Conventional Limit Research Articles

Implementation of phosphonium salt for high-performance supercapacitors from room to ultra-low temperature conditions

Conventional supercapacitors encounter limitations in operating voltage and performance at low temperatures due to poor ionic conductivity and diminished interfacial dynamics in electrolytes. In this study, we synthesized the phosphonium salt 5-phosphinaspiro[4.4] nonane tetrafluoroborate (PSNBF4) for the first time. We extensively characterized the physical and electrochemical properties of PSNBF4 in acetonitrile (AN) and propionitrile (PN) as electrolytes, assessing their performance in supercapacitors at room temperature and − 40 °C. The results demonstrate PSNBF4 electrolytes exhibit high solubility, outstanding ionic conductivity (1 M PSNBF4/AN: 49.8 mS cm−1; 1 M PSNBF4/PN: 27.5 mS cm−1), and high electrochemical stability, contributing to good capacitance retention after 500 h of floating tests at 25 °C. Supercapacitors using PSNBF4/AN retained 78 % of their capacitance at 3.1 V, whereas those with PSNBF4/PN maintained 68 % at 3.2 V. Impressively, these supercapacitors performed exceptionally well at −40 °C, displaying excellent cycle stability and high capacitance retention at elevated voltages. Supercapacitors with PSNBF4/AN retain 96.6 % of their capacitance at 3.2 V, while PSNBF4/PN retained 93.4 % of their capacitance at 3.4 V after floating for 500 h. These results demonstrate PSNBF4-based supercapacitors can operate effectively at high voltages in both room and extremely low temperatures, addressing a significant challenge in commercial supercapacitor applications.

Aberration correction in complex media over multiple isoplanatic patches by exploiting their spatial correlation

Optical imaging in complex media, such as biological tissues, poses significant challenges due to the presence of aberrations and scattering that distort wavefronts and degrade image quality. Wavefront correction has emerged as a crucial technique for mitigating these effects. Recent advances, such as adaptive optics, wavefront shaping, and matrix-based methods, have made significant strides in correcting for wavefront distortions to achieve high-resolution imaging. However, current approaches face challenges when addressing spatially varying distortions and small isoplanatic patches, where the corrections lose reliability due to the rapid spatial variation of distortions. To address this limitation, we introduce a method that exploits the spatial correlation of wavefront distortions across neighboring isoplanatic patches. This approach, termed "inward-outward progression", enables a more reliable wavefront correction across multiple small patches. The process involves two key steps: (1) the inward progression, where a small zone surrounding a pixel is optimized while progressively shrinking the optimized region, and (2) the outward progression, where the wavefront correction phases of the corrected zone serve as the initial guess to optimize adjacent areas, extending the correction from the small zone at the inward progression until the entire field of view is covered. We validate this method by imaging a USAF resolution target under 700-µm-thick chicken breast. Our approach shows a significant improvement in image quality and superior performance over existing techniques, paving the way for noninvasive imaging where conventional methods face substantial limitations due to the spatially varying aberrations and scattering.

Nitrogen substitution of bilayer penta-carbides: high solar-to-hydrogen conversion efficiency and excellent electrocatalytic activity for water splitting.

The shortage of fossil energy and environmental crises are two important issues in the 21st century, and the search for alternatives to fossil fuels, e.g. H2, is crucial. Herein, based on penta-germagraphene p-Ge2C4, bilayer penta-carbides of p-Ge4C8 and nitrogen substituted materials p-Ge4NnC8-n are proposed. The results indicate that gradually replacing the carbon atoms on the surface of p-Ge4C8 with nitrogen atoms can change the surface activities and electronic structures, and this is beneficial for both photocatalysis and electrocatalysis. The vertical intrinsic electric field in the two-dimensional materials can enhance photocatalytic water splitting of p-Ge2C4/Ge2N2C2 and break the conventional limitation of 1.23 eV for the band gap of photocatalysts. Therefore, solar energy conversion efficiency can reach 31%. The smaller effective mass and deformation potential of p-Ge2C4/Ge2N2C2 along the x and y directions lead to a huge electron mobility (425.64 × 103 cm2 V-1 s-1) at room temperature. Moreover, the overpotential for the hydrogen evolution reaction of p-Ge2C4/Ge2N2C2 is 0.074 V, which is beneficial for electrocatalysis. The external strain and electric field can also enrich the electronic properties. Surface modification achieved by introducing the nitrogen atoms can improve the catalytic performance of penta-carbides.

