This work presents the fabrication of a large-area flexible matrix sensor array using a two-step sequential grayscale Digital Light Processing (sDLP) process. In this approach, digitally modulated masks enable a two-step exposure that forms 3D flexible microstructures in a single printing sequence, followed by the deposition of a resincarbon nanotube (CNT) conductive layer. A single sensor unit ($4.05 ~\text{mm} \times 4.05 ~\text{mm}$) was fabricated and tested across a broad pressure range ($1-3500 \text{kPa}$), exhibiting excellent linearity from 1 to $1500 \text{kPa}\left(\mathrm{R}^{2}=0.99\right)$ and high sensitivity ($296.5 \text{kPa}^{-1}$). The device demonstrated rapid response times of 0.07 s under loading and 0.01 s during release. By tuning the grayscale mask values, this design was successfully scaled to a $16 \times 9$ array (144 units), enabling large-scale fabrication via the two-step sDLP process. The array exhibited extremely low cross-talk (SNR $>99 ~\text{dB}$) and an effective spatial resolution of $\sim 5.98 ~\text{mm}$ per pixel, enabling precise mapping of multiple simultaneous contacts with minimal interference.

This study presents the development of a Python-based and user-friendly graphical interface system (Digital Mask Generation-Graphical User Interface, DMG-GUI) specifically for the design and fabrication of microlens array (MLA). The system integrates grayscale digital mask generation, machine learning (ML) algorithms, and digital light processing (DLP) technology to enable the precise fabrication of microlens arrays of various sizes. Through an intuitive graphical user interface (GUI), users can easily configure design parameters and quickly modify mask patterns as needed. Additionally, an embedded artificial neural network (ANN) model provides optimal process parameter suggestions, significantly enhancing fabrication efficiency and product quality. This work explores the construction and application of the machine learning model in depth. The fabricated microlenses are systematically analyzed in terms of morphology, dimensional stability, and optical performance. Experimental results demonstrate that the system offers easy operation and rapid mask generation, greatly simplifying the overall design and manufacturing workflow. With process parameters optimized by machine learning, the fabricated microlenses exhibit high stability and excellent optical performance, meeting the demands of high-precision optical applications. Overall, the DMG-GUI system successfully combines a graphical user interface with machine learning-driven process optimization. It not only improves the efficiency of microlens array design and fabrication but also highlights the practical potential of machine learning in the rapid manufacturing of micro-optical components.

This work introduces a rapid print-pause-print digital light processing (DLP) method that integrates porous membranes and flexible electronic layers directly into a single photo-curable resin structure through digital light encapsulation (DLE). This straightforward process eliminates conventional molding, bonding, and extracellular matrix (ECM) coating steps, enabling device fabrication within minutes. The resulting platform supports epithelial cell adhesion and monolayer formation without ECM pretreatment. Embedded capacitive electrodes allow real-time monitoring, with capacitance changes strongly correlating with actual cell morphology. This rapid and scalable approach provides an efficient method for constructing multimaterial microdevices for monitoring cell culturing process.

Microneedles (mu Ns) have emerged as a transformative alternative to conventional hypodermic needles, offering notable benefits such as reduced hazardous waste, lower risk of injury, improved safety, and greater acceptance among individuals with needle phobia. In this study, we introduce an efficient fabrication method combining Vat Photopolymerization and Machine Learning (VP-ML) for mu Ns production. The method begins with creating a dataset, which is used to develop ML model. This model predicts the printing parameters needed for VP machine based on the desired dimensions. The VP machine works by selectively curing a liquid photopolymer resin layer by layer with a digital mask, enabling precise mu Ns fabrication. This cutting-edge approach surpasses traditional methods by eliminating the need for molds and complex multi-step processes, enabling faster, customizable fabrication. VP-ML significantly reduces material waste and production costs while yielding sharper needle tips. By integrating Bayesian regularization backpropagation algorithm with ten hidden layers (BR10), the process optimizes printing parameters to enhance production speed and ensure high-quality outcomes. Experimental results demonstrate that VP-ML effectively produces mu Ns with base diameters greater than 150 mu m and heights exceeding 500 mu m, achieving a Mean Absolute Percentage Error (MAPE) of less than 10 % among 32 cases demonstrated in this study. Additionally, penetration tests demonstrated that the mu Ns fabricated using VP-ML were able to penetrate up to 90 % of its total height. These results highlight VP-ML as a green-manufacturing and cost-effective solution for mu Ns fabrication, while maintaining remarkable improvements in precision, manufacturing efficiency, flexibility, and scalability in the field.

