relaxation theory predicts the bi-exponential relaxation processes for heteronuclear spin systems; the bi-exponentiality is, however, rarely observed experimentally. The reason for this effect was discussed with 3-fluoroaniline-2,4,6-d3,ND2 as an example.

Lack of Evidence Supporting Widespread Use of 1,2-Dibromoethane as an Activator for Zinc: Alternative Stirring or TMSCl Activation

Identifying risk patterns for sudden cardiac death in athletes: A clustering and principal component analysis approach

Sudden Cardiac Death (SCD) is a critical and unexpected condition that occurs due to cardiac causes within one hour of the onset of acute cardiovascular symptoms or twenty-four hours in unwitnessed cases. Despite advancements in cardiovascular medicine, practical methods for predicting SCD are still lacking, and there are no standardized systems to identify individuals at risk, especially in seemingly healthy populations such as athletes. In this study, we employed hierarchical clustering and principal component analysis (PCA) on data from 711 competitive athletes, revealing distinct patterns and cluster distributions in PCA space. Specifically, Clustering revealed characteristic feature combinations associated with increased SCD risk in athletes. Notably, certain clusters shared traits, including participation in Class C sports, sinus tachycardia, ventricular pre-excitation, personal or family history of heart disease, T-wave inversions, and prolonged QTc intervals. PCA helped visualize these patterns in distinct spatial regions, highlighting underlying structures and aiding intuitive risk interpretation. These results enable scientists to derive cluster metrics that serve as reference points for classifying new individuals and visually representing risk patterns in a clear graphical format. These findings establish a foundation for predictive tools that, with additional clinical validation, could aid in the prevention of SCD. The dataset used in this study, along with the clustering and PCA results, is available to the scientific community in an open format, together with the necessary tools and scripts to enable independent experimentation and further analysis.

Density of deeply supercooled alkali chloride aqueous solutions: Experimental and simulation results

The density of supercooled aqueous solutions of lithium, sodium, and potassium chloride was experimentally determined at atmospheric pressure down to −60 °C using dilatometry. To avoid freezing of the solutions, they were dispersed in a hydrophobic matrix, forming an emulsion, which inhibits heterogeneous nucleation. The experiments were conducted for solutions with concentrations up to the solubility limit under ambient conditions (NaCl and KCl) or up to the eutectic (LiCl). The temperature of maximum density and the apparent molar volume were calculated from density data. Molecular dynamics simulations were carried out for these systems within the same temperature and concentration ranges, using the TIP4P/2005 and Madrid-2019 force fields. Simulation and experimental results are critically compared, evaluating the performance of the model to reproduce the experimental results. Good agreement is obtained, a fact that avails the use of the model to study the structure of these solutions. This is performed by analyzing a set of radial distribution functions and the angular distributions of water molecules with respect to ions. The structural comparison among the cations indicates that Na+ and K+ salts share similar solvation patterns, while Li+ shows a distinct configuration, characterized by a tetragonal arrangement of water molecules around the ion that resembles that observed in solid-state environments. This finding aligns with the experimental results, since the analyses of the temperature of maximum density and apparent molar volume reveal that LiCl deviates from the tendencies observed for NaCl and KCl.

Research on cross-regional adaptation strategies for AI-enabled teaching devices from an educational equity perspective

This study investigates the impact of AI-enabled teaching devices on educational equity, focusing on the differences in their effectiveness between economically developed and underdeveloped regions in China, particularly along the Hu Huanyong Line. The research aims to assess whether AI devices can enhance educational outcomes and promote equity in regions with limited resources. Using a mixed-methods approach, the study involved a comparative analysis of eight schools across four cities on both sides of the Hu Huanyong Line. Data were collected through an online questionnaire survey of 247 teachers and an analysis of 620 student exam scores. The findings indicate that AI devices significantly improved teaching effectiveness in economically underdeveloped regions, with an average improvement of 7% to 20% in student performance. The study also revealed that while teachers in underdeveloped regions were generally positive about AI devices, they faced challenges in integrating the technology into their teaching practices due to insufficient familiarity and lack of support mechanisms. The results highlight the potential of AI technology to bridge educational gaps and promote equity by providing high-quality educational resources to under-resourced areas. However, the study emphasizes the need for comprehensive support measures, including teacher training and improved infrastructure, to ensure sustainable educational development. Future research should focus on long-term trends in educational resource investment and the development of culturally appropriate AI devices to further enhance educational equity and sustainable development.

Applying the modified mean-field model to superparamagnetic nanoparticles

Over the past decade, magnetic nanoparticles have been actively used in biomedicine to develop new diagnostic and therapeutic methods based on the magnetic response of tissues/cells with embedded particles. A successful application requires the development of theoretical methods for predicting the properties of magnetic nanoparticle ensembles, taking into account the inherent magnetic interactions between particles. This paper presents a simple and universal modified mean-field approach that considers both the interparticle magnetic interactions in a straightforward manner, appropriate for engineering and biomedical uses, along with the superparamagnetic degrees of freedom of magnetic nanoparticles. The presented approach shows high efficiency in characterizing the static and dynamic magnetic responses of superparamagnetic nanoparticles, whether they are suspended in liquid matrices or fixed in solid materials.