A deep learning method for the recovery of standard-dose imaging quality from ultra-low-dose PET on wavelet domain.

Recent development in positron emission tomography (PET) dramatically increased the effective sensitivity by increasing the geometric coverage leading to total-body PET imaging. This encouraging breakthrough brings the hope of ultra-low dose PET imaging equivalent to transatlantic flight with the assistance of deep learning (DL)-based methods. However, conventional DL approaches face limitations in addressing the heterogeneous domain of PET imaging. This study aims to develop a wavelet-based DL method capable of restoring high-quality imaging from ultra-low-dose PET scans. In contrast to conventional DL techniques that denoise images in the spatial domain, we introduce WaveNet, a novel approach that inputs wavelet-decomposed frequency components of PET imaging to perform denoising in the frequency domain. A dataset comprising total-body 18F -FDG PET images of 1447, acquired using total-body PET scanners including Biograph Vision Quadra (Siemens Healthineers) and uEXPLORER (United Imaging) in Bern and Shanghai, was utilized for developing and testing the proposed method. The quality of enhanced images was assessed using a customized scoring system, which incorporated weighted global physical metrics and local indices. Our proposed WaveNet consistently outperforms the baseline UNet model across all levels of dose reduction factors (DRF), with greater improvements observed as image quality decreases. Statistical analysis (p < 0.05) and visual inspection validated the superiority of WaveNet. Moreover, WaveNet demonstrated superior generalizability when applied to two cross-scanner datasets (p < 0.05). WaveNet developed with total-body PET scanners may offer a computational-friendly and robust approach to recover image quality from ultra-low-dose PET imaging. Its adoption may enhance the reliability and clinical acceptance of DL-based dose reduction techniques.

Comparison of contrast-enhanced ultrasound imaging (CEUS) and super-resolution ultrasound (SRU) for the quantification of ischaemia flow redistribution: a theoretical study

The study of microcirculation can reveal important information related to pathology. Focusing on alterations that are represented by an obstruction of blood flow in microcirculatory regions may provide an insight into vascular biomarkers. The current in silico study assesses the capability of contrast enhanced ultrasound (CEUS) and super-resolution ultrasound imaging (SRU) flow-quantification to study occlusive actions in a microvascular bed, particularly the ability to characterise known and model induced flow behaviours. The aim is to investigate theoretical limits with the use of CEUS and SRU in order to propose realistic biomarker targets relevant for clinical diagnosis. Results from CEUS flow parameters display limitations congruent with prior investigations. Conventional resolution limits lead to signals dominated by large vessels, making discrimination of microvasculature specific signals difficult. Additionally, some occlusions lead to weakened parametric correlation against flow rate in the remainder of the network. Loss of correlation is dependent on the degree to which flow is redistributed, with comparatively minor redistribution correlating in accordance with ground truth measurements for change in mean transit time,dMTT(CEUS,R = 0.85; GT,R = 0.82) and change in peak intensity,dIp(CEUS,R = 0.87; GT,R = 0.96). Major redistributions, however, result in a loss of correlation, demonstrating that the effectiveness of time-intensity curve parameters is influenced by the site of occlusion. Conversely, results from SRU processing provides accurate depiction of the anatomy and dynamics present in the vascular bed, that extends to individual microvessels. Correspondence between model vessel structure displayed in SRU maps with the ground truth was>91%for cases of minor and major flow redistributions. In conclusion, SRU appears to be a highly promising technology in the quantification of subtle flow phenomena due ischaemia induced vascular flow redistribution.

Noninvasive Imaging of Transgene Expression in Neurons Using Chemical Exchange Saturation Transfer MRI.