Hybrid microfluidic platforms combining paper and polymer substrates hold great potential for chemical and biomedical applications but often face challenges like leakage and limited functionality due to weak integration methods. This study introduces a two-step fabrication process to create compact, one-piece hybrid paper/ polymer (OHPP) microfluidic devices with enhanced bonding strength, reaching up to 720 kPa, and long-term stability. The process ensures direct, leak-free bonding between substrates, enabling the paper membrane to function not only as a passive but also as an active fluidic component. Using digital light processing (DLP), we fabricated porous paper membranes with patternable hydrophilic and hydrophobic zones for advanced fluid control. We demonstrated three key applications, including (1) a passive micromixer where the cellulose fiber network induces chaotic advection and initiates mixing action, (2) a concentration gradient generator where different paper patterns and flow rates precisely controlled the gradient profile, and (3) a two-phase oil/water separator where paper membranes with stripe pattern achieved over 80 % recovery and 100 % purity by using capillary action and interfacial tension. The devices operate reliably at flow rates up to 60 mu L/min and maintain stable performance over time. This fabrication method provides a low-cost, scalable, and customizable solution for hybrid microfluidic devices, enhancing the role of paper membranes in complex fluidic processes.

This paper reports a novel method for fabricating hierarchical microstructure tactile sensors using sequential exposure Digital Light Processing (sDLP) 3D printing. Unlike traditional layer-by-layer 3D printing, this requires only two UV light exposures. Two-step include rapidly printing a flexible TPU resin as a transducer and selectively printing CNT resin active layer, all within minutes. The tactile sensor shows high sensitivity, up to 10.000 kPa(-1). It can sustain up to 2500 cycles and operates reliably at frequencies from 0.6Hz to 2Hz, with a response time of 90ms at 0.6kPa. The multiaxial forces tactile sensor can be created to detect bending and horizontal forces, making them suitable for wearable devices in gesture and texture recognition. Our scalable approach can produce a 3x3 sensor array in one step process and performed low crosstalk <0.1%.

This paper presents a rapid and robust method for fabricating portable energy harvesting and flexible self-powered sensors using digital light processing (DLP) 3D printing. PEDOT:PSS/polyacrvlate resin was used as conductive or positively charged material, with a conductivity of 4x10(-2) S/cm, while Teflon served as negatively charged material. The combination of DLP 3D printing and PEDOT:PSS allows for the precise creation of micro-dome structures, each with a diameter of 500 micrometers, in just a few minutes. The microstructural 3D-printed nanogenerator can create portable energy harvesting devices that reach up to by with finger tapping and maintain stable energy output for 900 cycles. A self-powered sensor is demonstrated, capable of capturing motion signals such as walking, running, and jumping, with the energy stored in capacitors.

