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Overcoming the trade-offs among power consumption, fabrication cost, and signal amplification has been a long-standing issue for wearable electronics. We report a high-gain, fully inkjet-printed Schottky barrier organic thin-film transistor amplifier circuit. The transistor signal amplification efficiency is 38.2 siemens per ampere, which is near the theoretical thermionic limit, with an ultralow power consumption of <1 nanowatt. The use of a Schottky barrier for the source gave the transistor geometry-independent electrical characteristics and accommodated the large dimensional variation in inkjet-printed features. These transistors exhibited good reliability with negligible threshold-voltage shift. We demonstrated this capability with an ultralow-power high-gain amplifier for the detection of electrophysiological signals and showed a signal-to-noise ratio of >60 decibels and noise voltage of <0.3 microvolt per hertz1/2 at 100 hertz.

Chronic obstructive pulmonary disease (COPD) and lung cancer, closely related to smoking, are major lung diseases affecting millions of individuals worldwide. The generated gas mixture of smoking is proved to contain about 4,500 components such as carbon monoxide, nicotine, oxidants, fine particulate matter, and aldehydes. These components were considered to be the principle factor driving the pathogenesis and progression of pulmonary disease. A large proportion of lung cancer patients showed a history of COPD, which demonstrated that there might be a close relationship between COPD and lung cancer. In the early stages of smoking, lung barrier provoked protective response and DNA repair are likely to suppress these changes to a certain extent. In the presence of long-term smoking exposure, these mechanisms seem to be malfunctioned and lead to disease progression. The infiltration of inflammatory cells to mucosa, submucosa, and glandular tissue caused by inhaled cigarette smoke is responsible for the destruction of matrix, blood supply shortage, and epithelial cell death. Conversely, cancer cells have the capacity to modulate the proliferation of epithelial cells and produce of new vascular networks. Comprehension understanding of mechanisms responsible for both pathologies is necessary for the prevention and treatment of COPD and lung cancer. In this review, we will summarize related articles and give a glance of possible mechanism between cigarette smoking induced COPD and lung cancer.

Microfluidics can precisely control and manipulate micro-scale fluids, and are also known as lab-on-a-chip or micro total analysis systems. Microfluidics have huge application potential in biology, chemistry, and medicine, among other fields. Coupled with a suitable detection system, the detection and analysis of small-volume and low-concentration samples can be completed. This paper reviews an optical imaging system combined with microfluidics, including bright-field microscopy, chemiluminescence imaging, spectrum-based microscopy imaging, and fluorescence-based microscopy imaging. At the end of the article, we summarize the advantages and disadvantages of each imaging technology.

With the continuous expansion of pixel arrays in digital microfluidics (DMF) chips, precise droplet control has emerged as a critical issue requiring detailed consideration. This paper proposes a novel capacitance-based droplet sensing system for thin-film transistor DMF. The proposed circuit features a distinctive inner and outer dual-pixel electrode structure, integrating droplet driving and sensing functionalities. Discharge occurs exclusively at the inner electrode during droplet sensing, effectively addressing droplet perturbation in existing sensing circuits. The circuit employs a novel fan-shaped structure of thin-film transistors. Simulation results show that it can provide a 48 V pixel voltage and demonstrate a sensing voltage difference of over 10 V between deionized water and silicone oil, illustrating its proficiency in droplet driving and accurate sensing. The stability of threshold voltage drift and temperature was also verified for the circuit. The design is tailored for integration into active matrix electrowetting-on-dielectric (AM-EWOD) chips, offering a novel approach to achieve precise closed-loop control of droplets.

In this study, an “all-in-one” digital microfluidics (DMF) system was developed for automatic and rapid molecular diagnosis and integrated with magnetic bead-based nucleic acid extraction, loop-mediated isothermal amplification (LAMP), and real-time optical signal monitoring. First, we performed on- and off-chip comparison experiments for the magnetic bead nucleic acid extraction module and LAMP amplification function. The extraction efficiency for the on-chip test was comparable to that of conventional off-chip methods. The processing time for the automatic on-chip workflow was only 23 min, which was less than that of the conventional methods of 28 min 45 s. Meanwhile, the number of samples used in on-chip experiments was significantly smaller than that used in off-chip experiments; only 5 µL of E. coli samples was required for nucleic acid extraction, and 1 µL of the nucleic acid template was needed for the amplification reaction. In addition, we selected SARS-CoV-2 nucleic acid reference materials for the nucleic acid detection experiment, demonstrating a limit of detection of 10 copies/µL. The proposed “all-in-one” DMF system provides an on-site “sample to answer” time of approximately 60 min, which can be a powerful tool for point-of-care molecular diagnostics.