Advances in gene therapy, especially for brain diseases, have created new imaging demands for noninvasive monitoring of gene expression. While reporter gene imaging using co-expression of fluorescent protein-encoding gene has been widely developed, these conventional methods face significant limitations in longitudinal invivo applications. Magnetic resonance imaging (MRI), specifically chemical exchange saturation transfer (CEST) MRI, provides a robust noninvasive alternative that offers unlimited depth penetration, reliable spatial resolution, and specificity toward particular molecules. In this study, we explore the potential of CEST-MRI for monitoring gene expression in neurons. We designed a CEST polypeptide reporter expressing 150 arginine residues and evaluated its expression in the living brain after viral vector delivery. A longitudinal study performed at one and 2 months postinjection showed that specific CEST signal was observable. In particular, the CEST contrast exhibited distinct peaks at 0.75 and 1.75 ppm, consistent with the expected hydroxyl and guanidyl protons resonance frequencies. Histological study confirmed the specific neuronal expression of the transgene evidenced by the fluorescence signal from the td-Tomato fluorophore fused to the polypeptide. The ability to image noninvasively a neuron-specific CEST-MRI reporter gene could offer valuable insights for further developments of gene therapy for neurological disorders.

Advancing Human–Machine Interface (HMI) through Development of a Conductive‐Textile Based Capacitive Sensor

AbstractSmart wearable sensors in human–machine interfaces (HMI) facilitate communication and interface between humans and robots, as well as among humans. Conventional wearables face significant limitations, including performance degradation under various deformations (e.g., strain and pressure), limited stretchability and flexibility, poor comfort, and breathability, complicating their integration into HMI applications. In response to these limitations, a smart, sewable, high‐precision HMI device based on a soft, textile‐based sensor with machine learning (ML)‐assisted data processing is proposed. The (Ag)‐based fabric electrode integrated into polymeric composite dielectric layer, exhibiting hysteresis error of (≤4%), a tensile strain sensitivity of gauge factor (GF) ≃ 1.03 at 100% elongation, a pressure sensitivity of ≈1.583 × 10−2 kPa−1 at pressures (&lt;50 kPa) and excellent stability (&gt;1000 cycles). To demonstrate its versatility in HMI, the textile‐based sensor is integrated into a glove for real‐time control of a commercially available prosthetic hand and a drone, as well as for sign language recognition, achieving 95.4% classification accuracy using the random forest (RF) classifier across 10 gestures. Beyond gesture recognition, the sensor measures a range of subtle physiological activities (&gt;0.35 kPa), functioning as a force myograph, esophageal manometry, and in gait analysis, among other strain and pressure‐related applications, highlighting exceptional capabilities for advanced wearable systems.

Programming the Dynamic Range of Nanochannel Biosensors for MicroRNA Detection Through Allosteric DNA Probes

AbstractSolid‐state nanochannel biosensors are extensively utilized for microRNA (miRNA) detection owing to their high sensitivity and rapid response. However, conventional nanochannel biosensors face limitations in their fixed dynamic range, restricting their versatility and efficacy. Herein, we introduce tunable triblock DNA probes with varying affinities for target miRNA to engineer solid‐state nanochannel biosensors capable of customizable dynamic range adjustment. The triblock DNA architecture comprises a poly‐adenine (polyA) block for adjustable surface density anchoring, alongside stem and loop blocks for modulating structural stability. Through systematic manipulation of these blocks, we demonstrate the ability to achieve diverse target binding affinities and detection limits, achieving an initial 81‐fold dynamic range. By combining probes with various affinities, we extend this dynamic range significantly to 10,900‐fold. Furthermore, by implementing a sequestration mechanism, the effective dynamic range of the nanochannel biosensor is narrowed to only a 3‐fold span of target concentrations. The customizable dynamic range of these advanced nanochannel biosensors makes them highly promising for a broad spectrum of biomedical and clinical applications.