This paper introduces an ultra-fast and scalable method for fabricating tactile sensors within seconds using sequential digital light processing (sDLP) 3D printing. The process involves two UV exposure steps: (1) printing a flexible microstructure and (2) printing a thin conductive layer. The flexible microstructure made from TPU resin, serving as the transducer, is printed in just 6 seconds, followed by the resin-CNT active layer as a conductive layer which is printed within 500 seconds. The miniaturized sensor (5 x 5 mm(2)), smaller than a fingertip, can detect pressures ranging from 50 kPa to 320 kPa, with sensitivities of 121.7 kPa.(1) in the low-pressure range and 30 kPa.(1) in the high-pressure range, both exhibiting excellent linearity (R-2 = 0.99). The sensor provides a rapid response time of 10 ms and 20 ms during loading and unloading. This scalable approach enables the fabrication of a 7x4 sensor matrix within seconds, capable of accurately detecting varying pressure magnitudes and locations. Furthermore, we demonstrate the integration of the tactile sensor array with a microcontroller, enabling precise tactile feedback sensing on both flat, hard surfaces and curved, soft surfaces. This advancement opens new possibilities for enhancing robotic touch capabilities and improving human-machine interactions in a wide range of applications.

This work describes a novel fabrication procedure for single-unit paper/PDMS microfluidic platforms with controllable bonding strength reaching up to 970 kPa, enabling highly efficient applications in two-phase oil/water separation and gradient generation. All tested cases showed excellent performance: (1) A continuous two-phase flow of oil/water droplets was completely separated with 100% purity and an over 80% recovery rate; (2) By varying flow rates and changing paper patterns, different gradient profiles could be generated with full control of concentration levels and transition types.

The rapid proliferation of over a thousand new psychoactive substances (NPS) presents a growing challenge for global public health and forensic science, due to their structural diversity and widespread illicit use, which significantly complicate analytical detection. In response, a fundamentally reengineered ionization strategy is introduced that integrates mesoporous graphitic-zeolite nanoparticles (MGNs) with UV-activated photothermal nanomaterials into a next-generation surface-assisted laser desorption/ionization time-of-flight mass spectrometry (SALDI-TOF MS) platform. MGNs-hierarchically structured carbon-based semiconductors synthesized via chemical vapor deposition-exhibit an exceptionally high surface area (>900 m2 g-1), broadband light absorption, and efficient photothermal energy transfer. These properties collectively enhance desorption and ionization efficiency in the low m/z range (100-500), a region where small-molecule drug detection is often hindered by matrix interference and low signal fidelity. Compared to conventional organic matrices, the MGN-based system achieves a 95-fold improvement in signal-to-noise ratio and delivers quantification accuracies exceeding 90% for twelve representative compounds across seven drug classes, including structurally diverse NPS. Mechanistic investigations reveal that localized photothermal heating, in synergy with strong analyte adsorption within the mesoporous framework, accelerates soft ionization while suppressing background noise. This design enables rapid, reproducible, and interference-free detection-key to real-time diagnostics and screening applications.

Understanding the metabolism of drugs is a principal consideration when it comes to understanding the activity of a precursor drug and determining if the precursor is converted into bioactive metabolites after ingestion in the human body. This process is typically studied using either animal models or in vitro models, such as human liver microsomes (HLM). In this research, a novel one-piece microreactor was fabricated with light-curing 3D printing technology, which can be seamlessly and directly integrated with a liquid chromatography–mass spectrometer (LC–MS) system for drug metabolic analysis after an in vitro human liver microsomal reaction. The results clearly showed that: (1) this system was able to conduct metabolic reactions (demonstrated by three commonly abused substances or impurity in illicit heroin including heroin, 6-acetylcodeine, and buprenorphine) at the operation temperature of 37 °C and operation pressure ranging from 7.8 to 21.5 bars, and its performance was very competitive to the conventional method while reducing total processing steps and minimizing manual operation, (2) the integrated LC–MS system demonstrated a high stability and precision where the RSD of chromatographic peak area and retention time was only 2.53

