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Validation of the Rosenberg Self-Esteem Scale among the Iranian adult population: A cross-sectional study

Background Self-esteem refers to an individual’s overall sense of self-worth, which plays a crucial role in their well-being. One of the most commonly used instruments to measure this concept is the Rosenberg Self-Esteem Scale (RSES), which has been translated and psychometrically validated in numerous cultures and languages to date. Therefore, this study aimed to assess the psychometric properties of the Persian version of the RSES in the Iranian adult population. Methods This cross-sectional validation study was conducted following the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines in Shahroud city, northeastern Iran. A total of 533 adults from this city who spoke Persian and were literate were included in the study using convenience sampling. After translating and culturally adapting the RSES in accordance with World Health Organization (WHO) guidelines, face and content validity were assessed using both qualitative and quantitative methods. Additionally, construct validity was evaluated through exploratory and confirmatory factor analysis. Reliability was also determined by calculating Cronbach’s alpha coefficient, McDonald’s omega coefficient, composite reliability (CR), and intraclass correlation coefficient (ICC). Results The face and content validity of all items were confirmed through both qualitative and quantitative methods. Based on the results of the exploratory factor analysis using polychoric correlations and robust weighted least squares (WLSMV) estimation, the Persian version of this scale consists of two factors: (1) Positive self-esteem and (2) Negative self-esteem, which account for 59.1% of the total variance of the scale. All goodness-of-fit indices in the confirmatory factor analysis also supported this model (CMIN/ df  = 2.29, RMSEA = .049, CFI = .981). These two factors showed acceptable internal consistency and stability, as evidenced by Cronbach’s alpha coefficients (.828 and .801), McDonald’s omega coefficients (.833 and .810), composite reliabilities (.820 and .796), and intraclass correlation coefficients (.838 and .875). Conclusion The findings of the current study showed favorable psychometric properties of the Persian version of the RSES for measuring self-esteem in the Iranian adult population.

Lexical feature analysis of Chinese informed consent forms based on the information entropy methods: A paired study of minor and their guardian’ version

High-quality informed consent forms (ICFs) are crucial to facilitate effective communication between researchers and patients. However, the complex and specialized terminologies in ICFs often result in biased or late interpretations, thus hindering the ability to make decisions in line with their own will. Therefore, evaluating the readability of ICFs is an important task for Institutional Review Boards (IRBs) and regulatory agencies. This study proposes the use of information theory methods, including Shannon entropy and its extension—Rényi entropy ( α values = 0, 0.5, and 1.5), as a set of comparisons, to quantify the lexical characteristics of Chinese ICFs, for either minors or their guardians. The Shannon entropy and Rényi entropy values of minor-version ICFs were significantly lower than those of guardian-version ICFs. The Shannon entropy and Rényi entropy with α  = 1.5 of the ICFs for minors show no significant differences compared to those of the sixth-grade textbooks, while the Rényi entropy with both α  = 0 and 0.5 shows no significant differences compared to the ninth-grade textbooks. This study utilized information entropies to assess lexical features of ICFs, as a pilot study to validate the feasibility of implementing Shannon and Rényi entropies to evaluate readability in Chinese ICFs.

Editorial Note: Measurement and driving factors of carbon productivity in China’s provinces: From the perspective of embodied carbon emissions

Zinc ion increases the effectiveness of phosphorus in agricultural soils through microbial solubilization

Phosphorus (P) is a key limiting factor for crop growth in agroecosystems, where soil microorganisms and mineral ions are essential for P turnover and mobilization. However, it is unclear how mineral ions affect microbially-mediated phosphorus cycling. In this study, 15 soil samples were collected from a typical agricultural area in Southwest China with a complex mineral composition. Soil phosphorus levels, microbial phosphorus genes, and typical soil mineral ions were subsequently analyzed to determine the mechanism of soil mineral ions on phosphorus cycling. The results showed that Zn 2+ was the main driver of P mobilization in the soils, and its accumulation led to changes in the expression of two important phosphorus genes, gcd and phnW . Ecological clusters dominated by Actinobacteria and Proteobacteria, phyla that carry the gcd gene, directly contributed to P activation through mutualistic interactions. phnW encodes a phosphorylglycolide hydrolase enzyme involved in phosphate accumulation, and its transcription may enhance the soil available phosphorus (AP) content by promoting enzymatic carbon-phosphorus bond cleavage, a process influenced by microbes in the phyla Proteobacteria, Gemmatimonadetes, Actinobacteria, and Acidobacteria. Herein, our work provides a new perspective on the mechanism of P cycling and improve P effectiveness in agricultural soils.