Synthetic Biology-Enabled Engineering of Probiotics for Precision and Targeted Therapeutic Delivery Applications

Probiotics, defined as live microorganisms that offer health benefits when consumed in adequate amounts, have traditionally been used to maintain gastrointestinal (GI) health. They are effective in managing conditions such as inflammatory bowel disease (IBD), irritable bowel syndrome (IBS), and antibiotic-associated diarrhea. Recent research, however, has expanded their potential applications beyond the GI tract to include immune modulation, metabolic regulation, and mental health support. Despite these advances, conventional probiotics face limitations such as instability in harsh environments, lack of precise targeting, and ineffective control over drug release. This review explores the application of synthetic biology in addressing these limitations, emphasizing how tools such as CRISPR-Cas systems, synthetic gene circuits, and plasmid-based mechanisms enhance the stability, functionality, and specificity of engineered probiotics for therapeutic delivery. Applications of synthetic biology-enabled probiotics are examined across a range of health contexts, including GI health, cancer therapy, metabolic disorders, immune modulation, and mental well-being. Synthetic biology tools provide precise genetic modifications that improve the stability, viability, and functionality of engineered probiotics, enabling more targeted therapeutic delivery. Additionally, technologies such as encapsulation support probiotic survival, while gene circuits allow programmable, stimulus-responsive therapeutic release. As synthetic biology continues to advance, the development of programmable probiotics holds promise for personalized, targeted medicine, positioning probiotics as living therapeutics and patient-friendly alternatives to conventional therapeutic delivery systems.

Distinction of clinical features and microbiological methods between Chlamydia psittaci and Legionella pneumophila pneumonia confirmed by metagenomic next-generation sequencing

Objectives Detection and diagnosis of Chlamydia psittaci (C. psittaci) pneumonia is often overlooked due to conventional methods limitations and similarity to other atypical community acquired pneumonia (CAP). Using mNGS, we aimed to distinguish psittacosis from legionellosis for early C. psittaci pneumonia diagnosis and better prognosis. Methods Thirty-seven patients diagnosed with atypical CAP were enrolled in this retrospective study, including 14 C. psittaci pneumonia and 23 Legionella pneumophila (L. pneumophila) pneumonia. We collected and compared baseline, lab results, radiology imaging, conventional microbiological methods and more importantly, mNGS results of clinical samples, as well as the treatments and prognosis between psittacosis and legionellosis. Results Patients with C. psittaci and L. pneumophila had similar symptoms and were presented with high levels of inflammatory markers. However, patients with C. psittaci pneumonia were more likely to have exposure to birds or parrots [11 (78.6%) vs. 2 (8.7%), p < 0.001], had higher proportions of fever and chill (p = 0.015 and 0.035), higher levels of hemoglobin and albumin (p = 0.002 and 0.018) compared with those with L. pneumophila. Of 14 C. psittaci patients, only one had positive IgM antibody, with no positive cultures. Early identification of pathogens by mNGS method contributed to timely antibiotics’ adjustment and better outcomes then, yet with similar hospital mortality between two groups [7.1% (1/14) vs. 34.8% (8/23), p = 0.112]. Conclusion Early mNGS detection of atypical pathogens in multiple samples improves on traditional methods, promptly adjust empirical antimicrobial treatment to pathogen-targeted antibiotics, further improve prognosis.

Intelligent water quality prediction system with a hybrid CNN–LSTM model

ABSTRACT Water pollution remains a longstanding challenge globally, prompting substantial investment in water quality protection. The integration of advanced machine learning models offers promising avenues for accurate water quality prediction, enabling proactive measures to safeguard water sources. Presently, water quality assessment relies predominantly on physical and chemical metrics. This study developed MultiLayer Perceptron (MLP), eXtreme Gradient Boosting (XGBoost), long short-term memory (LSTM), and a hybrid CNN–LSTM model to forecast pH and dissolved oxygen (DO) levels. Results demonstrated the hybrid model's superior performance, with mean squared errors (MSEs) of 0.0015 and 0.0361 for pH and DO prediction, respectively. For Water Quality Classification (WQC), Random Forest (RF), k-Nearest Neighbors (kNNs), Support Vector Machine (SVM), and Light gradient Boosting Machine (Light GBM) were employed, with SVM achieving the highest accuracy at 88.75%. The research underscores the effectiveness of the CNN–LSTM model in predicting pH and DO levels. Leveraging these predictions as inputs to the SVM model offers valuable insights, particularly in regions where conventional monitoring methods face limitations. This streamlined approach, requiring only two parameters, signifies a significant advancement in accurate water quality prediction.