The development of flexible tactile sensors has been constrained by fabrication methods that are often complex, costly, and limited in design flexibility. In this study, a fully printed, mold-free, ultrafast, and scalable fabrication strategy is presented, based on sequential grayscale digital light processing (s-gDLP) printing. A dual-exposure UV process with pixel-level grayscale modulation is employed to construct hierarchical microstructures within 6 seconds, followed by the direct deposition of conductive layers without the need for molds or vacuum-based techniques. A dome-shaped architecture with a 60 mu m micro-gap is formed, enabling a tactile switching mechanism that suppresses baseline current and results in ultrahigh sensitivity (10,692 kPa-1), rapid response (0.09 s), and low power consumption (0.01 V). Grayscale-modulated masks are utilized to precisely define microstructural features, allowing for the customization of sensors capable of detecting multidirectional forces, including shear and bending. The scalability of the process is demonstrated through the fabrication of 5 x 5 sensor arrays with uniform conductive layer patterning, exhibiting stable performance under both static and dynamic loading conditions, with minimal signal crosstalk (1.6%). Through this work, s-gDLP is showcased as a transformative additive manufacturing tool for the production of flexible, wearable sensors and soft microelectronic devices.

BACKGROUND:High-resolution matrix-assisted laser desorption/ionization-time of flight mass spectrometry (MALDI-TOF MS) and nuclear magnetic resonance (NMR) spectroscopy are powerful tools to identify unknown psychoactive substances. However, in complex matrices, trace levels of unknown substances usually require additional fractionation and concentration. Specialized liquid chromatography systems are necessary for both techniques. The small flow rate of nano LC, typically paired with MALDI-TOF MS, often results in prolonged fractionation times. Conversely, the larger flow rate of semi-preparative LC, used for NMR analysis, can be time-consuming and labor-intensive when concentrating samples. To address these issues, we developed an integrated automatic system that integrated to regular LC. RESULT:Automatic spot collector (ASC) and automatic fraction collector (AFC) were present in this study. The ASC utilized in-line matrix mixing, full-contact spotting and real time heating (50 °C), achieving great capacity of 5 μL droplet on MALDI plate, high recovery (76-116%) and rapid evaporation in 2 min. The analytes were concentrated 4-8 times, forming even crystallization, reaching the detection limit at the concentration of 50 μg L-1 for 12 psychoactive substances in urine. The AFC utilizes flexible tubing which flash-tapped the microtube's upper rim (3 mm depth) instead of reaching the bottom. This method prevents sample loss and minimizes the robotic arm's movement, providing a high fractionating speed at 6 s 12 psychoactive compounds were fractionated in a single round analysis (recovery: 81%-114%). Methamphetamine and nitrazepam obtained from drug-laced coffee samples were successful analyzed with photodiode array (PDA) after one AFC round and NMR after five rounds. SIGNIFICANCE:The ASC device employed real-time heating, in-line matrix mixing, and full-contact spotting to facilitate the samples spotting onto the MALDI target plate, thereby enhancing detection sensitivity in low-concentration and complex samples. The AFC device utilized the novel flash-tapping method to achieve rapid fractionation and high recovery rate. These devices were assembled using commercially available components, making them affordable (400 USD) for most laboratories while still meeting the required performance for advanced commercialized systems.

A need exists for scalable, automated lab-on-chip systems to separate blood plasma for medical diagnostics. In this study, a vacuum-actuated peristaltic micropump (VPM) was developed, incorporating with the inertial microfluidic technique for the separation and collection of blood plasma from diluted blood. The features of the micropump were investigated by varying parameters such as frequency, vacuum pressure, and the number of microchannels. The highest achievable flow rate was found to be 832 mu L/min. Subsequently, to minimize the occurrence of red blood cell rupture during the separation process and significantly reduce hemolysis, the configuration of the vertical wall inside the microchannel was modified to an inclined wall. This improvement was validated through experiments using high-speed cameras and fluorescent particles. Blood plasma separation was achieved with high efficiency (98.5 %), rapidity (<1 min), automation, and minimal whole blood usage (5 L). Importantly, the vacuum actuator with an inclined wall obstruction design demonstrated very low hemolysis (less than 2 %).

