Nitrogen-enriched porous organic polymers for high-performance CO2/N2 separation in mixed-matrix membranes

The development of high-performance mixed matrix membranes (MMMs) for CO2/N2 separation requires fillers that simultaneously enhance gas permeability and selectivity without introducing interfacial defects. In this study, we report the synthesis of nitrogen-containing porous organic polymers (POPs) via a one-step Friedel-Crafts polymerization, designed to introduce CO2-philic functional groups directly into the polymer backbone while preserving intrinsic porosity. Structural and gas sorption analyses confirmed that the synthesized POPs exhibit high microporosity, surface area, and nitrogen content, which together enhance CO2 adsorption affinity. When incorporated into Matrimid-based MMMs, the POPs significantly improved CO2 permeability and CO2/N2 selectivity, with the pp-tpta filler (containing the highest nitrogen content) delivering the most pronounced performance enhancement. Further optimization using a high-permeability 6FDA-DAM polyimide matrix yielded a membrane with a CO2 permeability of 1967 Barrer and a selectivity of 33.4 at 20 wt% pp-tpta loading, surpassing the 2008 Robeson Upper Bound. Solubility-diffusivity analyses, supported by both experimental measurements and molecular dynamics simulations, revealed that the pp-tpta filler enhances CO2/N2 separation primarily by increasing CO2 solubility and diffusivity within the membrane matrix. These results underscore the potential of rationally designed, nitrogen-rich POP fillers as effective, scalable materials for advanced gas separation membranes.

Exploring the relationship between air quality and happiness in South Korea using artificial neural networks

This study investigates the relationship between air quality and subjective happiness across South Korean districts using artificial neural network (ANN)-based modeling. By aggregating the Korean National Assembly Futures Institute's happiness survey (2020-2021) data with the Korean Ministry of Environment's air quality data, among others, six major air pollutants were examined for their potential associations with the happiness ladder at the minuscule city level throughout South Korea. Complex non-linear patterns were observed. Among the pollutants, PM2.5 exhibited the most consistent negative association with the happiness ladder. The robust modeling and training strategies provide insights into the intricate relationships between air quality factors and the individual happiness ladder. The analysis effectively captures subtle relationships under fixed socioeconomic and happiness-related conditions, highlighting varying confidence intervals across multiple scenarios. These findings underscore the potential of ANN-based modeling in assessing the environmental factors of subjective happiness. Despite limitations related to the spatiotemporal scale of the annual happiness survey, this study contributes to the methods by applying deep learning techniques to infer the relationship between air quality and happiness, providing evidence that may inform environmental policymaking and urban sustainability strategies.

Surfactant-stabilized cyclopentane hydrate emulsions for removing tetramethylammonium hydroxide from semiconductor wastewater

This study presents an innovative hydrate-based wastewater treatment approach specifically designed for semiconductor wastewater containing tetramethylammonium hydroxide (TMAH), utilizing cyclopentane (CP)in-water emulsions stabilized by nonionic surfactants Span 80 and Vitamin E-TS. The stabilized emulsions significantly accelerated hydrate formation kinetics, reducing the induction time from 313.33 min to approximately 12.67 min (a 96 % decrease), thus greatly enhancing the process's energy efficiency. Structural analyses using powder X-ray diffraction (PXRD) and Raman spectroscopy confirmed that TMAH molecules at concentrations of 515 mg/L were effectively excluded from the hydrate structure, demonstrating no interference with hydrate crystalline structures or cage occupancy. Furthermore, incorporating Vitamin E-TS as an antiagglomerant promoted the formation of porous hydrate structures, increasing TMAH removal efficiency substantially from 45.51 % to 62.96 %, though reducing water recovery from 69.74 % to 61.64 %. Subsequent post-washing procedures further increased TMAH removal efficiency to 74.88 % but decreased water recovery to 49.24 % due to partial hydrate melting. These findings underscore the importance of balancing water recovery and contaminant removal efficiency, providing essential insights for optimizing hydrate-based wastewater treatment processes, and highlighting its potential as an energy-efficient and sustainable solution for semiconductor wastewater and other industrial wastewater containing persistent organic contaminants.

