The predictive modeling of one-dimensional (1D) supramolecular assemblies depends on the identification of stable, low-energy configurations-a task frequently hindered by the vast configurational space and highly multimodal energy landscapes-inherent to non-covalently bonded systems. In this study, we introduce the π -stack optimizer, a modular, open-source framework designed to generate energetically favorable 1D stacking motifs directly from a single monomeric building block with minimal computational overhead. The framework systematically explores high-dimensional space by globally sampling coupled rigid-body translational and rotational degrees of freedom, while optionally accounting for intramolecular torsional flexibility. Extensive validation across 14 chemically diverse supramolecular systems demonstrates that the framework reliably identifies stable low-energy configurations, including systems stabilized by directional intermolecular hydrogen-bonding networks. Comparative analyses indicate that, while algorithms differ in robustness and efficiency, they consistently converge to nearly identical low-energy minima. Coupled with automated hyperparameter optimization, the π -stack optimizer serves as a scalable and practical tool for generating high-quality initial structures for advanced quantum-mechanical calculations and molecular simulations. The π -stack optimizer utilizes global optimization algorithms within a multidimensional parameter space defined by rigid-body translations, rotations, and intramolecular degrees of freedom. By integrating the molecular symmetry constraints, the framework minimizes redundant exploration of equivalent configurations. Configurational sampling was performed using multiple metaheuristic algorithms, including Particle Swarm Optimization, Genetic Algorithms, Grey Wolf Optimizer, and a hybrid PSO-Nelder-Mead approach, with convergence governed by early-stopping criteria. Candidate stack geometries were evaluated using semi-empirical quantum-mechanical energy calculations, primarily employing the GFN2-xTB Hamiltonian. The objective function combines intermolecular binding energies with quadratic steric-penalty terms to bypass unphysical configurations and target chemically realistic minima. Developed in Python with a modular architecture, the framework features parallelized execution and automated hyperparameter optimization via Optuna, providing a flexible, open-source tool for efficient generation of supramolecular stacks with minimal user inputs.
All-solid-state lithium batteries (ASSLBs) have garnered worldwide attention as promising next-generation energy storage technologies owing to their high energy densities and enhanced safety. However, their long cyclability remains unsatisfactory for practical application, primarily due to poor solid-solid interfacial contact. A high stack pressure is often required during operation, hampering their commercialization. This perspective presents a fundamental understanding of the roles of stack pressure in ASSLBs and analyzes the intrinsic challenges to achieve optimal battery performance under low-stack-pressure conditions. Recent advances for reducing high-stack-pressure demands are summarized from the point of views of solid electrolyte/ active electrode material design and interface engineering. Finally, the perspective layouts the key challenges and prospects for future breakthroughs to achieve low-stack-pressure ASSLBs. It is hoped that the material-centered solutions highlighted in this perspective will inspire meaningful progress in future advanced battery systems.
Developing highly efficient photocatalysts to achieve near-infrared (NIR) light-driven CO2 reduction is of great significance yet remains a great challenge. In this article, we found that BODIPY-based π frameworks can serve as good NIR photocatalysts, achieving highly efficient CO2 reduction to HCOO- coupled with benzyl alcohol oxidation to benzaldehyde in pure water. More impressively, the photocatalytic performance of these π frameworks can be improved by regulating π-π stacking interactions, among which the optimized π-pyrenyl (π-PY) framework with the most π-π stacking interactions exhibits HCOO- production rates of 4045 and 1693 µmol g-1 h-1 in >99% and 15% CO2 atmospheres, respectively. π-PY, thus, stands for the current state-of-the-art photocatalyst in NIR-light-driven CO2 reduction. Experiments together with theoretical calculations demonstrated that the high photocatalytic activity of π-PY could be due to the abundant π-π stacking interactions, which accelerates charge separation and transfer, as well as prolongs carrier lifetime and reduces energy gap. This work gives new insights in understanding the contribution of π-π stacking interactions to photocatalysis, and introduces a new strategy for developing efficient photocatalysts for NIR-light-driven CO2 reduction.