An active-matrix electrowetting-on-dielectric (AM-EWOD) system integrates hundreds of thousands of active electrodes for sample droplet manipulation, which can enable simultaneous, automatic, and parallel on-chip biochemical reactions. A smart detection system is essential for ensuring a fully automatic workflow and online programming for the subsequent experimental steps. In this work, we demonstrated an artificial intelligence (AI)-enabled multipurpose smart detection method in an AM-EWOD system for different tasks. We employed the U-Net model to quantitatively evaluate the uniformity of the applied droplet-splitting methods. We used the YOLOv8 model to monitor the droplet-splitting process online. A 97.76% splitting success rate was observed with 18 different AM-EWOD chips. A 99.982% model precision rate and a 99.980% model recall rate were manually verified. We employed an improved YOLOv8 model to detect single-cell samples in nanolitre droplets. Compared with manual verification, the model achieved 99.260% and 99.193% precision and recall rates, respectively. In addition, single-cell droplet sorting and routing experiments were demonstrated. With an AI-based smart detection system, AM-EWOD has shown great potential for use as a ubiquitous platform for implementing true lab-on-a-chip applications.

Biological applications of core/shell near-infrared quantum dots (QDs) have attracted broad interest due to their unique optical and chemical properties. Additionally, the use of multifunctional nanomaterials with near-infrared QDs and plasmonic functional nanoparticles are promising for applications in electronics, bioimaging, energy, and environmental-related studies. In this work, we experimentally demonstrate how to construct a multifunctional nanoparticle comprised of CdSe/ZnS QDs and gold nanorods (GNRs) where the GNRs were applied to enhance the photoluminescence (PL) of the CdSe/ZnS QDs. In particular, we have obtained the scattering PL spectrum of a single CdSe/ZnS QD and GNR@CdSe/ZnS nanoparticle and comparison results show that the CdSe/ZnS QDs have an apparent PL enhancement of four-times after binding with GNRs. In addition, in vitro experimental results show that the biostability of the GNR@CdSe/ZnS nanoparticles can be improved by using folic acid. A bioimaging study has also been performed where GNR@CdSe/ZnS nanoparticles were used as an optical process for MCF-7 breast cancer cells.

Sorting specific cells from heterogeneous samples is important for research and clinical applications. In this work, a novel label‐free cell sorting method is presented that integrates deep learning image recognition with microfluidic manipulation to differentiate cells based on morphology. Using an Active‐Matrix Digital Microfluidics (AM‐DMF) platform, the YOLOv8 object detection model ensures precise droplet classification, and the Safe Interval Path Planning algorithm manages multi‐target, collision‐free droplet path planning. Simulations and experiments revealed that detection model precision, concentration ratios, and sorting cycles significantly affect recovery rates and purity. With HeLa cells and polystyrene beads as samples, the method achieved 98.5% sorting precision, 96.49% purity, and an 80% recovery over three cycles. After a series of experimental validations, this method can also be used to sort HeLa cells from red blood cells, cancer cells from white blood cells (represented by HeLa and Jurkat cells), and differentiate white blood cell subtypes (represented by HL‐60 cells and Jurkat cells). Cells sorted using this method can be lysed directly on chip within their hosting droplets, ensuring minimal sample loss and suitability for downstream bioanalysis. This innovative AM‐DMF cell sorting technique holds significant potential to advance diagnostics, therapeutics, and fundamental research in cell biology. This paper presents a novel label‐free cell sorting method using Active‐Matrix Digital Microfluidics and YOLOv8 object detection model. The method demonstrates high purity and precision in sorting HeLa cells, red blood cells, and white blood cell subtypes, showcasing its potential for applications in cell sorting, omics research, single cell research, and drug development.

Accurate prediction of distributed photovoltaic (DPV) power generation is crucial for stable grid operation, yet existing methods struggle with the non-linear, intermittent nature of solar power, and traditional machine learning models face hyperparameter selection and overfitting challenges. This study developed a highly accurate DPV power prediction method by optimizing a Long Short-Term Memory (LSTM) network's hyperparameters using an improved Multi-Objective Particle Swarm Optimization (MO-PSO) algorithm. A hybrid LSTM-PSO model was created, where the LSTM network served as the core prediction model, and the improved MO-PSO algorithm optimized its hyperparameters, enhancing generalization and avoiding overfitting. The LSTM-PSO model significantly improved prediction accuracy compared to traditional methods. Key results from two power stations included a maximum deviation of 6.2 MW at Power Station A, a peak time deviation of less than 0.1 MW at Power Station B, and a prediction interval error controlled below 30 MW at an 80% confidence level. The optimized LSTM-PSO model effectively captures DPV power generation dynamics, and the superior performance metrics demonstrate its potential for intelligent grid management. However, limitations include prediction accuracy under extreme weather and computational efficiency for large datasets. Future work will focus on broader applicability and more efficient algorithm variants.

Herein, we describe a novel integrated biosensor for performing dielectric spectroscopy to analyze biological samples. We analyzed biomolecule samples with different concentrations and demonstrated that the solution's impedance is highly correlated with the concentration, indicating that it may be possible to use this sensor as a concentration sensor. In contrast with standard spectrophotometers, this sensor offers a low-cost and purely electrical solution for the quantitative analysis of biomolecule solutions. In addition to determining concentrations, we found that the sample solution impedance is highly correlated with the length of the DNA fragments, indicating that the sizes of PCR products could be validated with an integrated chip-based, sample-friendly system within a few minutes. The system could be the basis of a rapid, low-cost platform for DNA characterization with broad applications in cancer and genetic disease research.