Spatiotemporal-dependent reliability analysis with adaptive sampling physics-informed neural networks

Balancing accuracy and computational efficiency remains a challenge for simulation-based time-dependent reliability analysis (TRA) under uncertainty. Traditional TRA methods often fall short in focusing solely on fixed hotspot(s) of complex engineering systems, neglecting the dynamic nature of potential failure regions, such as moving hotspots. To address these limitations, this paper proposes a spatiotemporal-dependent reliability analysis (STDRA) framework for engineering systems governed by partial differential equations (PDEs). Key contributions include: (1) resolving incompleteness by employing a physics-informed neural network (PINN) to calculate global performance across all spatiotemporal "spots" in the investigated PDE system; (2) enabling STDRA by deriving results through Monte Carlo simulations of the PINN-based framework, without the need for design of experiment (DoE) samples; and (3) enhancing accuracy and efficiency through a novel adaptive spatiotemporal sampling (ASTS) strategy, which optimally trains the PINN by focusing on critical spatial and temporal domains. The proposed ASTS-PINN-based STDRA framework is validated using a 2D isotropic elastic plate and a complex laser cladding process, showcasing its superiority over existing state-of-the-art TRA methods.

Light-Responsive Block Copolymer Particles with Persistent Shape Memory and Programmable Reconfiguration

Light-responsive polymeric particles provide a versatile platform that can undergo precisely programmed shape and color transformations, offering opportunities for advanced multi-level memory systems. We report a block copolymer (BCP) particle system that functions as a structural memory element by reversibly switching among distinct morphologies and retaining each state with long-term stability. The incorporation of hydrazone-based photoswitches into polystyrene-block-poly(2-vinylpyridine) (PS-b-P2VP) particles enables reversible and light-programmed transformations, governed by (E)/(Z) isomerization under dual-wavelength irradiation at 410 and 365 nm. The photoisomerization modulates the charge-transfer character of the N & horbar;Br interaction within P2VP domains, yielding three well-defined and distinct morphologies: lamellar ellipsoids (dark), networked lamellae (410 nm), and surface-wrapped discs (365 nm). These photoinduced morphologies can be reversibly switched over multiple cycles without detectable fatigue. Importantly, each programmed state persists as a metastable configuration over 30 days in the dark, retaining > 97% of its original morphology. Furthermore, the incorporation of domain-selective fluorescent dyes enables the system to provide real-time, color-coded visual readout of its encoded states via Forster resonance energy transfer modulation, opening new avenues for multi-level data storage with direct optical access.

Regional Stabilization and Estimation of Domains of Attraction for Discrete-Time T-S Fuzzy Systems via Fuzzy-modeled Membership Functions

This article addresses the problem of regional stabilization for discrete-time Takagi-Sugeno fuzzy systems using parallel distributed compensation output-feedback controllers. The goal is to ensure asymptotic stability and to maximize the estimate of the corresponding domain of attraction. The key contribution of this work is the use of a fuzzy modeling approach for the membership functions, enabling a polytopic representation based on the premise variables. Unlike conventional approaches, this method avoids the need for explicit bounds on membership function variations, thereby reducing conservatism in the stabilization conditions. The synthesis procedure is presented as an iterative algorithm based on linear matrix inequalities, consisting of two distinct phases: the first phase stabilizes the system, while the second phase aims to maximize the estimated region of attraction. Numerical examples are provided to demonstrate the advantages of the proposed technique over existing methods.

On-site microRNA detection with 'off-the-shelf' glucose meter empowered by chimeric probe connecting CRISPR/Cas13a activation to kinases-driven glucose phosphorylation

MicroRNAs (miRNAs) are promising biomarkers for cancer diagnosis due to their stability in body fluids and disease-specific expression profiles. However, current detection methods suffer from limitations including cumbersome workflows, heavy instrumentation for signal readout, or vulnerability in minimizing instrumentation. To address these challenges, we describe a novel point-of-care miRNA detection platform executable with "off-the-shelf", personal glucose meter (PGM), termed 'KEY-FACT (Kinases Ensemble-driven glucose phosphorYlation upon Fuel-Aided CRISPR acTivation)'. Upon recognition of target miRNA, a fuel-assisted toehold-mediated strand displacement reactions liberate guide RNAs (gRNAs) to activate Cas13a to cleave a chimeric reporter probe, producing 2 ',3 '-cyclic adenosine monophosphates (cAMP). Subsequent dephosphorylation and kinases ensemble-mediated phosphorylation/dephosphorylation cycles lead cAMP to consume a large amount of glucose. A user can immediately measure resulting glucose level change with PGM on the spot. This strategy allows sensitive, prompt detection of miR-135b, a gastric cancer (GC) biomarker, with a limit of detection (LOD) of 1.4 pM within 2 h. KEY-FACT is specific to the target miRNA and is applicable to body fluids such as human serum with dilution (95.2% < recovery rates <104.3%, coefficients of variation <= 13%). Owing to its simple probe design, KEY-FACT was readily expanded to detect another GC biomarker, miR-21, with comparable sensitivity (LOD = 1.5 pM). The proposed platform fulfills minimal instrumentation and thus enables cost-effective, field-deployable analysis, paving the way for practical, on-demand miRNA diagnostics.