Enhancement of Synaptic Behavior in Organic Electrochemical Transistors via the Introduction of Layer‐by‐Layer Grown Metal‐Organic Framework

AbstractOrganic electrochemical transistors (OECTs) are promising for neuromorphic architectures as they can generate multiple electrical states through the control of ion transport. However, conventional OECTs face limitations in mimicking a fully functional biological synapse due to their inability to achieve long‐term plasticity. In this study, a metal‐organic framework (MOF)‐enhanced OECT (MOECT) is fabricated by introducing MOF into the ion‐organic semiconductor (OSC) layer. MOFs are synthesized using the layer‐by‐layer (LBL) method, and additional cross‐linked OSC is introduced to prevent damage to the semiconductor layers during synthesis. The synthesized MOF layers hinder the rediffusion of ions in OECT, allowing ions to remain in the OSC for an extended period. In this study, the MOECT showed a change in current depending on the doping level, recording a current state 4.4 × 107 times higher than that of pristine OECT. Ultimately, the developed MOECTs are applied as synaptic transistors. MOECTs show 14% higher excitatory postsynaptic current (EPSC) after 130 s compared to pristine OECT, thereby strengthening the long‐term plasticity characteristics of neuromorphic devices. This method enhances the performance of synaptic transistors by introducing MOF, offering various possibilities through the selection of different MOF structures and ions, indicating it is a methodological approach with high potential.

Highly chiral exceptional point in coupled resonators perturbed by nanoscatterers

With employing strong chirality in an optical system, the direction of light propagation can be controlled and subwavelength particles can be detected. Here, we show that a different kind of chiral exceptional point (EP) with high (spatial) chirality can appear in a coupled resonator perturbed by nanoscatterers, in which both the distance and position of the scatterers can be tuned. We achieve strong chiral EP in two different distances between the resonators, with chirality around 0.99, in both cases. Besides, chiral EP associated with the higher harmonic whispering gallery mode is achieved, with chirality around 0.95. We also investigate the interaction of particles with same and different spin of light, which can mimic the spin-up and spin-down of electrons in quantum mechanics. The proposed device provides a tunable scheme to achieve high directionality of different cavity modes by incorporating one or more nanoscatterers. With incorporating more than one tunable mechanism such as nanoscatterers, nonlinearity, and time-modulation, simultaneously, the conventional limitations in chirality and sensitivity may be surpassed in the presence of unwanted imperfections of fabrication and noisy environment.

Programming the Dynamic Range of Nanochannel Biosensors for MicroRNA Detection through Allosteric DNA Probes.

Solid-state nanochannel biosensors are extensively utilized for microRNA detection owing to their high sensitivity and rapid response. However, conventional nanochannel biosensors face limitations in their fixed dynamic range, restricting their versatility and efficacy. Herein, we introduce tunable tri-block DNA probes with varying affinities for target miRNA to engineer solid-state nanochannel biosensors capable of programmable dynamic range adjustment. The tri-block DNA architecture comprises a poly-adenine (polyA) block for adjustable surface density anchoring, alongside stem and loop blocks for modulating structural stability. Through systematic manipulation of these blocks, we demonstrate the ability to achieve diverse target binding affinities and detection limits, achieving an initial 81-fold dynamic range. By combining probes with different affinities, we extend this dynamic range significantly to 10,900-fold. Furthermore, by implementing a sequestration mechanism, the effective dynamic range of the nanochannel biosensor was narrowed to only a 3-fold range of target concentrations. The customizable dynamic range of these advanced nanochannel biosensors makes them highly promising for a broad spectrum of biomedical and clinical applications.