Heart arrhythmias are associated with serious cardiovascular diseases and can result in fatal outcomes if not diagnosed early. Electrocardiograms (ECG) are generally used to diagnose heart arrhythmias. Prior studies employ deep learning architectures including Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and Transformer based methods to diagnose arrhythmias from ECG signals, achieving promising results. However, most of the deep learning models when used individually exhibit inherent limitations. CNNs can learn spatial features effectively but is limited in learning temporal characteristics. LSTMs capture temporal characteristics, but struggle with capturing long-range dependencies. Transformer models are effective at modeling long-range dependencies but they are often prone to overfitting. To address the limitations of individual models while leveraging their feature learning capabilities, this work proposes a two-level stacked ensemble framework for ECG arrhythmia classification. At the first level, deep learning models including a Deep Neural Network (DNN), CNN, LSTM, and Transformer models are trained as base learners while at the second level, the predictions generated by the base learners are combined and passed to the Multilayer Perceptron (MLP) which acts as the meta-learner. MLP learns how to weigh the predictions of the first level models to generate improved final predictions. The combination of these models in a stacked ensemble framework helps exploit the strengths of all models, enabling fusion of spatial, temporal, and long-range dependency features from the ECG signal. The proposed framework is evaluated on the MIT-BIH and INCART arrhythmias databases to classify the input ECG signals into five arrhythmia categories. Comparison against the base models and prior approaches demonstrates that the proposed stacked ensemble model outperforms the base models and state-of-the-art (SOTA) techniques, achieving an F-score of 99.79% on MIT-BIH and 99.62% on INCART dataset.
Developing an artificial photosynthesis technology that converts earth-abundant resources into fuels using metal-free photocatalysts with water as an electron donor is challenging. A few active organic semiconductors require harsh synthesis conditions and expensive reagents. Here, we report that a 1,4-conjugated linear polymer, poly(2,3-dihydroxynaphthalene) [poly23DHN], synthesized by the polymerization of inexpensive 23DHN at room temperature, behaves as a heterogeneous catalyst for artificial photosynthesis of hydrogen peroxide (H2O2) in water with O2. The polymer is soluble in polar organic solvents but insoluble in water. The insolubilization of the polymer leads to self-assembly of a hydroquinone-quinone donor+acceptor semiconducting architecture via H-bonding and π-stacking interactions. The formed powders catalyze water oxidation and O2 reduction under visible light up to ∼700 nm. Photocatalyst sheets easily prepared by dropping a polymer-dissolved solution onto a substrate stably generate H2O2. This polymer design that creates semiconducting structure through spontaneous H-bonding and π-stacking offers a scalable, easy-to-handle, cost-effective approach for solar fuel generation.
Understanding the hydrochemical evolution and primary chemical components governing groundwater quality in industrial regions is crucial for sustainable groundwater management. This study established an integrated analytical framework by combining conventional hydrogeochemical analyses with a Stacking ensemble machine learning model. The framework was applied to a dataset comprising 26 hydrochemical parameters measured from 100 groundwater samples collected in the heavily industrialized Jiyuan Plain, China. Results show that the shallow groundwater is featured by types of HCO₃-Ca·Mg and SO₄·Cl-Ca·Mg, with major ions originating from mineral dissolution of halite, carbonate, gypsum, and silicate. Moreover, the results of ion ratios, PCA (i.e., principal components analysis), EWQI (i.e., Entropy weighted quality index) reveal that hydrochemistry of the shallow groundwater is shaped by the interaction between natural hydrogeochemical processes (e.g., mineral weathering) and intense anthropogenic activities (industrial and agricultural discharges). Most importantly, the combining application of EWQI and Stacking model quantitatively identified manganese and nitrogen species as the foremost drivers of groundwater quality degradation, a key finding that directly challenges conventional assumptions based on the region's prominent lead industry, which was found to contribute insignificantly to overall groundwater pollution. Notably, the hybrid traditional and data-driven framework successfully uncovered key pollutants in composite pollution areas, offering a transferable methodology for quantitative source apportionment in other industrial regions worldwide. For water management, the findings of this study suggested a targeted strategy for groundwater quality controlling. Prioritization must be given to investigating the origins of manganese (geogenic vs. industrial) and effectively intercepting the inputs of nitrogen/organic matter pathways.
