Rigorous Model Research Articles

Family successor CEO and innovation investment: a study based on the theoretical upper echelons and conflict theory

PurposeGrounded in upper echelons theory (UET) and conflict theory, the purpose of this study is to analyzes the innovative behavior of family firms from the perspective of the cognitive differences between successors and senior managers.Design/methodology/approachThis research employed a sample listed family firms in China. The obtained results were subjected to hierarchical regression analysis, complemented by rigorous model robustness testing through propensity score matching and regression with substitution variables.FindingsSuccessors engender task conflicts with family members in the top management team (TMT) due to cognitive differences, thereby stimulating corporate innovation. Conversely, successors engender relationship conflicts with non-family members in the TMT, impeding innovation. Furthermore, the performance expectations and the gender of the successor CEO also influence the relationship between cognitive differences and innovation between the successor and the TMT.Originality/valueThis study's originality and value lie in its innovative application of UET and conflict perspectives to dissect the intricate layers of intergenerational cognitive differences and their impact on the innovative behavior of family firms. It augments our comprehension of how the internal dynamics within family firms shape strategic innovation decisions.

Female CEOs and Green Innovation: Evidence from Asian Firms

This study aims to examine how female CEOs influence green innovation and whether firm size moderates this connection. Our paper focuses on CEOs, who are considered the strategic leaders of corporations, because of their crucial role in making important decisions. This research paper examines how female CEOs influence green innovation (GI) in the Asian industrial sector. The primary goal is to address these research questions: Do Asian industrial firms with female and male CEOs differ in their GI efforts? Is there a positive moderating influence of Asian industrial enterprises’ size on the nexus between women in CEO positions and eco-innovation? Based on our research questions, firm size is likely a determining factor in the GI of female CEOs. This research employs rigorous econometric modeling to analyze a substantial dataset of listed Asian industrial companies from 2013 to 2022. We have found a significant positive correlation between female CEOs and GI in Asian industrial firms. It has been proven that female CEOs in the industrial sector are more inclined to promote environmentally friendly practices. Furthermore, the size of an industrial firm amplifies the beneficial influence of a female CEO on the firm’s chances of engaging in GI initiatives. Regarding the moderating effect of size, the size of companies significantly magnifies the impact of female CEOs on GI. The effectiveness of female CEOs on environmentally friendly practices is more prominent in large corporations than in smaller ones. Our outcomes remain robust with respect to endogeneity issues using two-step GMM estimators. This study proposes that stakeholders, particularly in Asian countries, should promote the increased representation of females in CEO roles, particularly within large corporations. This is because women-led companies demonstrate superior performance in GI endeavors. Hence, regulators must establish policies that facilitate the participation of women in CEO positions within large-scale enterprises. These policies may strengthen the private sector’s capacity to foster sustainable innovation.

Adsorption Thermodynamics for Process Simulation.

Adsorption has rapidly evolved in recent decades and is an established separation technology extensively practiced in gas separation industries and others. However, rigorous thermodynamic modeling of multicomponent adsorption equilibrium remains elusive, and industrial practitioners rely heavily on expensive and time-consuming trial-and-error pilot studies to develop adsorption units. This article highlights the need for rigorous adsorption thermodynamic models and the limitations and deficiencies of existing models such as the extended Langmuir isotherm, dual-process Langmuir isotherm, and adsorbed solution theory. It further presents a series of recent advances in the generalization of the classical Langmuir isotherm of single-component adsorption by deriving an activity coefficient model to account for the adsorbed phase adsorbate-adsorbent interactions, substituting adsorbed phase adsorbate and vacant site concentrations with activities, and extending to multicomponent competitive adsorption equilibrium, both monolayer and multilayer. Requiring a minimum set of physically meaningful model parameters, the generalized Langmuir isotherm for monolayer adsorption and the generalized Brunauer-Emmett-Teller isotherm for multilayer adsorption address various thermodynamic modeling challenges including adsorbent surface heterogeneity, isosteric enthalpies of adsorption, BET surface areas, adsorbed phase nonideality, adsorption azeotrope formation, and multilayer adsorption. Also discussed is the importance of quality adsorption data that cover sufficient temperature, pressure, and composition ranges for reliable determination of the model parameters to support adsorption process simulation, design, and optimization.

