Particle size evolution in seeded semibatch emulsion polymerization is monitored by two real-time monitoring techniques: online turbidity spectroscopy (TUS) and inline photon density wave spectroscopy (PDWS). An automatic dilution system that withdraws a sample from the reactor and upon dilution transfers to the measurement cell is used for the online TUS analysis. A PDWS probe is immersed in the reactor and collects inline the scattered light directly from the reacting latex. The particle sizes retrieved from TUS and PDWS are compared to offline dynamic light scattering (DLS) values. The particle size obtained by TUS is close to the intensity-average particle size obtained offline by DLS, while the particle size obtained by PDWS lies closer to the number-average particle size from DLS.

A bio-hydrogel is prepared via a low-cost and time-saving strategy and is studied as a self-powered wound dressing for precision medicine and health monitoring. Promoted by a dual self-catalytic pair composed of Fe3+ and catechol, gelation time is dramatically accelerated to 15 s and the hydrogel can be freely modeled at -18 °C without losing flexibility. As smart wound dressing, the required properties such as self-healing, self-adhesion, antibacterial, and sensing stability, are integrated into one hydrogel. TA@CNC offers abundant hydrogen bond and metal-ligand coordination which facilitate the hydrogel with a self-healing efficiency of 91.6%. Owing to the catechol in TA@CNC, hydrogel can adhere to multiple substrates including skin, and show good antibacterial activity. Inspired by a fruit battery, a self-powered wound dressing is fabricated, which exhibits excellent correlation and efficiency in real-time monitoring of body activity and drug release. In vivo experiments prove that efficient drug release of hydrogel dressing significantly accelerate wound healing. Additionally, the dressing exhibits excellent biocompatibility and has no negative impacts on organs. Herein, a smart wound dressing that is different from the traditional way is proposed. As a self-powered device, it can be integrated with wireless devices and is expected to participate in promising applications.

Hydrogen fuel, which is essential for the hydrogen economy, including hydrogen cell vehicles, must be of high quality for optimal hydrogen cell use. Currently, hydrogen fuel quality control is mainly done by offline analysis with periodic sampling. However, with the anticipated surge in hydrogen charging stations, there\'s a pressing need for cost-effective, high-throughput online analysis systems. Additionally, the miniaturization of these analytical instruments for field application is also a challenge. In this study, we present a compact, real-time hydrogen fuel analyzer based on gas chromatography with a pulsed discharge helium ionization detector. Its dual-column system efficiently analyzes major impurities in hydrogen fuel in less than 30 min. Indicator species (CO, CO2, CH4, O2, N2, and additional hydrogen sulfide [H2S]) are determined by examining hydrogen production and supply processes. The analyzer\'s measurement capability is consistent with µmol/mol-level analysis, providing valuable real-time information for hydrogen infrastructure managers. Additionally, it can analyze H2S, a crucial marker of sulfur compounds acting as catalytic poisons in fuel cells. This real-time analyzer offers efficient, informed decision-making support for hydrogen infrastructure managers, enhancing the overall reliability of hydrogen fuel in fuel-cell electric vehicles.

Current reconstruction chemistry studies are mainly operated at the laboratory scale, where the operating parameters are different from those used in industrial water electrolyzers. This gap leads to unclear reconstruction behaviors under industrial conditions and constrains the application of catalysts. Here, this work presents a new reconstruction mechanism and anomalous detachment phenomena observed in leaching-type oxygen-evolving precatalysts under industrial conditions, different from the reported results obtained under laboratory conditions. The identified detachment issues are closely linked to the production of a potassium salt separate phase, which proves sensitive to the local environment, and its instability easily leads to catalyst stripping from the substrate. By establishing detachment critical point and operating parameter-detachment correlation, a targeted reconstruction strategy is proposed to achieve smooth ligand leaching and effectively solve the detachment issue. Theoretical analyses validate the dual-site regulation in directionally reconstructed catalysts with optimized intermediate adsorption. Under industrial conditions, the coupled electrolyzer delivers an industrial-level current density at low cell voltage with prolonged durability, 1 A cm-2 at 2 V for over 340 h. This work bridges the gap of leaching-type precatalysts between laboratory test conditions and industrial operating conditions.

Raman spectroscopy has been used to measure the concentration of a pharmaceutically relevant model amine intermediate for positive allosteric modulators of nicotinic acetylcholine receptor in a ω-transaminase-catalyzed conversion. A model based on a one-dimensional convolutional neural network was developed to translate raw data augmented Raman spectra directly into substrate concentrations, with which the conversion from ketone to amine by ω-transaminase could be determined over time. The model showed very good predictive capabilities, with R2 values higher than 0.99 for the spectra included in the modeling and 0.964 for an independent dataset. However, the model could not extrapolate outside the concentrations specified by the model. The presented work shows the potential of Raman spectroscopy as a real-time monitoring tool for biocatalytic reactions.