Cholesteric liquid crystal polymer network (CLCN) mirrors featuring a broad reflection band have attracted considerable interest for use in optical filters and decorative applications. To date, however, the development of mirrors with encryption capabilities remains unexplored. In this work, a series of chiral additives were synthesized from isosorbide and 4'-n-alkyl-(1,1'-biphenyl)-4-carboxylic acids. Using polymerization-induced chiral additive diffusion, we then prepared a set of CLCN films exhibiting broad reflection bands. By stacking these films with an optically clear adhesive, a broadband CLCN mirror with high reflectance was fabricated. Furthermore, a patterned half-wave plate was fabricated using a nematic liquid crystal polymer network (NLCN) in combination with a CLCN. A modified mirror was constructed by incorporating the patterned half-wave plate during the stacking process. When illuminated with circularly polarized light, this configuration revealed vivid colored images. These results demonstrate the potential of such mirrors for encryption purposes.
This is a narrative conceptual paper, not a systematic review. Ultrasound-guided fascial hydrorelease (FHR) has been reported to provide sustained pain relief in patients with chronic musculoskeletal pain; however, its underlying biological mechanisms remain incompletely understood. In this paper, we propose the "Fascial Memory Reset Hypothesis" as an integrative framework linking mechanobiology, extracellular matrix (ECM) remodeling, peripheral nociception, microcirculatory dynamics, and ultrasound imaging findings. Mechanobiological research has demonstrated that increased tissue stiffness activates YAP/TAZ signaling, promoting fibroblast activation, ECM deposition, and mechano-epigenetic regulation. These mechanically driven processes can stabilize pathological tissue phenotypes without DNA sequence alterations. The "Fascial Memory Reset Hypothesis" proposes that targeted mechanical interventions such as FHR may partially reverse these mechanically maintained states by restoring tissue mobility and modifying stiffness-dependent mechanotransduction. We propose that "stacking fascia" (observed as layered hyperechoic bands on ultrasound) represents the macroscopic structural phenotype of mechano-epigenetic memory formed through sustained mechanical stress. Integrating molecular mechanotransduction pathways, mechano-epigenetic mechanisms, neural sensitization, and vascular factors, we propose that FHR may hypothetically partially normalize pathological fascial states by mechanically restoring tissue mobility and modifying stiffness-dependent signaling. Although direct molecular evidence of the effect of FHR in human fascia remains limited, this hypothesis provides a biologically plausible link between mechanical stress, ultrasound-visible structural alterations, and sustained clinical improvement.
Tissue harmonic imaging (THI) has been widely used to improve ultrasound imaging quality, and further enhancing its performance remains a research focus.
Approach: In this work, we propose a dual-frequency mixed harmonic imaging (DF-MHI)method based on a stack-layer dual-frequency ultrasound transducer (SL-DFUT) to further enhance the harmonic imaging signal-to-noise ratio (SNR). This method utilizes the overlapping sound fields of the SL-DFUT to generate difference and sum frequency harmonics at lower voltages. The method not only improves the imaging quality of harmonic imaging but also reduces the ultrasound system's voltage requirements. To validate the effectiveness of the DF-MHI, we developed a DF-MHI imaging platform capable of generating both DF-MHI and THI images. 
Main results: The phantom imaging results show that, under low high-frequency acoustic pressure, THI fails to generate effective harmonic signals in highly reflective regions, whereas DF-MHI successfully generates these harmonic signals. When the high-frequency acoustic pressure is five times that of the low-frequency pressure, the visual difference between THI and DF-MHI became less pronounced than at lower pressure ratios, but DF-MHI still maintained clear quantitative advantages. Specifically, the contrast ratio
(CR) increases by 46.53%, the contrast-to-noise ratio (CNR) improves by 47.26%, and thegeneralized contrast-to-noise ratio (gCNR) rises by 34.87%.
Significance: The DF-MHI provides higher image quality than conventional harmonic imaging under the same excitation voltage.