Towards high-performance deep learning architecture and hardware accelerator design for robust analysis in diffuse correlation spectroscopy

This study proposes a compact deep learning (DL) architecture and a highly parallelized computing hardware platform to reconstruct the blood flow index (BFi) in diffuse correlation spectroscopy (DCS). We leveraged a rigorous analytical model to generate autocorrelation functions (ACFs) to train the DL network. We assessed the accuracy of the proposed DL using simulated and milk phantom data. Compared to convolutional neural networks (CNN), our lightweight DL architecture achieves 66.7% and 18.5% improvement in MSE for BFi and the coherence factor β, using synthetic data evaluation. The accuracy of rBFi over different algorithms was also investigated. We further simplified the DL computing primitives using subtraction for feature extraction, considering further hardware implementation. We extensively explored computing parallelism and fixed-point quantization within the DL architecture. With the DL model's compact size, we employed unrolling and pipelining optimizations for computation-intensive for-loops in the DL model while storing all learned parameters in on-chip BRAMs. We also achieved pixel-wise parallelism, enabling simultaneous, real-time processing of 10 and 15 autocorrelation functions on Zynq-7000 and Zynq-UltraScale+ field programmable gate array (FPGA), respectively. Unlike existing FPGA accelerators that produce BFi and the β from autocorrelation functions on standalone hardware, our approach is an encapsulated, end-to-end on-chip conversion process from intensity photon data to the temporal intensity ACF and subsequently reconstructing BFi and β. This hardware platform achieves an on-chip solution to replace post-processing and miniaturize modern DCS systems that use single-photon cameras. We also comprehensively compared the computational efficiency of our FPGA accelerator to CPU and GPU solutions.

ESR Essentials: radiomics-practice recommendations by the European Society of Medical Imaging Informatics.

Radiomics is a method to extract detailed information from diagnostic images that cannot be perceived by the naked eye. Although radiomics research carries great potential to improve clinical decision-making, its inherent methodological complexities make it difficult to comprehend every step of the analysis, often causing reproducibility and generalizability issues that hinder clinical adoption. Critical steps in the radiomics analysis and model development pipeline-such as image, application of image filters, and selection of feature extraction parameters-can greatly affect the values of radiomic features. Moreover, common errors in data partitioning, model comparison, fine-tuning, assessment, and calibration can reduce reproducibility and impede clinical translation. Clinical adoption of radiomics also requires a deep understanding of model explainability and the development of intuitive interpretations of radiomic features. To address these challenges, it is essential for radiomics model developers and clinicians to be well-versed in current best practices. Proper knowledge and application of these practices is crucial for accurate radiomics feature extraction, robust model development, and thorough assessment, ultimately increasing reproducibility, generalizability, and the likelihood of successful clinical translation. In this article, we have provided researchers with our recommendations along with practical examples to facilitate good research practices in radiomics. KEY POINTS: Radiomics' inherent methodological complexity should be understood to ensure rigorous radiomic model development to improve clinical decision-making. Adherence to radiomics-specific checklists and quality assessment tools ensures methodological rigor. Use of standardized radiomics tools and best practices enhances clinical translation of radiomics models.

Nomogram Predictive Model for Vaginal Birth after One Prior Cesarean Section: A Retrospective Study

Background: Repeat cesareans pose significant maternal risks, whereas vaginal birth after cesarean (VBAC) offers better outcomes, with a complication rate of 2.4% compared with 3.6% for elective repeat cesareans and 14.1% for failed trial of labor after cesarean (TOLAC). TOLAC rates are low in China, and success rates vary between 60% and 80%. This study aims to develop a nomogram-based predictive model for VBAC in China and improve existing, less rigorous scoring models. Methods: This retrospective cohort study was conducted at Hangzhou Women's Hospital from February 2015 to March 2020, and included 159 parturient attempts at labor after one prior low transverse cesarean section. The participants were divided into two groups based on their mode of delivery for comparison: the VBAC and the TOLAC failure group. Univariate and multivariate logistic regression analyses were conducted to identify independent predictors for VBAC and develop a nomogram predictive model to estimate the success rate of TOLAC. The bootstrap method was used for internal validation of the models. Three different VBAC prediction models were evaluated by plotting receiver operating characteristic (ROC) curves, calibration curves, and decision curves. Results: A total of 127 women had VBAC and 32 women failed TOLAC, with a success rate of 79.9%. Three independent factors affecting the VBAC were identified: gestational age at delivery, Bishop score and newborn birth weight. A predictive nomogram model for the VBAC was constructed that incorporates these three factors. The model showed a good fit (χ2 = 11.94, p = 0.154) with an overall prediction accuracy of 81.1%. The area under the ROC curve was 0.83 (95% CI (confidence interval), 0.76–0.90) (p < 0.001) and the optimal cut-off value was 83.4%. The bootstrap internal validation showed that our predictive model maintained high overall accuracy and specificity, but exhibited low sensitivity and a low Kappa coefficient. Compared to the Grobman model and Jiaming Rao et al. model, our developed prediction model possesses the strongest discriminatory ability and the highest net benefit, followed by the model by Jiaming Rao et al. All three models demonstrate a high degree of fit. Conclusions: Shorter gestational age at delivery, lower newborn birth weight and higher cervical Bishop score are favorable factors for VBAC. The predictive nomogram model for the VBAC after a single cesarean section, constructed with these three factors, has good predictive efficacy. The model is simple to calculate and has practical value in the clinical selection of suitable candidates for TOLAC after a single cesarean section.