Pore pressure prediction based on conventional well logs and seismic data using an advanced machine learning approach

Pore pressure is a decisive measure to assess the reservoir’s geomechanical properties, ensures safe and efficient drilling operations, and optimizes reservoir characterization and production. The conventional approaches sometimes fail to comprehend complex and persistent relationships between pore pressure and formation properties in the heterogeneous reservoirs. This study presents a novel machine learning optimized pore pressure prediction method with a limited dataset, particularly in complex formations. The method addresses the conventional approach's limitations by leveraging its capability to learn complex data relationships. It integrates the best Gradient Boosting Regressor (GBR) algorithm to model pore pressure at wells and later utilizes Continuous Wavelet Transformation (CWT) of the seismic dataset for spatial analysis, and finally employs Deep Neural Network for robust and precise pore pressure modeling for the whole volume. In the second stage, for the spatial variations of pore pressure in the thin Khadro Formation sand reservoir across the entire subsurface area, a three-dimensional pore pressure prediction is conducted using CWT. The relationship between the CWT and geomechanical properties is then established through supervised machine learning models on well locations to predict the uncertainties in pore pressure. Among all intelligent regression techniques developed using petrophysical and elastic properties for pore pressure prediction, the GBR has provided exceptional results that have been validated by evaluation metrics based on the R2 score i.e., 0.91 between the calibrated and predicted pore pressure. Via the deep neural network, the relationship between CWT resultant traces and predicted pore pressure is established to analyze the spatial variation.

Design of adaptive recommendation system for autism children using optimal feature selection-based adaptive dilated 1DCNN-LSTM with attention mechanism

One kind of neurological disorder is caused in the brain which is defined as Autism Spectrum Disorder (ASD). It has acquired the symptoms that appear in young children. In addition to that, it influences how the individual behaves and learns as well as communicates and interacts with others. More specifically, the term Autism is defined as a developmental disorder that impacts communication and social skills and it may vary from mental handicap cases to relieving superior cognitive abilities, intact, and the characteristic pattern of poor. Moreover, the school activities have acquired various difficulties to the given model that include changes in expected routines, intense sensory stimulation, noisy or disordered environments, and social interactions. Consequently, the conventional approaches face certain limitations like user privacy, scalability, and cold-start. Here, a novel suggestion system for autistic children is developed to detect distractions and anxious situations using deep learning and then treat the children based on their abilities. It has helped to prevent the risk to children. The data is given to the selection of the feature stage. The weight optimization is performed using the Modified Garter Snake Optimization Algorithm (MGSOA) during the selection of features. Then, the selected features are given to the Adaptive Dilated One Dimensional Conventional Neural Network (1DCNN) and Long Short-Term Memory (LSTM) with Attention Mechanism termed AD-1DCNN + LSTM-AMfor detecting the autism disorder for children. Here, the parameter optimization is performed using MGSOA optimization. It effectively forecasts the symptoms in a short time. This optimization helps to provide reliable and flexible outcomes for the developed recommendation system for autistic children. The developed recommendation system for autistic children is compared to baseline techniques with efficacy metrics to visualize elevated results.

Enhancing the Photovoltaic Efficiency of In0.2Ga0.8N/GaN Quantum Well Intermediate Band Solar Cells Using Combined Electric and Magnetic Fields.

This study presents a theoretical investigation into the photovoltaic efficiency of InGaN/GaN quantum well-based intermediate band solar cells (IBSCs) under the simultaneous influence of electric and magnetic fields. The finite element method is employed to numerically solve the one-dimensional Schrödinger equation within the framework of the effective-mass approximation. Our findings reveal that electric and magnetic fields significantly influence the energy levels of electrons and holes, optical transition energies, open-circuit voltages, short-circuit currents, and overall photovoltaic conversion performances of IBSCs. Furthermore, this research indicates that applying a magnetic field positively influences conversion efficiency. Through the optimization of IBSC parameters, an efficiency of approximately 50% is achievable, surpassing the conventional Shockley-Queisser limit. This theoretical study demonstrates the potential for next-generation photovoltaic technology advancements.