Autonomous odor source localization remains a challenging problem for aerial robots due to turbulent airflow, sparse and delayed sensory signals, and strict payload and computation constraints. While prior unmanned aerial vehicle (UAV)-based olfaction systems have demonstrated gas distribution mapping or reactive plume tracing, they rely on predefined coverage patterns, external infrastructure, or extensive sensing and coordination. In this work, we present a complete, open-source UAV system for online odor source localization using a minimal sensor suite. The system integrates custom olfaction hardware, onboard sensing, and a learning-based navigation policy that we train in simulation and deploy on a real quadrotor. Through our minimal framework, the UAV is able to navigate directly toward an odor source without constructing an explicit gas distribution map or relying on external positioning systems. We incorporate vision as an optional complementary modality to accelerate navigation under certain conditions. We validate the proposed system through real-world flight experiments in a large indoor environment using an ethanol source, demonstrating consistent source-finding behavior under realistic airflow conditions. The primary contribution of this work is a reproducible system and methodological framework for UAV-based olfactory navigation and source finding under minimal sensing assumptions. We elaborate on our hardware design and open-source our UAV firmware, simulation code, olfaction-vision dataset, and circuit board to the community.
The rapid expansion of urban living has led to increased road congestion, posing significant challenges for maintaining driver attention and ensuring road safety. This study investigates advanced machine learning techniques to assess and predict driver focus by analyzing key metrics such as concentration scores, reaction times, and stress management capabilities. A comprehensive evaluation of various machine learning models was conducted, including Logistic Regression (LR), Linear Discriminant Analysis (LDA), Ridge Classifier, Decision Tree (DT), Light Gradient Boosting Machines (LightGBM), Naive Bayes (NB), Support Vector Machines (SVM), Random Forest (RF), AdaBoost, XGBoost, CatBoost, K-Nearest Neighbors (KNN), and Stacking. To optimize performance, a metaheuristic optimization algorithm was employed for precise hyperparameter tuning. The novelty of this research lies in the introduction of a novel hybrid machine learning algorithm, "CatBoost+Stacking." By integrating the strengths of CatBoost within a stacking framework, this new model significantly enhances the accuracy of predicting driver concentration levels. Experimental results demonstrate that the "CatBoost+Stacking" model outperforms existing baseline models, offering a more effective approach for monitoring driver behaviour. These findings provide practical insights for developing proactive road safety strategies and reducing accidents, highlighting the transformative potential of hybrid models in creating safer driving environments.
Sliding ferroelectricity in bilayer hexagonal boron nitride (hBN) provides an atomically sharp, nonvolatile knob for engineering interfacial polarization in van der Waals heterostructures. Here, we combine time-dependent density functional theory with nonadiabatic molecular dynamics to study how stacking-dependent ferroelectric polarization in bilayer hBN can be used to tune charge redistribution, electron-phonon coupling, and nonradiative carrier recombination in MoSe2/hBN and WSe2/hBN heterostructures. Switching the bilayer hBN stacking between nonpolar AA' and ferroelectric AB/BA reverses the interfacial potential step and charge-transfer direction, which modulates the nonadiabatic coupling, electronic decoherence, and recombination kinetics. As a result, carrier lifetimes can be tuned over more than one order of magnitude, from 1.46 to 74.6 ns, by choosing the stacking sequence and TMD species. Fourier analysis of band-gap fluctuations identifies mode-selective phonon coupling: long-lived configurations are associated with spectra dominated by low-frequency interlayer shear and breathing modes, whereas short-lived ones show enhanced contributions from intralayer optical phonons of A1', A2″, and E' symmetry that more efficiently modulate the band edges. These results establish sliding-ferroelectric proximity engineering as an effective strategy for programming interfacial charge dynamics in 2D heterostructures and provide microscopic design rules for reconfigurable ferroelectric semiconductor platforms for future optoelectronic and information devices.