Optimization of SVR and CatBoost models using metaheuristic algorithms to assess landslide susceptibility

In this study, a landslide susceptibility assessment is performed by combining two machine learning regression algorithms (MLRA), such as support vector regression (SVR) and categorical boosting (CatBoost), with two population-based optimization algorithms, such as grey wolf optimizer (GWO) and particle swarm optimization (PSO), to evaluate the potential of a relatively new algorithm and the impact that optimization algorithms can have on the performance of regression models. The Kerala state in India has been chosen as the test site due to the large number of recorded incidents in the recent past. The study started with 18 potential predisposing factors, which were reduced to 14 after a multi-approach feature selection technique. Six susceptibility models were implemented and compared using the machine learning algorithms alone and combining each of them with the two optimization algorithms: SVR, CatBoost, SVR-PSO, CatBoost-PSO, SVR-GWO, and CatBoost-GWO. The resulting maps were validated with an independent dataset. The performance rankings, based on the area under the receiver operating characteristic curve (AUC) metric, are as follows: CatBoost-GWO (AUC = 0.910) had the highest performance, followed by CatBoost-PSO (AUC = 0.909), CatBoost (AUC = 0.899), SVR-GWO (AUC = 0.868), SVR-PSO (AUC = 0.858), and SVR (AUC = 0.840). Other validation statistics corroborated these outcomes, and the Friedman and Wilcoxon-signed rank tests verified the statistical significance of the models. Our case study showed that CatBoost outperformed SVR both in case the models were optimized or not; the introduction of optimization algorithms significantly improves the results of machine learning models, with GWO being slightly more effective than PSO. However, optimization cannot drastically alter the results of the model, highlighting the importance of setting up of a rigorous susceptibility model since the early steps of any research.

Self‐Powered Wearable Pressure Sensors for Detection and Separation of Signals for Various Human Movements

A detailed study on the dynamic response of mountable pressure sensors is presented, with a focus on foot pressure sensors integrated with carbon nanotube (CNT)‐coated cotton fibers. The research explores the sensor's sensitivity to pressure changes, repeatability, hysteresis, and durability through rigorous modeling and experimental validation. Computational simulations using Python and experimental data demonstrate the sensor's nonlinear conductance response to applied force, attributed to the varying contact area and number of contact points among the fibers. Long‐term outdoor exposure tests confirm the material's resilience, maintaining electrical conductivity and structural integrity. The study also demonstrates the sensor's ability to monitor human activities like walking, running, stair climbing, and jumping, as well as detecting muscle movements during swallowing, coughing, and speech, highlighting potential applications in health monitoring and artificial voice synthesis. The MRMR algorithm is used for feature selection to distinguish activities, demonstrating the sensor's potential for activity recognition. Additionally, a piezoelectric sensor on the pressure sensors estimates harvested electric power to power wearable devices. This work contributes to the advancement of self‐powered wearable pressure sensors to monitor real‐time human activity, with implications for healthcare, sports performance, and assistive technologies.