Hybrid machine learning models for enhanced arrhythmia detection from ECG signals using autoencoder and convolution features

Automated arrhythmia detection from electrocardiogram (ECG) signals is crucial and important for the early treatment of cardiac disease (CD). In this investigation, eight machine-learning models have been developed to identify improved ECG arrhythmia detection using two standard datasets (MIT-BIH Arrhythmia and the ECG 5000). In the first phase, two types of feature extraction schemes (autoencoder) and (Convolution) are used to obtain relevant features from ECG samples and subsequently, eight ML models are successfully trained and tested to find various performance matrices through simulation-based experiments. Then, the TOPSIS and mRMR ranking schemes are used to rank the ML models and identify the three best-performing models recommended for real-time arrhythmia detection. In this study, it is observed that for the same number of input features, models based on autoencoder features offer enhanced performance compared to those based on convolutional features. It is generally observed that the top identified hybrid model, Autoencoder Features with Neural Network (AEFNN) on the MIT-BIH dataset, achieves an accuracy of 97.96% and on the ECG5000 dataset, the hybrid model achieves an accuracy of 99.20%. This proposed model can be utilized for the early detection of arrhythmia, particularly in large-scale healthcare screening programs, thereby aiding in timely diagnosis and intervention. In this study, two types of features are used to model development in future work. Other relevant important features can be extracted from ECG samples, and those features can be used to develop accurate models to identify Heart disease.

Decentralized trust optimization in VANETs: A blockchain-driven hybrid PoS-PBFT architecture for enhanced security and energy-efficient communication

Vehicular Ad Hoc Networks (VANETs) are essential for the success of Intelligent Transportation Systems (ITS), providing real-time communication between vehicles and infrastructure. However, the highly dynamic and decentralized nature of VANETs introduces significant challenges in ensuring trust and security across the network, including security threats, communication overhead, and energy inefficiencies. This paper presents a novel blockchain-based trust management framework that addresses these issues by incorporating lightweight consensus mechanisms, optimized data propagation strategies, and energy-aware protocols. Our approach reduces communication overhead by selectively propagating trust updates, leading to a 35% decrease in overall network traffic compared to traditional broadcast-based systems. In terms of trust accuracy, our model achieves over 95% accuracy in detecting malicious nodes, significantly outperforming existing solutions. The proposed system demonstrates the identification and penalization of malicious behaviors such as Sybil attacks and false reporting with a 25% improvement in detection rate, while maintaining low latency (an average reduction of 30% compared to PoW-based systems) and efficient energy consumption, reducing energy use by up to 40%. The proposed model also incorporates a hybrid Proof of Stake (PoS) and Practical Byzantine Fault Tolerance (PBFT) consensus mechanism, which further enhances its scalability and fault tolerance. Simulation results show that our framework converges to accurate trust values faster than traditional methods, ensuring that reliable trust evaluations are made in real-time, even under high mobility conditions. The combination of these optimizations ensures that our framework is not only secure but also highly efficient, capable of supporting scalable and resilient VANET deployments. Furthermore, our decentralized approach ensures that trust decisions are made in real-time without the need for a centralized authority, making the system more adaptable to the high-mobility conditions of VANETs. This research offers a comprehensive solution for VANETs trust management, significantly improving communication efficiency, trust accuracy, and energy consumption while maintaining robust security and scalability. Our proposed blockchain-based trust management system provides a secure, energy-efficient, and scalable solution for VANETs, setting the stage for future developments in secure vehicular communication networks.

Reduction of noise pollution in CNC wood milling through multi-parameter optimization using response surface methodology

Background CNC (Computer Numerical Control) wood milling machines offer significant productivity advantages but are associated with excessive noise pollution, posing health risks to workers. This study investigates the influence of machining parameters on Noise Pollution Level (NPL) in CNC wood milling and aims to optimize these parameters to minimize noise emissions. Methods A Response Surface Methodology (RSM) based on Box-Behnken Design (BBD) was employed to model the effects of cutting speed, feed rate, depth of cut, and step over on NPL. A total of 27 experimental runs were conducted. Statistical analysis, including ANOVA and regression modeling, was performed to determine the significance of each parameter. The model was further optimized using a Genetic Algorithm (GA). Results The NPL observed across experiments ranged from 97.4 dB to 103.8 dB, with all values exceeding the NIOSH recommended limit of 85 dB. ANOVA results revealed that cutting speed, cutting speed squared, feed rate, and depth of cut had a statistically significant effect on NPL (p  <  0.05). The regression model showed a high degree of fit (R²  =  0.945). Optimal parameters—cutting speed of 12,730 rpm, feed rate of 58 mm/s, depth of cut of 3.2 mm, and step over of 6.4 mm—were identified using GA, resulting in a predicted NPL of 96.2 dB, which closely matched the experimentally validated value of 95.8 dB. Conclusion The study confirms that NPL in CNC wood milling can be significantly reduced by optimizing machining parameters. The integration of RSM and GA provides a reliable framework for minimizing occupational noise exposure, thereby enhancing worker safety in woodworking environments.