Background: Hospital readmission among patients with diabetes remains a major challenge for healthcare systems, contributing to increased costs and adverse patient outcomes. Early identification of high-risk patients may support targeted interventions and improved care management. Objectives: This study aimed to develop and rigorously evaluate a machine learning framework for predicting 30-day hospital readmission in patients with diabetes using a large multi-institutional clinical dataset. Methods: The study utilized the Diabetes 130-US Hospitals dataset from the UCI Machine Learning Repository, comprising 101,766 hospital encounters. Data preprocessing included missing-value handling and feature engineering. Several machine learning models were evaluated, including Logistic Regression, Random Forest, XGBoost, and LightGBM, alongside a stacking ensemble model. Model performance was assessed using nested cross-validation (5 outer folds, 3 inner folds), probability calibration via Platt scaling, and statistical robustness through 1000 bootstrap resamples. Clinical utility was evaluated using decision curve analysis and clinical impact curves, while SHAP analysis was applied for model interpretability. Results: The stacking ensemble model achieved a nested cross-validated ROC-AUC of 0.664 and a calibrated AUC of 0.688, with a Brier score of 0.094. Risk stratification demonstrated a clear gradient between low- and high-risk groups, and decision curve analysis indicated positive clinical net benefit across relevant decision thresholds. Conclusions: The proposed machine learning framework provides a robust and clinically interpretable approach for predicting 30-day hospital readmission in diabetic patients, with potential utility for supporting clinical decision-making and care management.
The development of tight heavy-oil reservoirs is severely hampered by the high viscosity and poor mobility of crude oil caused by strong intermolecular stacking interactions among asphaltenes, coupled with the substantial adsorption loss and inadequate deep transport capacity of conventional displacement agents. By targeted penetrant delivery, a novel nanoemulsion system with a well-defined "core-shell" architecture was synthesized to address these critical challenges. The physicochemical properties, stability and oil displacement performance were evaluated. The prepared nanoemulsion exhibited an ultrasmall and uniform particle size distribution between 10 nm and 20 nm. It also demonstrated exceptional dispersibility in aqueous media and remarkable thermal and salinity stability under reservoir conditions. Furthermore, an ultralow critical micelle concentration of approximately 0.01% could be achieved and the oil-water interfacial tension was reduced to 7.3 × 10-2 mN/m, significantly outperforming the conventional surfactant AES. Core flooding tests revealed that the proposed nanoemulsion enhanced oil recovery by 37.1% and attained a displacement efficiency of 68.9% in oil-wet capillary models. Molecular dynamics simulations further elucidated the underlying synergistic mechanism. The hydrophilic shell minimized adsorption on rock surfaces, facilitating deep migration within nanoporous channels. The hydrophobic core, containing terpinene as a penetrant, effectively disrupted the π-π stacking of asphaltenes due to its nonplanar molecular configuration. This disruption transformed the asphaltene aggregates from a tightly packed state to a dispersed state, resulting in substantial viscosity reduction. This work elucidated the mechanism of asphaltene aggregate disruption by nanoemulsions at the molecular level, offering a promising and theoretically grounded strategy for the efficient exploitation of tight heavy-oil reservoirs.
Sustainable agriculture has been greatly challenged by the problems of dense canopies that result in extreme occlusion, scale disparities and imbalances of classes, making it difficult to detect small and overlapping weeds in real field conditions. Actual blackgram fields in Karnataka, India, were used to generate a dataset that featured a high level of crop to weed contest, and exhibited a significant amount of scale diversity, with 75 percent of objects comprising less than 0.8 percent of the image space. To address these challenges, the paper presents a better Faster R-CNN framework that adds five modules, i.e. Spatial Attention (SA), Multi-Scale Fusion (MSF), Context-Aware RoI, Shape-Aware Prediction and Adaptive Non-Maximum Suppression (ANMS). The research shows that with larger architectural complexity, there is no guarantee of better performance through an extensive ablation study involving 32 model configurations. The best two-module combination (SA + ANMS) obtained the highest F1 -score of 0.9547, precision of 0.9439, recall of 0.9658, and a mean Intersection over Union (mIoU) of 0.9445, and inference speed of 4.42 FPS, which was higher than the full five-module version. It is important to note that only 12.5% of configurations were better than the baseline, highlighting the fact that too much module stacking may hurt performance. The analysis shows that there is a high positive synergy between SA and ANMS and that the MSF module competes slightly on features. The novelty of this work lies in the systematic exploration of how various enhancement modules can be interacted to function as a single detection framework and show that the selective combination of modules can produce a better outcome than random stacking. Especially, the finding of a complementarity, optimum minimum combination of SA + ANMS confirms that the efficiency-based design is used, not complexity-based expansion. It provides informative insight on designing efficient detection system for precision agriculture applications by revealing the impact of wise module selection instead of architecture complexity on obtaining consistent weed detection against occlusion and inter-class inconsistencies.