Characterizing anomalous peaks in the resistance–temperature profile of Bi2Sr2CaCu2O8+δ flakes featuring surface degeneration

Abstract This study focuses on an observed anomalous resistance peak in the temperature-dependent resistance (RT) curves of Bi2Sr2CaCu2O8+δ (BSCCO), attributed to surface degradation and pronounced electrical resistance anisotropy. Employing a standard four-point probe technique on the ab-plane, this research circumvents conventional c-axis testing limitations, enhancing the understanding of BSCCO’s electrical behavior by avoiding contact resistance and etching issues. A comprehensive three-dimensional model, developed using the finite element method, captures the strong resistive anisotropy and correlates the depth of surface degradation with the anomalous resistance peaks, explaining this phenomenon from a quantitative perspective, providing a more specific reference for future analysis of relevant signals. The fabrication process involved pre-patterning and mechanical exfoliation techniques to minimize atmospheric exposure and ensure device integrity. Despite these efforts, surface degradation impacting the superconductivity of surface layers was inevitable. The study’s experimental results, complemented by numerical modeling, reveal the intricate relationship between surface layer thickness and the anomalous resistance peak, providing an approach to gauge the extent of degradation in BSCCO devices. Moreover, it underscores the potential necessity of employing some critical techniques to avoid degradation, such as low-temperature exfoliation in other literatures where degradation signal is notably absent from RT curves. This work advances the understanding of BSCCO’s electrical properties and highlights the critical need for precise fabrication and environmental controls in developing high-temperature superconducting technologies.

Integrated One-Step Fabrication of Protonic Sensing Devices for Respiratory Monitoring.

The development of direct fabrication routes with seamless integration of both the macro/micropatterning process and nanostructure synthesis is crucial for the commercial realization of cost-effective nanoscale sensor devices. This, if realized, can liberate us from the conventional limitations inherent in nanoscale device manufacturing. Specifically, such fabrication routes can, in principle, address the challenges such as the complexity, multistep nature, and substantial costs associated with existing technologies, which are not suitable for widespread market adoption in everyday-use devices. Herein, we propose a novel yet facile one-step fabrication approach that simultaneously accomplishes both patterning and nanostructure synthesis by employing low-power, cost-effective laser technology with a minimal environmental footprint. Versatile in nature, this approach can enable the incorporation of diverse functionalities spanning a broad spectrum of technologies, encompassing fields such as sensors, catalysts, photonics, energy storage, and biomedical monitoring devices. As a proof of concept, using our approach, we fabricated an ultra responsive, high-speed protonic sensing device for real-time respiratory monitoring. The enhanced temporal characteristics of our as-fabricated device, particularly in the relative humidity levels of interest in breath monitoring (typically over 55%), exhibited a superior response/recovery time in rapid humidity fluctuations. We envisage that the advantages brought by the presented fabrication approach can pave the way to establish a new method for inexpensive large-scale functional-material-based device fabrication.

Advancements in metal oxide bio‐nanocomposites for sustainable food packaging: Fabrication, applications, and future prospectives

AbstractThe research on metal oxide bio‐nanocomposites for sustainable food packaging has witnessed significant advancements, offering a promising alternative to traditional food packaging materials. This review briefly describes their fabrication techniques, applications, superiority over conventional packaging, challenges, limitations, and potential trends. These new materials are derived by incorporating metal oxide nanoparticles into the biopolymers and show better properties, such as better antimicrobial properties, which are vital in food packaging. The advantages of using metal oxide bio‐nanocomposites over typical food packaging films include enhanced mechanical properties, better moisture and oxygen resistance, bacterial resistance, and light protection. These versatile materials not only serve the purpose of properly preserving the quality and possibly even the wholesomeness of packed food products, but they are also environmentally friendly. Moreover, the review presents current developments and areas of use of metal oxide bio‐nanocomposites in food packaging and it also proposes future developments to meet the modern challenge of the food industry in the development of advanced packaging technologies.