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Real‐time detection and differentiation of diverse external stimuli with one tactile senor remains a huge challenge and largely restricts the development of electronic skins. Although different sensors have been described based on piezoresistivity, capacitance, and triboelectricity, and these devices are promising for tactile systems, there are few, if any, piezoelectric sensors to be able to distinguish diverse stimuli in real time. Here, a human skin‐inspired piezoelectric tactile sensor array constructed with a multilayer structure and row+column electrodes is reported. Integrated with a signal processor and a logical algorithm, the tactile sensor array achieves to sense and distinguish the magnitude, positions, and modes of diverse external stimuli, including gentle slipping, touching, and bending, in real time. Besides, the unique design overcomes the crosstalk issues existing in other sensors. Pressure sensing and bending sensing tests show that the proposed tactile sensor array possesses the characteristics of high sensitivity (7.7 mV kPa−1), long‐term durability (80 000 cycles), and rapid response time (10 ms) (less than human skin). The tactile sensor array also shows a superior scalability and ease of massive fabrication. Its ability of real‐time detection and differentiation of diverse stimuli for health monitoring, detection of animal movements, and robots is demonstrated. Human skin‐inspired piezoelectric tactile sensor array can sense and distinguish the magnitude, positions, and modes of diverse external stimuli in real time. The dual‐layer comb structures of the sensor array with row+column electrodes eliminate crosstalk and reduce the number of connection wires. It excavates enormous applications in various settings, such as health monitoring, detection of animal movements, and robots.

The entire water cycle is contaminated with largely undetected micropollutants, thus jeopardizing wastewater treatment. Currently, monitoring methods that are used by wastewater treatment plants (WWTP) are not able to detect these micropollutants, causing negative effects on aquatic ecosystems and human health. In our case study, we took collective samples around different treatment stages (aeration tank, membrane bioreactor, ozonation) of a WWTP and analyzed them via Deep-UV laser-induced Raman and fluorescence spectroscopy (LIRFS) in combination with a CNN-based AI support. This process allowed us to perform the spectra recognition of selected micropollutants and thus analyze their reliability. The results indicated that the combination of sensitive fluorescence measurements with very specific Raman measurements, supplemented with an artificial intelligence, lead to a high information gain for utilizing it as a monitoring purpose. Laser-induced Raman spectroscopy reaches detections limits of alert pharmaceuticals (carbamazepine, naproxen, tryptophan) in the range of a few µg/L; naproxen is detectable down to 1 × 10−4 mg/g. Furthermore, the monitoring of nitrate after biological treatment using Raman measurements and AI support showed a reliable assignment rate of over 95%. Applying the fluorescence technique seems to be a promising method in observing DOC changes in wastewater, leading to a correlation coefficient of R2 = 0.74 for all samples throughout the purification processes. The results also showed the influence of different extraction points in a cleaning stage; therefore, it would not be sensible to investigate them separately. Nevertheless, the interpretation suffers when many substances interact with one another and influence their optical behavior. In conclusion, the results that are presented in our paper elucidate the use of LIRFS in combination with AI support for online monitoring.

ABSTRACT Microbial bioprocessing is a key technology for the production of a wide range of biomolecules, including proteins, enzymes, antibiotics, and other bioactive compounds. In recent years, there has been an increasing interest in using microfluidic platforms for bioprocessing, due to the ability to precisely control and manipulate fluids at the microscale. Microfluidics offers a transformative platform for the manufacturing of biomolecules intended for clinical applications by addressing key technical challenges in scalability, precision, reproducibility, and the ability to study complex biological systems. In this review, various methods used to fabricate microfluidic platforms and the current state‐of‐the‐art in the synthesis/production of biopharmaceuticals, polymers, bioactive compounds, and real‐time monitoring in microscale bioprocesses are discussed. Additionally, the future trends and directions are highlighted. Overall, we envisage the utilization of microfluidic platforms to advance the field of microbial bioprocessing and applications in the biomedical field.

The deterioration of gait can be used as a biomarker for ageing and neurological diseases. Continuous gait monitoring and analysis are essential for early deficit detection and personalized rehabilitation. The use of mobile and wearable inertial sensor systems for gait monitoring and analysis have been well explored with promising results in the literature. However, most of these studies focus on technologies for the assessment of gait characteristics, few of them have considered the data acquisition bandwidth of the sensing system. Inadequate sampling frequency will sacrifice signal fidelity, thus leading to an inaccurate estimation especially for spatial gait parameters. In this work, we developed an inertial sensor based in-shoe gait analysis system for real-time gait monitoring and investigated the optimal sampling frequency to capture all the information on walking patterns. An exploratory validation study was performed using an optical motion capture system on four healthy adult subjects, where each person underwent five walking sessions, giving a total of 20 sessions. Percentage mean absolute errors (MAE%) obtained in stride time, stride length, stride velocity, and cadence while walking were 1.19%, 1.68%, 2.08%, and 1.23%, respectively. In addition, an eigenanalysis based graphical descriptor from raw gait cycle signals was proposed as a new gait metric that can be quantified by principal component analysis to differentiate gait patterns, which has great potential to be used as a powerful analytical tool for gait disorder diagnostics.