There is an urgent need to achieve a large second-harmonic generation (SHG) response in ultraviolet nonlinear optical (UV NLO) crystals to advance UV laser technologies. SHG efficiency depends on the alignment and properties of functional groups, particularly π-conjugated units with high hyperpolarizability. Layered structures are widely recognized as an optimal template for densely preorganizing π-conjugated units into orderly arrays, yet the interlayer linkers are typically metal cations that couple adjacent layers through largely non-directional ionic interactions, often producing random even antiparallel stacking that cancels macroscopic SHG. Herein, we propose a Reverse Weak Polarity-Induced Ordered-layer Control (RPIO) strategy, in which a "layer→linker→layer" polarity-transfer pathway-driven by dipole-dipole interactions-enforces consistent alignment of polar layers. Guided by this strategy, three isostructural compounds, RE(C3H2O4)NO3·4H2O (RE = Y, Gd, Lu), are synthesized, featuring polar [ RE ( C 3 H 2 O 4 ) ( H 2 O ) 4 ] ∞ + ${[\text{RE}(\text{C}_3 \text{H}_2 \text{O}_4){(\text{H}_2\text{O})}_4]}^{+}_{\infty}$ layers coherently aligned induced by NO3 - linkers. All compounds exhibit large SHG responses (8.5-9.5× KDP), wide band gaps (∼4.13 eV), sizable birefringences (∼0.135 at 589.3 nm), and favorable growth habits. By explicitly engineering interlayer connectivity, this work establishes a predictable and controllable route to coherently stack polar layers and thereby maximize SHG in layered UV NLO materials.
Automating the layer-by-layer separation of stacked fabrics remains a major bottleneck in garment manufacturing due to the high deformability of textiles and the difficulty of isolating the top layer without disturbing adjacent layers. To address this challenge, this work proposes a rotational-pinch-inspired layered grasping method and a soft pneumatic gripper capable of reproducing the coordinated pressing-rotating-pinching behavior observed in human fingers. The gripper integrates a cavity-based pneumatic actuation module and a mechanical torsion module that collaboratively regulate the fingertip opening distance, normal force, and rotation angle-three key parameters governing layered fabric separation. A mechanical analysis establishes the relationship between these parameters and the evolution of shear deformation that triggers interlayer detachment. Experiments including parameter-impact, adaptability, and stability tests were conducted using six representative garment fabrics with diverse physical properties. Results demonstrate that the proposed grasping method enables nondestructive, continuous, and stable separation under different stacking layers and grasping positions, achieving success rates exceeding 96.7% on most fabrics. Overall, this work provides reliable technical support for garment manufacturing and is expected to facilitate the transition toward more efficient, precise, and intelligent production.