Association between fusion and clinical outcomes after anterior cervical discectomy at 1-, 2- and 5-year follow-up

Introduction Fusion achievement is considered a crucial factor in recovery following anterior discectomy. Nevertheless, the direct correlation between fusion and clinical outcomes, such as pain and disability, remains ambiguous due to inconsistent fusion measurement methods. Recent advancements in diagnostic fusion criteria now enable a more accurate fusion assessment. This study aimed to assess the association between fusion and clinical outcomes in patients undergoing anterior cervical discectomy. Methods This post-hoc analysis was conducted using data from the NEtherlands Cervical Kinematics (NECK) trial (NTR1289). Patients with a single level herniated disc that underwent anterior cervical discectomy between 2010 and 2014 were evaluated at 52, 104, and 260 weeks. Fusion was assessed using dynamic radiographs, applying the de Vries-Vleggeert criterion (≤3.0° Cobb angle and ≤2.0 mm interspinous distance). Clinical outcomes included the Neck Disability Index (NDI) and Visual Analog Scale (VAS) for arm and neck pain. Linear and logistic regressions were performed to evaluate correlations. Results Fusion was present in 57% (52 weeks), 75% (104 weeks), and 83% (260 weeks) of patients. Linear regression analyses revealed a clear trend suggesting favorable long-term VAS arm and neck pain scores in the patient’s demonstrating fusion with statistically significant lower VAS arm pain (mean difference: –18.9, 95% CI –36.9 to –0.9, p = 0.040) in the fusion group at 260 weeks follow-up. At earlier follow-up points, the differences in VAS arm pain did show a trend, but did not reach statistical significance (W52: 6.0, 95% CI –6.6 to 18.6, p = 0.346; W104: –11.5, 95% CI –24.2 to 1.3, p = 0.076). VAS neck pain scores showed a trend, but no statistically significant differences between groups across follow-up (W52: 3.2, 95% CI –8.1 to 14.5, p = 0.572; W104: –1.5, 95% CI –13.8 to 10.8, p = 0.808; W260: –12.7, 95% CI –30.9 to 5.6, p = 0.170). No significant differences were observed in NDI outcomes at any time point (W52: 3.2, 95% CI –4.8 to 11.2, p = 0.431; W104: –3.7, 95% CI –11.6 to 4.2, p = 0.358; W260: 2.5, 95% CI –7.2 to 12.1, p = 0.608). Logistic regression analysis using success rates based on established cut-off values showed a trend towards patients with fusion having markedly higher odds of success long term, with a significant higher odd of success in VAS arm pain at 260 weeks FU (OR 9.88, 95% CI 1.55–62.80, p = 0.015). Conclusion A comparative analysis indicated reduced arm and neck pain in the fusion group at the 260-week follow-up. This finding becomes apparent only in the long-term post-intervention period, suggesting that muscle tension may function as a natural brace during the initial years following surgery. This tension effectively limits excessive flexion-extension movements, thereby mitigating discomfort. These results have potential implications for routine clinical surgical practice. Future studies with larger sample sizes are needed to validate these findings, including short-term follow-up.

PRCnet: An efficient model for automatic detection of brain tumor in MRI images

Brain tumors are the most prevalent and life-threatening cancer; an early and accurate diagnosis of brain tumors increases the chances of patient survival and treatment planning. However, manual tumor detection is a complex, cumbersome and time-consuming task and is prone to errors, which relies on the radiologist’s experience. As a result, the development of an accurate and automatic tumor detection system is critical. In this paper, we proposed a new model called Parallel Residual Convolutional Network (PRCnet) model to classify brain tumors from Magnetic Resonance Imaging. The PCRnet model uses several techniques (such as filters of different sizes with parallel layers, connections between layers, batch normalization layer, and ReLU) and dropout layer to overcome the over-fitting problem, for achieving accurate and automatic classification of brain tumors. Our methodology used data augmentation techniques such as rotation, flipping, and scaling. These enhanced the diversity and quantity of the training dataset, contributing significantly to the model’s improved performance. The PRCnet model is trained and tested on two different datasets and obtained an accuracy of 94.77% and 97.1% for dataset A and dataset B, respectively which is way better as compared to the state-of-the-art models. Our PRCnet code publicly available at: https://github.com/Ahmeed-Suliman-Farhan/PRCnet-Model