Perception and Trust in Autonomous Vehicles Post Cyber Security Incidents

The integration of Autonomous Vehicles (AVs) into modern systems of transportation brings with it a new and transformative era. Central to the successful realisation of this transformation is the public’s trust in these vehicles and their safety, particularly in the aftermath of cyber security breaches. The following research therefore explores the various factors underpinning this trust in the context of cyber security incidents. A dual-methodological approach was used in the study. Quantitative data was gathered from structured questionnaires distributed to and completed by a cohort of 151 participants and qualitative data, from comprehensive semi-structured interviews with AV technology and cyber security experts. Rigorous Structural Equation Modelling of the quantitative data then allowed for the identification of the key factors influencing public trust from the standpoint of the research participants including the perceived safety of AV technology, the severity of cyber security incidents, the historic cyber security track record of companies and the frequency of successful cyber security breaches. The role of government regulations, though also influential, emerged as less so. The qualitative data, processed via thematic analysis, resonated with the findings from the quantitative data. This highlighted the importance of perceived safety, incident severity, regulatory frameworks and corporate legacy in shaping public trust. Whilst cyber incidents no doubt erode trust in AVs, a combination of technological perception, regulatory scaffolding and corporate history critically impacts this. These insights are instrumental for stakeholders, from policymakers to AV manufacturers, in charting the course of AV assimilation successfully in future.

Intensive γ -ray light sources based on oriented single crystals

The feasibility of gamma-ray light sources based on the channeling phenomenon of ultrarelativistic electrons and positrons in oriented single crystals is demonstrated using rigorous numerical modeling. Case studies are presented for 10 GeV and sub-GeV e−/e+ beams incident on 10−1−100 mm thick diamond and silicon crystals. It is shown that for moderate values of the beam average current (≲10 μA), the average photon flux in the energy range of 100–102 MeV emitted within the 101–103 μrad cone and 1% bandwidth can be on the level of 1010 photon/s for electrons and 1010–1012 photon/s for positrons. These values are higher than the fluxes available at modern laser-Compton gamma-ray light sources. Published by the American Physical Society 2024

Unlocking the efficiency potential of all-perovskite tandem solar cells: Insights from photoelectric coupling simulations

All-perovskite tandem solar cells (TSCs) have garnered widespread attention due to their high-efficiency potential and low-cost fabrication processes. However, a significant efficiency gap remains between all-perovskite TSCs (30.1%) and their Shockley-Queisser limit (∼44%), primarily due to a lack of comprehensive understanding of the working mechanisms and design principles for TSCs. Here, we develop rigorous photoelectric coupling simulation models and systematically explore the device physics and working mechanisms of all-perovskite TSCs. Through in-depth simulations, we find that: 1) TSC performance is more sensitive to the interface defects in current-limited sub-cells, while bulk defects in both wide-bandgap and narrow-bandgap perovskites are crucial in limiting TSC performance, regardless of current-matching conditions. 2) The interface defects of perovskite/functional layers (FLs) near intermediate recombination layers (IRLs) have a substantial effect on TSC efficiency, which, however, can be mitigated by a double-layer FL design. 3) The work function of the IRL and the energy levels of adjacent FLs should be well-controlled to balance carrier transport barriers at FL/perovskite and FL/IRL interfaces. Based on these principles, we propose a detailed roadmap to enhance the efficiency of all-perovskite TSCs to 34.15% through collaborative optimization strategies. This study provides valuable guidance for designing high-efficiency all-perovskite TSCs.

Economic feasibility of the biorefinery processing bamboo residues with biphasic phenoxyethanol-acid pretreatment technology: Techno-economic analysis

This study conducted a techno-economic analysis to assess the economic feasibility of a biorefinery converting bamboo residues to bioproducts via phenoxyethanol-acid pretreatment. The analysis is integrated with a rigorous process simulation model with the support of experiment data. The bioproducts include ethanol or polylactic acid (PLA), with lignin-based wood adhesive as the byproduct. The minimum selling price (MSP) is $515-$819 per t ethanol and $1,512-$1,842 per t PLA when biphasic treatment is deployed, compared to the MSP of $2,318-$2,721 per t ethanol and $2,859-$3,211 per t PLA for no biphasic pretreatment. Adopting a ratio of 1:1 phenoxyethanol: acid solution (1%) in pretreatment decreases the final product yield but leads to lower MSP than 4:1 phenoxyethanol: acid solution (1%) due to lower phenoxyethanol consumption. A sensitivity analysis identifies the key drivers of reducing MSP, including increasing economic gain from the lignin-based wood adhesive, lowering the phenoxyethanol cost, and expanding plant capacity.