Tetracycline antibiotics are increasingly detected in aquatic environments because of their ecological risks and persistence, while conventional wastewater treatment processes are often insufficient for their effective removal from water. Here, we introduce a novel 3D graphene oxide-based nanocomposite that stacks Cu-NPs and amino-functionalized MIL-101(Fe) (denoted by Cu/NH2-MIL-101(Fe)@GO) to effectively remove tetracycline (TC) and oxytetracycline (OTC) from environmental water samples. XPS, XRD, TEM, SEM, and FTIR analyses were conducted to characterize the structure and surface morphology of the Cu/NH2-MIL-101(Fe)@GO nanocomposite. Overall, it was confirmed that GO, NH2-MIL-101(Fe), and Cu-NPs were successfully incorporated, resulting in a porous material with high access to Cu-related sites as well as oxygen- and nitrogen-based functionalities (such as amino-, hydroxy-, and carboxy-groups). This hybrid system facilitates the adsorption by complementary mechanisms like surface complexation/chelation at Cu and Fe centers with the pH-dependent tetracycline species in electrostatic interactions, hydrogen bonding, π-π stacking, and molecule confinement in the metal-organic framework (MOF) pores, and by the synergistic effects at the GO-MOF(Fe)-Cu junction interfaces. The batch adsorption studies showed that the quick and efficient uptake of the two antibiotics at pH 6.5, with removal rates of 99.65-99.83%, was achieved by 15.0 mg of Cu/NH2-MIL-101(Fe)@GO at an initial concentration of 20 ppm in 40 min at 25 °C. Equilibrium data were found to be well-fitted by the Langmuir isotherm (R2 = 0.908-0.909), suggesting monolayer-dominated adsorption with the maximum capacity of 769.8-775.2 mg g-1. The adsorption kinetics was well-described by the pseudo-second order model (R2 = 0.9641-0.9749), which agreed with the strong binding between the tetracyclines and active sites of the nanocomposite. The main novelty of this work consists of the design of a single recoverable platform integrating GO-based preconcentration, pore accessibility of NH2-MIL-101(Fe), and Cu-driven complexation, which led to the strong removal of tetracyclines under a relevant range of water conditions. These findings demonstrate that Cu/NH2-MIL-101(Fe)@GO could serve as a promising high-efficiency and potentially reusable adsorbent for removing tetracycline from aqueous solution, which provides a more sustainable approach for pharmaceutical wastewater treatment.
Embedded cryptography stands or falls on entropy quality, yet small devices have few trustworthy sources and little tolerance for heavyweight protocols. We build a Quantum Entropy as a Service (QEaaS) system that moves QRNG-derived entropy from a Quantis device to ESP32-class clients over post-quantum-secured channels. On the server side, the design exposes two paths: direct quantum entropy through a custom OpenSSL provider and mixed entropy through the Linux system pool. On the client side, we extend libcoap's Zephyr support, integrate wolfSSL-based DTLS 1.3 into the CoAP stack, and add a BLAKE2s entropy pool that preserves the standard Zephyr extraction interface while introducing an injection API for server-provided entropy. Benchmarks on ESP32 hardware, targeting 100 iterations per configuration, show that ML-KEM-512 completes a DTLS 1.3 handshake in 313 ms on average without certificate verification, 35% faster than ECDHE P-256. Pairing ML-KEM-512 with ML-DSA-44 lowers the mean to 225 ms. Certificate verification adds roughly 194 ms for ECDSA but only 17 ms for ML-DSA-44, so the fully post-quantum configuration remains 63% faster than classical ECDHE P-256 with ECDSA even under full verification. Local BLAKE2s pool operations stay below 0.1 ms combined. On this platform, post-quantum key exchange and authentication are not only feasible; they are faster than the classical baseline.
The PGPR strain of Bacillus amyloliquefaciens MHR24 (MHR24) was recently reported as a strong biocontrol strain. In this study, MHR24 was used to investigate phyllosphere effects during inoculations of tomato leaves (Solanum lycopersicum L.). When MHR24 was inoculated on foliar tissue, it caused apical chlorosis symptoms at 3-6 days after infiltration or submersion, which suggests that the bacterium may adopt a potentially pathogenic lifestyle in the phyllosphere. In order to detect the MHR24 interaction with the plant, it was stained with the commercial fluorophore 8-hydroxypyrene-1,3,6-trisulfonic acid trisodium salt, selected from a pyrene series bearing diverse functional groups, based on several in vitro staining assays. Fluorescence used as a detection signal was observed by LSCM mainly in the vascular bundles, suggesting that rhizobacteria may preferentially colonize these tissue regions. Molecular docking, performed by analyzing the possible interactions between the outer membrane protein assembly factor BamB of the family protein B. amyloliquefaciens and the fluorophore, indicates that hydrogen bonds with serine 126 (SER126), serine 182 (SER182), isoleucine 180 (ILE180), and tryptophan 66 (TRP66), charges attraction and π-stacking with TRP66, and non-bonded attractions with leucine 224 (LEU224) can occur, which likely gives rise to a stable complex. These results are important in view of the application of MHR24 as part of a sustainable approach for increasing tomato crop production.