A mixed-methods study to explore the modifiable aspects of treatment burden in Parkinson’s disease and develop recommendations for improvement

Background People with Parkinson’s (PwP) and their caregivers have to manage multiple daily healthcare tasks (treatment burden). This can be challenging and may lead to poor health outcomes. Objective To assess the extent of treatment burden in Parkinson’s disease(PD), identify key modifiable factors, and develop recommendations to improve treatment burden. Methods A mixed-methods study was conducted consisting of: 1) a UK-wide cross-sectional survey for PwP and caregivers using the Multimorbidity Treatment Burden Questionnaire (MTBQ) to measure treatment burden levels and associated factors and 2) focus groups with key stakeholders to discuss survey findings and develop recommendations. Results 160 PwP (mean age = 68 years) and 30 caregivers (mean age = 69 years) completed the surveys. High treatment burden was reported by 21% (N = 34) of PwP and 50% (N = 15) of caregivers using the MTBQ. Amongst PwP, higher treatment burden was significantly associated with advancing PD severity, frailty, a higher number of non-motor symptoms, and more frequent medication timings (>3 times/day). Caregivers reporting higher treatment burden were more likely to care for someone with memory issues, had lower mental well-being scores and higher caregiver burden. Three online focus groups involved 11 participants (3 PwP, 1 caregiver and 7 healthcare professionals) recruited from the South of England. Recommendations to reduce treatment burden that were discussed in the focus groups include improving communication. clear expectation setting, and better signposting from healthcare professionals, increasing education and awareness of PD complexity, flexibility of appointment structures, increasing access to healthcare professionals, and embracing the supportive role of technology. Conclusions Treatment burden is common amongst PwP and caregivers and could be identified in clinical practice using the MTBQ. There is a need for change at individual provider and system levels to recognise and minimise treatment burden to improve health outcomes in PD.

Correction: Associations between age at natural menopause and risk of hypothyroidism among postmenopausal women from the Canadian Longitudinal Study on Aging (CLSA)

Health risk realization versus warning: Impact on lifestyle behaviours

Using individual-level panel data from Understanding Society I estimate the response to a health risk realization on a healthy lifestyle index. To overcome the endogeneity of a diagnosis, I match on initial health risks. I find individuals improve their overall lifestyle healthiness when faced with a large negative health event such as a heart attack or diabetes diagnosis, interpreted as a precise signal about their health status, whereas they do not respond to a noisier signal through solely receiving information about certain health risk factors, such as a diagnosis of high blood pressure or angina (chest pain). The drivers of the overall effect are a decrease in the number of cigarettes smoked and an increase in not drinking alcohol; there is no significant effect found for either diet or exercise. I find some heterogeneity by sex, but only when looking at individual lifestyle behaviours. Overall, the findings suggest that the realization of a health risk leads individuals to improve their lifestyle behaviours, while only a noisier signal about their health risks leads to no such change.

High-protein diets reduce plasma pro-inflammatory cytokines following lipopolysaccharide challenge in Swiss Albino mice

Macronutrients serve as principal sources of energy, structural components, and regulators of physiological processes. However, the optimal macronutrient combination for health remains unclear. While previous studies indicate that dietary macronutrient composition influences immune function, many have examined individual nutrients in isolation, failing to reflect the interactive effects of macronutrients. This study addresses this gap by examining how varying ratios of dietary carbohydrates, proteins, and lipids modulate serum cytokine responses to lipopolysaccharide challenge in Swiss albino mice. Male and female Swiss albino mice (n = 6 per group), aged 6–8 weeks, were randomly assigned to six purified isocaloric diets with differing macronutrient ratios for 15 weeks. Body weights were monitored to assess nutritional status. Serum levels of TNF-α, IL-6, IL-1β, and IL-10 were measured in unchallenged mice and after three hours of intraperitoneal LPS administration. Mice fed high-carbohydrate, low-protein diets had the highest weight (33.1 g ± 1.1), while those on high-lipid, low-protein diets had the lowest (28.3 g ± 0.6). Plasma levels of TNF-α and IL-10 varied significantly (p < 0.05) by diet in the unchallenged mice. IL-1β did not differ markedly (p = 0.085) across the dietary groups, and IL-6 levels were below the assay’s detection limit (<230.312 pg/mL). Following the lipopolysaccharide challenge, all cytokines increased, with significant differences among diets. Mice on high-protein diets exhibited notably lower TNF-α, IL-6, and IL-1β levels compared to those on high-carbohydrate or high-lipid diets. In contrast, IL-10 levels were higher in mice fed low-protein, high-carbohydrate, or high-lipid diets. In conclusion, high-protein diets appeared to dampen the responsiveness to lipopolysaccharide challenge, as indicated by smaller increases in pro-inflammatory cytokine levels, whereas high-carbohydrate and high-lipid diets elicited greater cytokine responses. We recommend that nutritional strategies aimed at modulating inflammation should ensure adequate dietary protein to help protect against both acute and chronic inflammation.