Current skin sensors or wound dressings fall short in addressing the complexities and challenges encountered in real‐world scenarios, lacking adequate capability to facilitate wound repair. The advancement of methodologies enabling early diagnosis, real‐time monitoring, and active regulation of drug delivery for timely comprehensive treatment holds paramount significance for complex chronic wounds. In this study, a nanocomposite hydrogel is devised for real‐time monitoring of wound condition and comprehensive treatment. Tannins and siRNA containing matrix metalloproteinase‐9 gene siRNA interference are self‐assembled to construct a degradable nanogel and modified with bovine serum albumin. The nanogel and pH indicator are encapsulated within a dual‐crosslinking hydrogel synthesized with norbornene dianhydride‐modified paramylon. The hydrogel exhibited excellent shape adaptability due to borate bonding, and the click polymerization reaction led to rapid in situ curing of the hydrogel. The system not only monitors pH, temperature, wound exudate alterations, and peristalsis during wound healing but also exhibits hemostatic, antimicrobial, anti‐inflammatory, and antioxidant properties, modulates macrophage polarization, and facilitates vascular tissue regeneration. This therapeutic approach, which integrates the monitoring of pathological parameters with comprehensive treatment, is anticipated to address the clinical issues and challenges associated with chronic diabetic wounds and infected wounds, offering broad prospects for application. A nanocomposite dual network hydrogel system is designed for real‐time monitoring of wound status and comprehensive therapy, enabling the surveillance of pH, temperature, alterations in wound exudate, and mobility, and dynamically regulating the microenvironment throughout the wound healing process, thereby fostering the recovery of infected wounds. This is anticipated to address the clinical issues and challenges associated with chronic diabetic wounds.

Although research into ultrahigh dose-rate (UHDR) radiation therapy is ongoing, there is a significant lack of experimental measurements for two-dimensional (2D) dose-rate distributions. Additionally, conventional pixel-type detectors result in significant beam loss. In this study, we developed a pixel array-type detector with adjustable gaps and a data acquisition system to evaluate its effectiveness in measuring UHDR proton beams in real time. We measured a UHDR beam at the Korea Institute of Radiological and Medical Sciences using an MC-50 cyclotron, which produced a 45-MeV energy beam with a current range of 10–70 nA, to confirm the UHDR beam conditions. To minimize beam loss during measurement, we adjusted the gap and high voltage on the detector and determined the collection efficiency of the developed detector through Monte Carlo simulation and experimental measurements of the 2D dose-rate distribution. We also verified the accuracy of the real-time position measurement using the developed detector with a 226.29-MeV PBS beam at the National Cancer Center of the Republic of Korea. Our results indicate that, for a current of 70 nA with an energy beam of 45 MeV generated using the MC-50 cyclotron, the dose rate exceeded 300 Gy/s at the center of the beam, indicating UHDR conditions. Simulation and experimental measurements show that fixing the gap at 2 mm and the high voltage at 1000 V resulted in a less than 1% loss of collection efficiency when measuring UHDR beams. Furthermore, we achieved real-time measurements of the beam position with an accuracy of within 2% at five reference points. In conclusion, our study developed a beam monitoring system that can measure UHDR proton beams and confirmed the accuracy of the beam position and profile through real-time data transmission.

Polyhydroxyalkanoates (PHA) are biopolymers produced intracellularly from low‐cost and renewable feedstocks, whose production is usually assessed through laborious and offline tools. Two‐dimensional (2D) fluorescence spectroscopy is a noninvasive and nondestructive tool that can be used for real‐time monitoring of the biological systems producing PHA, without using solvents. Through projection to latent structures (PLS) modeling, models can be developed aiming at real‐time monitoring of the intracellular PHA content throughout the process stages where it is produced. This work shows the possibility of using fluorescence‐based models to monitor the intracellular PHA content under different operating conditions and during both stages of PHA production—culture selection (Stage 2) and PHA accumulation (Stage 3). Good PHA predictions were achieved regardless of the stage and operating conditions studied in the present work. The models developed for each specific operating condition present better PHA prediction abilities compared to the overall model (average errors ca. 4.0% and 5.0% gPHA/gTS, respectively). These results demonstrate the potential of optimizing the PHA production processes by better monitoring and controlling the systems, enabling the detection of the PHA maximum content while avoiding its consumption. Thus, losses of process productivity due to PHA consumption will be avoided. 2D fluorescence spectroscopy can monitor the PHA content in both stages where PHA is produced even when different operating conditions (OLR and sludge retention time [SRT]) were imposed, achieving intracellular PHA estimations with average errors below 4.0% and 5.0% gPHA/gTS. This reagentless tool can be used to monitor the PHA production systems in real‐time, aiming at improving process productivity.