Unraveling a novel biphasic CO2 capture process through rigorous modeling

Modification of the chemical absorbent is a crucial aspect in the field of CO2 capture. Nevertheless, the consequences arising from the utilization of modified solvents have not been thoroughly examined on a process scale. This study investigates a biphasic chemical absorption process for CO2 capture using a blended solvent composed of 2-ethylamino ethanol (EAE), diethylene glycol diethyl ether (DEGDEE), and water. The design was based on rigorous consideration of the physical properties of the pure components and the solution, as well as chemical equilibrium. Our results indicate that the proposed biphasic process reduces specific regeneration energy by 6.45 % compared to the conventional method using monoethanolamine (MEA) solution as a solvent (biphasic: 3.41 GJ/Ton; conventional: 3.63 GJ/Ton). Furthermore, it presents the advantage of utilizing low-temperature waste heat for solvent stripping, resulting in a notable 56 % decrease in indirect emissions (biphasic process: 0.119 Ton/Ton, conventional: 0.274 Ton/Ton). When processing flue gases that are more diluted in CO2, the CO2-e of the biphasic process was only slightly increased. Finally, the techno-economic analysis demonstrated that the biphasic process exhibits minimum required treatment prices (MRTPs) comparable to those of conventional methods for treating flue gases with CO2 concentrations ranging from 10 to 19 mol% (biphasic: 0.755 to 1.452 USD/kg, conventional: 0.756 to 1.455 USD/kg). This finding highlights the economic potential of the biphasic process. Overall, this investigation sheds light on the potential of intensifying the CO2 capture process in a biphasic environment.

Comparison of rigorous scattering models to accurately replicate the behaviour of scattered electromagnetic waves in optical surface metrology

Rigorous scattering models are based on Maxwell's equations and can provide high-accuracy solutions to model electromagnetic wave scattering from objects. Being able to calculate the scattered field from any surface geometry and considering the effect of the polarisation of the incident light, make rigorous models the most promising tools for complex light-matter interaction problems. The total intensity of the electric near-field scattering from a silicon cylinder illuminated by the transverse electric and transverse magnetic polarisation of the incident light is obtained using various rigorous models including, the local field Fourier modal method, boundary element method and finite element method. The intensity of the total electric near-field obtained by these rigorous models is compared using the Mie solution as a reference for both polarisation modes of the incident light. Additionally, the intensity of the total electric near-field scattered from a silicon sinusoid profile using the same rigorous models is analysed. The results are discussed in detail, and for the cylinder, the deviations in the intensity of the total electric field from the exact Mie solution are investigated.

A machine learning based framework to tailor properties of nanofiltration and reverse osmosis membranes for targeted removal of organic micropollutants

Nanofiltration (NF) and reverse osmosis (RO) membranes play an increasingly important role in the removal of organic micropollutants (OMPs), which puts higher demands on the customization of membranes suitable for OMPs removal based on the rejection mechanisms. Here, the pathways of OMPs-targeted optimization for membranes were constructed by using machine learning (ML) to capture the correlations between OMPs removal efficiency with properties of membranes and OMPs. Through expertise assistance and rigorous modeling methodology, an accurate and robust Extreme Gradient Boosting (XGBoost) model was established, which could well recognize the dominant rejection mechanisms of OMPs (i.e., the size exclusion effect and electrostatic interactions). An exemplary application to another dataset of several high-risk OMPs showed how the optimized model could be used to estimate the overall efficiency of OMPs risk control and, more importantly, to provide quantitative guidance on membrane properties for specific removal targets. The satisfying prediction results demonstrated the good generalization of the ML model and consequently its ability to sensitively define the ideal membrane properties for the targeted removal of these (and any other concerned) OMPs. This study provides a feasible and universal ML-based framework to achieve the tailored selection and design of NF/RO membranes for OMPs risk control.

Rigorous design and economic optimization of reactive distillation column considering real liquid hold-up and hydraulic conditions of industrial device

The liquid hold-up in a reactive distillation (RD) column not only has a significant impact on the extent of reactions, but also affects the pressure drop and hydraulic conditions in the column. Therefore, the liquid hold-up would be a critical design factor for RD columns. However, the existing design methods for RD columns typically neglect the influence of considerable amount of liquid hold-up in downcomers owing to the difficulties of solving a large-scale nonlinear model system by considering downcomer hydraulics, resulting in significant deviations from actual situation and even operation infeasibility of the designed column. In this paper, a pseudo-transient (PT) RD model based on equilibrium model considering tray hydraulics was established for rigorous simulation and optimization of RD plate columns considering the liquid hold-up both in downcomers and column trays, and a steady-state optimization algorithm assisted by the PT model was adopted to robustly solve the optimization problem. The optimization results of either ethylene glycol RD or methyl acetate RD demonstrated that assuming all the liquid hold-up of a stage belonged to the tray will cause significant deviations in the column diameter, weir height, and the number of stages, which leads to not meeting the separation requirements and even operation hydraulic infeasibility. The rigorous model proposed in this study which considers the liquid hold-up both on trays and in downcomers as well as hydraulic constraints can be applied to systematically design industrial RD plate columns to simultaneously obtain optimal operating variables and equipment structure variables.