A fast regulation algorithm for low voltage in regional grids with high uncertainty and high penetration of wind and solar loads at the grid end

To address the issue of low voltage fluctuations at the grid end caused by the high penetration of wind and solar loads in the power grid, a fast low-voltage regulation algorithm is proposed. This algorithm aims to resolve the stability challenges brought by the uncertainty of wind and solar power sources. The method involves predicting the power output fluctuations of wind turbines, photovoltaics, and loads. A multi-objective fast regulation model is constructed, incorporating safety, efficiency, and cost factors, with corresponding constraints established. A simultaneous optimization transmission mechanism is adopted to achieve rapid voltage regulation. Results indicate that the algorithm has a high degree of prediction accuracy, rapidly restoring voltage to normal levels and maintaining stability within a tolerance level of 0.95 or higher. In conclusion, this algorithm effectively improves the stability and reliability of the power grid, providing a practical technical solution for handling high penetration levels of wind and solar energy in the grid.

Modular Synthesis of Pyritide‐Inspired Macrocycles Featuring Bipyridine Motifs

Abstract Macrocycles represent a promising class of drug‐like scaffolds with unique structural features and the ability to engage challenging targets such as protein–protein interactions. Inspired by structural characteristics of pyritides, we constructed a library of 27 diverse macrocycles via a build/couple/pair approach, enabled by efficient synthesis of bipyridine‐based triaryl building blocks through azaindole cleavage. Kinetic and cheminformatic analyses confirmed both reactivity trends and structural diversity. From this library, we identified a potential ferroptosis inhibitor, 6 p aW , with clear structure–activity relationships, validating our diversity‐oriented synthesis platform. This strategy offers a robust approach to macrocycle library design, expanding opportunities for targeting previously inaccessible biological space.

Enhancing drug repositioning: A multi-class ensemble model for drug-target interaction prediction with action type categorization

Accurate prediction of drug–target interactions (DTIs) is critical for accelerating drug repositioning and reducing the cost of pharmaceutical development. Most existing studies frame DTI prediction as a binary task and often neglect the pharmacological action types and the quality of non-interaction data. This study introduces a multi-class classification framework that categorizes interactions into activators, inhibitors, and non-action classes. A novel zero-interaction selection algorithm is proposed, based on weighted drug–drug and protein–protein similarity scores, to improve dataset diversity and reliability. Drug and protein features were extracted from DrugBank, PubChem, and UniProt, and various feature selection and dimensionality reduction techniques—including decision tree, random forest importance scores, principal component analysis (PCA), Autoencoders, and Permutation importance—were evaluated to identify the most informative features for classification. We also compare concatenation-based and convolution-based feature integration strategies and systematically evaluate a range of classifiers, including both feature-based and graph-based models, with special attention to ensemble learning approaches. The concatenation method consistently outperforms convolution, and Histogram-based Gradient Boosting (HGB) achieves the best predictive overall accuracy with an average of 87.90% on the external test set. Meanwhile, HeteroGNN demonstrates more balanced class-wise performance, particularly for underrepresented classes. This work provides a scalable and interpretable framework for computational drug repositioning, supporting faster and more cost-effective identification of therapeutic candidates.