Self-monitoring in tumor therapy is a concept that allows for real-time monitoring of the location and state of applied nanomaterials. This monitoring relies on dynamic signals, such as wave or magnetic signals, which vary in response to changes in the location and state of nanomaterials. Dynamic changes in nanomaterials can be monitored using dynamic signals, making it possible to determine and control the treatment process. Theranostic nanomaterials, which possess unique physical and chemical properties, have recently been explored as a viable option for self-monitoring. With the help of self-monitoring, theranostic nanomaterials can guide themselves to achieve region-selective treatment with higher controllability and safety. In this review, self-monitoring theranostic nanomaterials will be introduced in three parts according to their roles during therapy: tumor accumulation, tumor therapy, and metabolism. The limitations and future challenges of current self-monitoring theranostic nanomaterials will also be discussed.

Fatigue causes deleterious effects to physical and mental health of human being and may cause loss of lives. Therefore, the adverse effects of fatigue on individuals and the society are massive. With the ever-increasing frequency of overtraining among modern military and sports personnel, timely, portable and accurate fatigue diagnosis is essential to avoid fatigue-induced accidents. However, traditional detection methods require complex sample preparation and blood sampling processes, which cannot meet the timeliness and portability of fatigue diagnosis. With the development of flexible materials and biosensing technology, wearable biosensors have attracted increased attention to the researchers. Wearable biosensors collect biomarkers from noninvasive biofluids, such as sweat, saliva, and tears, followed by biosensing with the help of biosensing modules continuously and quantitatively. The detection signal can then be transmitted through wireless communication modules that constitute a method for real-time understanding of abnormality. Recent developments of wearable biosensors are focused on miniaturized wearable electrochemistry and optical biosensors for metabolites detection, of which, few have exhibited satisfactory results in medical diagnosis. However, detection performance limits the wide-range applicability of wearable fatigue diagnosis. In this article, the application of wearable biosensors in fatigue diagnosis has been discussed. In fact, exploration of the composition of different biofluids and their potential toward fatigue diagnosis have been discussed here for the very first time. Moreover, discussions regarding the current bottlenecks in wearable fatigue biosensors and the latest advancements in biochemical reaction and data communication modules have been incorporated herein. Finally, the main challenges and opportunities were discussed for wearable fatigue diagnosis in the future.

Hairy roots are used to produce plant agents and additives. Due to their heterogeneous structure and growth characteristics, it is difficult to determine growth-related parameters continuously and in real time. Laser speckle contrast analysis is widely used as a non-destructive measurement technique in material testing or in medical technology. This type of analysis is based on the principle that moving objects or particles cause fluctuations in stochastic interference patterns known as speckle patterns. They are formed by the random backscattering of coherent laser light on an optically rough surface. A Laser Speckle Imager, which is well established for speckle studies of hemodynamics, was used for the first time for non-invasive speckle measurements on hairy roots to study dynamic behavior in plant tissue. Based on speckle contrast, a specific flux value was defined to map the dynamic changes in the investigated tissue. Using this method, we were able to predict the formation of lateral strands and to identify the growth zone in the apical root region, as well as dividing it into functional regions. This makes it possible to monitor physiological processes in the apical growth zone in vivo and in real time without labeling the target structures.

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.

Real-time monitoring of bioprocesses by the integration of analytics at critical unit operations is one of the paramount necessities for quality by design manufacturing and real-time release (RTR) of biopharmaceuticals. A well-defined process analytical technology (PAT) roadmap enables the monitoring of critical process parameters and quality attributes at appropriate unit operations to develop an analytical paradigm that is capable of providing real-time data. We believe a comprehensive PAT roadmap should entail not only integration of analytical tools into the bioprocess but also should address automated-data piping, analysis, aggregation, visualization, and smart utility of data for advanced-data analytics such as machine and deep learning for holistic process understanding. In this review, we discuss a broad spectrum of PAT technologies spanning from vibrational spectroscopy, multivariate data analysis, multiattribute chromatography, mass spectrometry, sensors, and automated-sampling technologies. We also provide insights, based on our experience in clinical and commercial manufacturing, into data automation, data visualization, and smart utility of data for advanced-analytics in PAT. This review is catered for a broad audience, including those new to the field to those well versed in applying these technologies. The article is also intended to give some insight into the strategies we have undertaken to implement PAT tools in biologics process development with the vision of realizing RTR testing in biomanufacturing and to meet regulatory expectations.