Background This study aims to investigate the benefits of employing a Physical Lifelike Brain (PLB) simulator for training medical students in performing craniotomy for glioblastoma removal and decompressive craniectomy. Methods This prospective study included 30 medical clerks (fifth and sixth years in medical school) at a medical university. Before participating in the innovative lesson, all students had completed a standard gross anatomy course as part of their curriculum. The innovative lesson involved PLB Simulator training, after which participants completed the Learning Satisfaction/Confidence Perception Questionnaire and some received qualitative interviews. Results The average score of students' overall satisfaction with the innovative lesson was 4.71 out of a maximum of 5 (SD = 0.34). After the lesson, students' confidence perception level improved significantly (t = 9.38, p < 0.001, effect size = 1.48), and the average score improved from 2,15 (SD = 1.02) to 3.59 (SD = 0.93). 60% of the students thought that the innovative lesson extremely helped them understand the knowledge of surgical neuroanatomy more, 70% believed it extremely helped them improve their skills in burr hole, and 63% thought it was extremely helpful in improving the patient complications of craniotomy with the removal of glioblastoma and decompressive craniectomy after completing the gross anatomy course. Conclusion This innovative lesson with the PLB simulator successfully improved students' craniotomy knowledge and skills.

Integration micro lens arrays (MLAs) into a microfluidic chip has presented outstanding performances in flow cytometry field due to capability of eliminating trade-off between sensitivity and field of view (FOV), allowing high throughput multiplex analysis, and enhancing signal-to-noise ratio (SNR). However, the practices are still remained inside high-end laboratory environment, restricting MLAs to be utilized in point-of-care (POC) based flow cytometry applications. A portable system for droplet flow cytometry was successfully developed, which consists of an optofluidic chip integrated with MLAs and a smartphone application (APP). To cater to the demand for point-of-care testing, a straightforward optical setup that employs inclined LED illumination and micro lens pairs was used. This configuration effectively amplifies fluorescence signals, allowing them to be captured and analyzed solely using a smartphone. Through a series of repeated experiments, clear and consistent results were obtained that highlight the system's capabilities: (1) the smartphone APP installed in the system enabled both quantitative and qualitative analyses of flowing fluorescence droplets. These analyses could be performed in real time, providing immediate detection results, (2) by incorporating MLAs into the detection process, a notable enhancement in detection sensitivity was achieved when compared to cases without MLAs. Specifically, in experiments involving droplet concentrations ranging from 10µm to 25µm, resolvability showed an increase, the sensitivity increased by up to 142%. and the SNR exhibited an 11dB improvement, (3) the customized APP exhibited comparable droplet counting results to a commonly utilized object tracking program. The measurement differences between the two methods ranged from 1.08% to 4.14%, reaffirming the accuracy of our self-developed APP. In conclusion, this portable system, coupled with the smartphone APP and MLAs-based optical configuration, demonstrated the ability to perform real-time, on-site droplet flow cytometry analysis with competitive detection performance.

There is a need for scalable, automated lab-on-chip systems for blood plasma separation that can be applied for diagnostics. Here, we developed a vacuum-actuated peristaltic micropump (VPM) integrating inertial microfluidic technique to separate and collect blood plasma from diluted blood. The device performs blood plasma separation with high efficiency (98.5%), quickly (<1 min), automatically, and with minimal whole blood usage (5 mu L). Significantly, very little hemolysis (less than 2%) could be achieved using an inclined wall obstruction design in the vacuum actuator.