Prediction Model for Electricity Energy Consumption and Pricing Using Xgboosts With L1 Regularization

The increasing global demand for electricity, driven by rapid industrialization, urbanization, and digitalization, necessitates more accurate and efficient predictive models for electricity generation and consumption. Traditional statistical techniques often fall short in incorporating the complex environmental factors influencing energy demand. This study presents an advanced machine learning model, leveraging XGBoost with L1 regularization, to improve the accuracy of electricity consumption and generation predictions. By integrating comprehensive datasets, including economic indicators and weather variables, the model addresses the critical gaps in existing forecasting methods. The research involved rigorous data pre-processing, feature engineering, and model validation using metrics such as RMSE, MAPE, and MAE. The model achieves a RMSE of 2.212 and MAPE of 2.393%, indicating good predictive performance. Effective data cleaning, feature engineering, and the application of PCA contributed to this result. The results demonstrate significant improvements in prediction accuracy, highlighting the potential of the proposed model to enhance energy management practices, optimize grid operations, and support the integration of renewable energy sources. This study provides a robust framework for future research and practical applications, contributing to the advancement of sustainable energy systems and informed policy-making. The findings underscore the importance of integrating diverse data sources and sophisticated machine-learning techniques to meet the growing challenges in the energy sector.

Social Networks and Digital Influencers in the Online Purchasing Decision Process

Social networks have become a significant part of people’s daily lives, particularly in the purchasing process. In this context, digital influencers have played an essential role in shaping consumers’ opinions. In this sense, studying the role of social networks and influencers in the online product purchase decision process was considered pertinent. We selected two technology adoption models to fulfill this purpose: the Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT). We conducted an exploratory study using a quantitative approach, with data being collected through a questionnaire distributed online. The statistical analysis of the empirical model employed a partial least squares structural equation modeling (PLS-SEM) technique. Analyzing the 135 responses allowed us to conclude that social networks are relevant in consumers’ purchase decision process. This research highlights the importance of the credibility of influencers, emotional connections with audiences, and the dynamics of the media in shaping consumer behavior. It highlights the strong influence of digital influencers on purchasing decisions and makes a methodological contribution with a rigorous empirical model. This study also suggests avenues for future research in order to deepen the understanding of influencer and social media marketing strategies.

Improved densification of SiCf/SiC composites by microwave-assisted chemical vapor infiltration process based on multifrequency solid-state sources excitation

This paper addresses the possibility of improving and controlling the temperature profile of a Ceramic Matrix Composite preform in a multiport Microwave-assisted Chemical Vapor Infiltration (MW-CVI) pilot-scale plant, based on a multifrequency excitation of the reactor by three solid-state sources. The choice of the most suited excitation frequencies is guided by rigorous numerical modeling of the reactor loaded by the sample, solving the coupled electromagnetic and thermal problems in a self-consistent way. The resulting infiltration front and residual porosity have been evaluated by a pseudo1D model, along the sample thickness, according to the previously computed heating pattern. The practical implementation and validation of the proposed technique are illustrated with MW-CVI trials on 10×10×0.3 cm3 SiCf/(SiC/BN)3/SiC preforms manufactured by Filament Winding method. The obtained results confirm the inside-out densification pattern, expected from the sample volumetric heating, along with an extension of the densification region by a factor of 2–3.

Rigorous Models for Vapor Diffusion in Polymers with Immobilization and Chemical Potential Gradients.

Two key phenomena─immobilization and concentration-dependent mixing free energies─simultaneously alter the sorption thermodynamics and diffusion of vapors in materials. This interrelation is leveraged to fit a unified model simultaneously capturing both sorption dynamics and the equilibrium isotherms. This transport model incorporates quasi-equilibrated immobilization reactions and considers Fick's law rigorously in terms of chemical potential gradients rather than concentration gradients. Five material case studies are discussed with varying characteristics, including fillers that provide sites for surface sorption, pores for capillary condensation, and apparent clustering or pooling at high vapor concentrations. Each case study illustrates that intrinsic diffusivity is constant, while the effective diffusivity changes predictably because of immobilization or changing free energies of mixing.