Climate-driven shifts in avocado suitability zones in India: Insights from ensemble modelling and niche hypervolume

Avocado ( Persea americana Mill.), a nutrient-rich tropical fruit, is gaining prominence in India due to rising domestic demand and export potential. However, its cultivation remains fragmented, largely confined to southern states, with limited knowledge of ecological requirements under diverse agro-climatic zones and climate change scenarios. This study aimed to identify key bioclimatic and non-bioclimatic factors influencing avocado suitability, model its current and future distribution using ensemble species distribution modelling (ESDM), assess niche dynamics under four Representative Concentration Pathways (RCPs 2.6, 4.5, 6.0, and 8.5) for 2050 and 2070, and evaluate implications for climate-resilient agroforestry planning. Using 35 spatially thinned occurrence records and high-resolution environmental predictors, ESDM integrating eight machine learning algorithms was applied. Model performance was robust (AUC: 0.86–0.91), with Random Forest and Maxent performing best. Critical predictors included isothermality, minimum temperature of the coldest month, precipitation in the coldest quarter, urbanization, and forest cover. Current suitability hotspots were concentrated in Kerala and Tamil Nadu. Future projections under RCPs 2.6 and 6.0 indicated northward and altitudinal expansion into the Western Ghats, northeastern hills, and eastern India, whereas RCP 8.5 suggested increased fragmentation and instability. Niche analysis revealed ecological breadth expansion under low to moderate emissions, but contraction and displacement under high-emission conditions. These findings highlight scope for expanding avocado cultivation under low to moderate emissions, provided thermal and precipitation stability is maintained. The study offers a geospatial foundation for climate-smart avocado production, conservation, and policy, emphasizing the protection of climatic refugia in southern India and adaptive agroecological strategies for long-term sustainability.

Effect of sequence variations on the phase behavior of a functional IDP fragment

Biomolecular phase separation can potentially influence processes such as signaling, transcription, and protein assembly. The driving force for phase separation is inter-molecular interactions, which are perturbed by amino acid mutations of phase-separating proteins. The pathogenic aggregated states of intrinsically disordered protein α-Syn are associated with several neurodegenerative diseases. A major pathway to form aggregates of α-Syn involves formation of liquid-like condensates, which may aid early assembly of α-Syn oligomers. Recent studies indicate that the P1 (residues 36–42) region in the N-terminal of α-Syn acts as a “master-controller” of its assembly and function. P1 can self-assemble and phase separate above a lower critical solution temperature. Here, we employ the P1 domain as a model peptide fragment to explore the role of sequence variation on phase behavior. In particular, the influence of point mutations Y39A and S42A of the P1 domain, known to be important for α-Syn assembly, are studied in detail by performing all-atom molecular dynamics simulations. The results reveal that both Y39A and S42A are able to self-assemble at elevated temperatures. Y39A exhibits similar thermo-responsive phase behavior to wild-type P1 and forms large oligomers. This indicates that although the presence of tyrosine stabilizes the network of interactions at lower temperatures, it is not crucial for forming the condensed phase at higher temperatures. In contrast, S42A shows anomalous temperature dependence and forms intermediate-sized oligomer assemblies. The study offers detailed insights into how sequence variation might affect the network of residue–residue interactions at different temperatures and alters the condensation pathway of IDP fragments.

'I fit the category of the box, it just doesn’t describe me well.' Exploring the perspectives of autistic women and gender-diverse individuals on self-report autism measures

Psychological assessments play a significant role in both clinical decision-making and the interpretation of research findings, with the quality of these inferences depending on the validity of the measures used. Recent evidence suggests there are gender differences in the presentation of autism, raising concerns about the validity of existing autism tools to measure autistic traits in women and the subsequent implications for clinical inferences and research. This study explored the perspectives of autistic women on the relevance of existing autism questionnaires to their lived experience, alongside additional input from gender-diverse individuals assigned female at birth (AFAB). Through interviews, focus groups, and online surveys, 22 autistic women and AFAB gender-diverse individuals shared their experiences using and perspectives on the Autism Spectrum Quotient-10, 14-item Ritvo Autism &amp; Asperger Diagnostic Scale, and Broad Autism Phenotype Questionnaire. The interview data were analysed using reflexive thematic analysis, identifying two overarching themes: (1) questionnaires measure only one way to be autistic, and not in an autism-friendly manner, and (2) enhancing questionnaires’ relevance for autistic women and individuals socialised as female: key missing experiences to include. The findings suggest that some of the most frequently used autism measures may not fully capture the experiences of autistic women and AFAB gender diverse individuals. Significant gaps were identified, indicating that important aspects of the participants’ lived experiences were missing. Furthermore, concerns were raised about the questionnaires’ lack of relevance to the autistic population as a whole. The findings underscore the non-satisfactory content validity of these tools for measuring autism in autistic women and AFAB gender-diverse individuals. This highlights the need for their refinement to better reflect contemporary understandings of different presentations of autistic traits, particularly the impact of gendered experiences, in a way that avoids the introduction of possible new biases and remains relevant and accessible to autistic individuals.

Rigidity-driven tail extension controls interfacial thickness in polymer–nanoparticle composites

We employ coarse-grained molecular dynamics simulations to investigate interfacial reorganization in polymer–nanoparticle composites, focusing on the competing effects of chain rigidity (Kbend) and attractive strength (ɛ). Geometric constraints create a critical adsorption threshold ɛk. Below this threshold, increasing attraction converts loops and tails into extended trains, improving surface-parallel alignment. Beyond ɛk, saturation causes competitive displacement that fragments trains and reduces orientational order. Machine learning analysis identifies the average tail segment length, ⟨Ltail⟩, as the primary controlling parameter of interfacial thickness δRMS (relative importance &amp;gt;89%). The derived scaling laws describe how rigidity enhances tail extension efficiency. Attractive strength influences thickness indirectly through its effect on ⟨Ltail⟩ within adsorption saturation constraints. These results establish two design principles: using rigidity-controlled tail manipulation for precise thickness tuning and applying ɛk-optimized attraction to maximize adsorption efficiency. This provides concrete guidelines for engineering nanocomposite interfaces.