Microneedle arrays (mu NA) are gaining popularity in the field of drug delivery due to their potential to minimize side effects compared to conventional hypodermic needles. Generally, mu NA is manufacturing through three or two stages which lack of flexibility in design. 3d printing is one of the powerful method that could be employed in three or two stages. However, it could be extended to single stage by directly manufacturing mu NA from designated material. Digital light processing (DLP) is a kind of 3d printing which is using in this study. A further understanding of DLP printing parameters would be investigated such as LED current (mA), curing time (sec), z-thickness (mu m), and grayscale level image (0 to 255). This study explores the integration of DLP technology and machine learning (ML), namely DLP-ML, to manufacture mu NA with customizable size, rapid production process, and reduced material wastage. The initial process in developing ML model is preparing a dataset obtained from experimental approach. An orthogonal table is simplified the number of experiment. The orthogonal table is contained five factors and five levels, L-25. The dataset is divided into input and output and substituted to ML system. In this approach, ML algorithms are employed to determine the optimal digital mask patterns and printing parameters for DLP-ML manufacturing. The study is employed various ML configurations in term of algorithm and number of hidden layer. Under different algorithm, the sort of larger to lower accuracy is arranged Bayesian regularization backpropagation (BR10), Levenberg-Marquardt backpropagation (LM10), and scaled conjugate gradient backpropagation (SCG(10)), respectively. It also notices that the deviation of BR10 is proving that mu NA is manufactured uniformly on a substrate. It is ultimately selecting Bayesian regularization backpropagation for its superior accuracy, exceeding 90%. For different number of hidden layer, the default value, 10, is also resulting highest accuracy among others. Then, BR10 could be concluded as the best ML configuration in this case. This algorithm empowers DLP-ML manufacturing to produce mu NA arrays with various aspect ratio (AR), representing the ratio between the height and base diameter of mu NA. The AR of 15 is highest that could be performed and the critical AR of mu NA bending is found at 7 (700/100). By harnessing the synergy of DLP-ML technologies, this research presents a promising avenue for the advancement of drug delivery systems, offering enhanced precision, scalability, and therapeutic efficacy in medical applications. Finally, DLP-ML is successfully developed a rapid mu NA manufacturing with different dimensions very accurately.

Amidst far-reaching COVID-19 effects and social constraints, this study leveraged wastewater-based epidemiology to track 38 conventional drugs and 30 new psychoactive substances (NPS) in northern Taiwan. Analyzing daily samples from four Taipei wastewater plants between September 2021 and January 2024-encompassing club reopenings, holidays, Lunar New Year, an outbreak, and regular periods-thirty-one drugs were detected, including 5 NPS. Tramadol, zolpidem tartrate, CMA, and MDPV were newly detected in Taiwanese sewage with frequency of 1.4 %- 89.0 %. Conventional drug use typically increased post-pandemic, aside from benzodiazepines and methadone. Methamphetamine showed 100 % frequency, indicating ongoing daily consumption despite COVID-19 measures. Methamphetamine and morphine's consumption dipped then rose around club reopening, hinting at limited access. The consumption trend of methadone appeared to compensate for the use of morphine. Ketamine and NPS demonstrated similar patterns throughout the entire period. NPS as party drugs seemed influenced by an unstable supply chain and complexities in implementation. Benzodiazepines, commonly abused alongside synthetic cathinones in Taiwan exhibited an opposing trend to NPS while aligned with acetaminophen, suggesting elevated stress and anxiety levels during the pandemic. No significant differences were observed in drug consumption between weekdays and weekends, potentially indicating that COVID-19 measures blurred the traditional distinctions between these timeframes. ENVIRONMENTAL IMPLICATION: New psychoactive substances refer to chemically modified variants of controlled drugs designed to mimic the effects of the original drugs while evading modern detection methods, categorizing them as hazardous materials. The study presents a sewage monitoring project conducted from 2021 to 2024, collecting samples from four WWTPs to analyze NPS and conventional drug trends during and after the COVID-19 pandemic. The findings uncovered connections between drug consumption patterns and pandemic-related policies. In light of the persistent drug abuse and their environmental presence, the results bear critical importance for both environmental and public health. We provide a thorough assessment of these relationships and prioritize areas for future research.