Enantioselective Synthesis of Mechanically Planar Chiral Rotaxanes by an Asymmetric Povarov Reaction‐Enabled Desymmetrization

Abstract Mechanically planar chiral (MPC) rotaxanes are a type of chiral mechanically interlocked molecules (MIMs) characterized by their unique chiral structures and dynamic motion properties, which have shown significant potential in various chiral science research fields. However, the exploration of efficient catalytic enantioselective methods for synthesizing these chiral molecules is still largely underexplored. We here report an efficient method for asymmetric synthesis of MPC rotaxanes through an enantioselective desymmetrization strategy. By employing the chiral phosphoric acid‐catalyzed asymmetric Povarov reaction, the symmetry of prochiral rotaxanes with a rotationally symmetric macrocycle was broken, which was followed by a one‐pot oxidative aromatization step to yield various MPC rotaxanes with good yields and high enantioselectivities. Moreover, in cases where the one‐pot procedure did not provide satisfactory enantioselectivities, a stepwise protocol was employed to produce both enantiomers of MPC rotaxanes with excellent enantioselectivities, highlighting the practicability of this method. Extensive studies were performed to elucidate the origin of mechanical planar chirality within this method, which was postulated to arise from the synergistic interplay between the excellent enantioselectivity in generating point stereogenicity during the asymmetric Povarov reaction and the preferential selection of the mechanical geometric conformer of substrates during this process.

“You just forget about preeclampsia and move on” –awareness of chronic disease risks and follow-up preferences after preeclampsia in Ireland: a national qualitative study

Background Preeclampsia is associated with increased long-term risks of cardiovascular disease, kidney disease, and stroke. International guidelines recommend structured follow-up care to prevent chronic disease, but limited research has explored women’s knowledge of these risks, and their preferences regarding long-term follow-up care. This may impede how obstetric information is used for chronic disease prevention in practice. Methods This qualitative study used purposive and snowball sampling to recruit women in Ireland diagnosed with preeclampsia at least one year prior. Semi-structured interviews were conducted online, exploring awareness of chronic disease risks and provision of follow-up care. Thematic analysis was performed using an inductive approach. Results Twelve women aged 28–64 years were interviewed, at median six years since preeclampsia diagnosis. Participants’ antenatal and postnatal care experiences varied widely, but most described follow-up care after preeclampsia as being inconsistent or absent. Three key themes were generated: (1) Preeclampsia in the ‘rear-view mirror’ —women often viewed preeclampsia as an acute, resolved event, and had limited awareness of any long-term risks. However, they regarded chronic disease risk information as valuable and empowering; (2) Changing priorities as ‘life takes over’ — women often prioritised other family members’ health needs above their own, particularly in the newborn phase. They favoured delaying discussions about chronic disease risks, ideally to 6–12 months after pregnancy, preferably provided through primary care; (3) Desire for proactive, ‘blameless’ follow-up care—women favoured systematic, non-judgmental follow-up programmes underpinned by clear communication between obstetric and primary care services, continuity of healthcare providers, and free access. Some described residual anxiety relating to their preeclampsia experience, and emphasised the importance of sensitive, person-centred follow-up care. Conclusion Women affected by preeclampsia in Ireland typically have limited awareness of its links with long-term chronic disease risks, and frequently experience a lack of structured follow-up care. They expressed strong support for receiving personalised information about opportunities for secondary prevention, and advocated for systematic, proactive follow-up. Participants emphasised that future models of care should be pragmatic, person-centred, and include default enrolment for all women.

Correction: The impacts of product characteristics and regulatory environment on smokers’ preferences for tobacco and alcohol: Evidence from a volumetric choice experiment

Ionic Covalent Organic Framework Membranes for Rapid Moisture‐Driven Actuation and Sensing

Abstract The development of smart materials capable of rapid and reversible responses to ambient humidity is essential for next‐generation sensors, monitoring systems, and adaptive devices. Covalent organic frameworks (COFs), with their tunable porosity and designable architectures, represent a promising class of materials for such stimuli‐responsive systems, yet practical implementation remains limited. In this study, we report a self‐standing ionic COF membrane, synthesized by integrating hydrogen‐bonding ionic functionalities into the framework backbone. This rational design endows the membrane with exceptional moisture‐driven actuation and sensing behavior. The unique hydrogen bonding interactions within the framework facilitate rapid water uptake and release, enabling a rapid response time of   1 s. The membrane demonstrates excellent mechanical flexibility, high water sorption capacity, and robust cycling durability. Results from DFT calculations and MD simulations revealed that water molecules could strongly adsorb onto the membrane via hydrogen bonding to modulate its micropore structure and facilitate its responsive behavior. Furthermore, its responsive behavior to subtle humidity changes makes it suitable for applications such as human‐interfacing soft actuators, smart switches, and soil moisture sensors. This study highlights the utility of ionic COF membranes as a versatile platform for creating next‐generation intelligent materials.