An empirical study on bounced clicks versus multi-page clicks in display advertising

The click behavior of users has been frequently used to measure the effectiveness of online display advertising campaigns. This study categorizes clicks into bounced clicks, where no additional user action is involved after the click, and multi-page clicks, which generate additional actions after the click, resulting in a multi-page session, and then examines the distinctive effects of two different types of clicks. We investigated whether bounced clicks are effective in generating conversions and measured the varying effectiveness of the two types of clicks. Moreover, we examined which factors influenced the occurrence of multi-page clicks versus bounced clicks. Using individual-level clickstream data from an actual display advertising campaign, we found that bounced clicks had a significantly positive impact on conversion, but multi-page clicks were approximately twice as effective as bounced clicks. We also found that user-specific, contextual, and environmental factors critically influenced the occurrence of multi-page clicks versus bounced clicks.

Minimizing quantification uncertainty in nanoplasmonic platforms: Multifunctional MoS2-integration for precise biomarker determination

Despite growing interest in nanoplasmonic biosensors-particularly surface-enhanced Raman spectroscopy (SERS) platforms-their potential has been limited by high quantification variability rooted in poor uniformity. Previous approaches to address this, such as incorporating internal standards (ISs), often sacrificed sensitivity for uniformity or lacked a clear analytical basis for accurate quantification. Here, a novel approach of integrating MoS2 into a SERS platform is introduced, with a focus on mitigating spot-to-spot relative standard deviation (RSD) and improving quantification accuracy. While maintaining the well-known enhanced sensitivity of MoS2, the Raman signal from a uniform monolayer is utilized to calibrate signal variations. As a result, the platform achieves the lowest RSD (5.29%) among MoS2-based systems, while offering the highest level of sensitivity in rhodamine 6G (R6G) measurements. For albumin, the target proteinuria biomarker, MoS2-based normalization outperforms conventional wafer-based methods and achieves a 42% RSD reduction over non-normalization because the atomic thickness MoS2 enables precise plasmonic calibration. Furthermore, a consistent, exponential relationship between MoS2 signal intensity and albumin concentration is discovered. Quantification trends are consequently highly predictable, resulting in a 4.8-fold increase in data separability. This quantification approach is shown to be effective for albumin mixed in artificial urine under various laser conditions, highlighting the practical potential of our platform for early-stage monitoring of biomarkers.

Deep learning-based segmentation framework to decouple matrix deformation and crack evolution in shales using X-ray computed tomography

Hydro-mechanical loading on shale can cause not only deformation of matrix but also evolution of cracks. As they both have significant impacts on fluid flow and volumetric behaviors of shales in fundamentally different ways, it is important to decouple their respective contributions. This study develops a robust deep learning-based segmentation framework to decouple matrix deformation and crack evolution in shales using X-ray computed tomography (CT). A shale X-ray CT dataset was generated, while varying saturation (i.e., as-received, saturated, and oven-dried) and imaging conditions (i.e., X-ray source voltages of 100 kV, 150 kV, and inside an aluminum ring). Using this dataset, 13 deep learning architectures were benchmarked, taking the widely adopted U-Net as a baseline. Three-fold cross validation results showed that FRRN-A and FRRN-B outperformed U-Net, with mean intersection over union values close to 85%. Moreover, FRRN-B with cross-entropy loss and a crack weight of 100 was found to be the optimal model for crack volume estimation based on their accuracy and robustness. This optimal model demonstrated its capability in visualizing 3D crack networks and decomposing bulk volumetric strains into matrix and crack contributions during wetting and drying. The matrix deformation appeared to be reproducible regardless of the initial crack configuration, while the crack evolution contributed to about 11-19% of the bulk strains and highly variable, highlighting the localized nature of crack opening/closure. By isolating matrix strains, the proposed framework provides an automated and interpretable platform that bridges observations of hydro-mechanical experiments on shale and continuum-scale constitutive modeling.