Eastern Oysters ( Crassostrea virginica ) are a keystone species and an important product of the commercial shellfish industry in Delaware. Oysters are known as “environmental engineers” that provide a structured habitat for the ecosystem, thus promoting biodiversity. In order to further investigate the role oysters play in increasing biodiversity, real‐time monitoring and environmental DNA (eDNA) were conducted at different sites around Rehoboth Bay, Delaware, USA. The sites include pilot artificial reefs, private aquaculture farms, and a control site without any oysters or habitat structure. Underwater GoPro Hero 3+ and 8 cameras were deployed every 2 weeks from June to October in 2022 and 2023. Cameras were deployed for approximately 2–3 h at a time, and upon retrieval, cameras were reviewed for any signs of aquatic life, and all documented species were identified and recorded for comparisons between sampling sites. Water samples were collected simultaneously for eDNA analysis to serve as a complementary method for species identification. DNA isolation and polymerase chain reaction (PCR) were performed to amplify gene sequences for targeted species. Using camera technology, 23 different animal species were recorded across the five study sites. The most abundant species included Spot ( Leiostomus xanthurus ), Atlantic Silverside ( Menidia menidia ), Atlantic Menhaden ( Brevoortia tyrannus ), Horseshoe Crab ( Limulus polyphemus ), Blue Crab ( Callinectes sapidus ), and Hermit Crabs ( Pagurus longicarpus ). The eDNA analysis also successfully detected these species, highlighting the effectiveness of eDNA as a tool for species monitoring. Notably, there was considerable overlap between species identified through both real‐time monitoring and eDNA methods. These findings contribute to ongoing oyster restoration initiatives and sustainable aquaculture practices in the Delaware Inland Bays (DIB), while also enhancing our understanding of complementary biodiversity monitoring techniques.

Machine learning (ML) and nanotechnology interfacing are exploring opportunities for cancer treatment strategies. To improve cancer therapy, this article investigates the synergistic combination of Graphene Oxide (GO)‐based devices with ML techniques. The production techniques and functionalization tactics used to modify the physicochemical characteristics of GO for specific drug delivery are explained at the outset of the investigation. GO is a great option for treating cancer because of its natural biocompatibility and capacity to absorb medicinal chemicals. Then, complicated biological data are analyzed using ML algorithms, which make it possible to identify the best medicine formulations and individualized treatment plans depending on each patient's particular characteristics. The study also looks at optimizing and predicting the interactions between GO carriers and cancer cells using ML. Predictive modeling helps ensure effective payload release and therapeutic efficacy in the design of customized drug delivery systems. Furthermore, tracking treatment outcomes in real time is made possible by ML algorithms, which permit adaptive modifications to therapy regimens. By optimizing medication doses and delivery settings, the combination of ML and GO in cancer therapy not only decreases adverse effects but also enhances treatment accuracy. ML‐integrated GO‐based analytical approaches to support cancer therapy.

We modified the bear cage trap with a low-cost remotely triggered trap door and fitted the trap with a motion activated camera that relayed bear captures to researchers in real time. We built 4 modified bear cage traps and used them to capture free-ranging American black bears (Ursus americanus) during summer 2020 on MPG Ranch in western Montana, USA. Lacking data on bear confinement time in traps from previous studies, we compared the amount of time that bears were confined in our modified traps with estimated confinement time had the same bears in our study been captured in traditional traps that were physically checked once daily at 0900, once daily at 1200, or twice daily at 0900 and 1600. We captured bears 30 times during 195 trap nights. The camera system relayed photos of all captures in real time and the remotely triggered door release was 100% successful. When we evaluated all bear captures and recaptures (n = 30), mean bear confinement time in our modified traps (4.92 hrs) was significantly lower than estimated mean confinement time had the same bears been captured in traditional traps that were checked once daily at 0900 (14.95 hrs), once daily at 1200 (10.73 hrs), or twice daily at 0900 and 1600 (7.01 hrs). For bears that were recaptured during regular trap monitoring hours (0700–1800) and remotely released without immobilization (n = 12), mean confinement time in our modified traps was <1 hr. By remotely monitoring traps, we saved an estimated US $6,580 or $13,624 in gas and staff hours during 3 months of trapping, had we physically checked traps once or twice daily, respectively. Our modified traps minimized animal confinement time, eliminated unnecessary animal immobilizations, and helped us maximize time and resource efficiencies. Our findings are relevant to bear research and management, and the technology we used can be adapted for trapping other wildlife species.