What can AI-TENG do for Low Abundance Biosensing?

Nanocellulose-based nanogenerators for sensor applications: A review

With the rapid development of the Internet of Things, nanogenerator as a green energy collection technology has attracted great attention in various fields. Specifically, the natural renewable nanocellulose as a raw material can significantly improve the environmental friendliness of the nanocellulose-based nanogenerators, which also makes the nanocellulose based nanogenerators expected to further develop in areas such as wearable devices and sensor networks. This paper mainly reports the application of nanocellulose in nanogenerator, focusing on the sensor. The types, sources and preparation methods of nanocellulose are briefly introduced. At the same time, the special structure of nanocellulose highlights the advantages of nanocellulose in nanogenerators. Then, the application of nanocellulose-based nanogenerators in sensors is introduced. Finally, the future development prospects and shortcomings of this nanogenerator are discussed.

Smart Nanozymes for Cancer Therapy: The Next Frontier in Oncology

Nanomaterials that mimic the catalytic activity of natural enzymes in the complex biological environment of the human body are called nanozymes. Recently, nanozyme systems have been reported with diagnostic, imaging, and/or therapeutic capabilities. Smart nanozymes strategically exploit the tumor microenvironment (TME) by the in situ generation of reactive species or by the modulation of the TME itself to result in effective cancer therapy. This topical review focuses on such smart nanozymes for cancer diagnosis, and therapy modalities with enhanced therapeutic effects. The dominant factors that guide the rational design and synthesis of nanozymes for cancer therapy include an understanding of the dynamic TME, structure-activity relationships, surface chemistry for imparting selectivity, and site-specific therapy, and stimulus-responsive modulation of nanozyme activity. This article presents a comprehensive analysis of the subject including the diverse catalytic mechanisms of different types of nanozyme systems, an overview of the TME, cancer diagnosis, and synergistic cancer therapies. The strategic application of nanozymes in cancer treatment can well be a game changer in future oncology. Moreover, recent developments may pave the way for the deployment of nanozyme therapy into other complex healthcare challenges, such as genetic diseases, immune disorders, and ageing.

Insight into continuous glucose monitoring: from medical basics to commercialized devices

According to the latest statistics, more than 537 million people around the world struggle with diabetes and its adverse consequences. As well as acute risks of hypo- or hyper- glycemia, long-term vascular complications may occur, including coronary heart disease or stroke, as well as diabetic nephropathy leading to end-stage disease, neuropathy or retinopathy. Therefore, there is an urgent need to improve diabetes management to reduce the risk of complications but also to improve patient's quality life. The impact of continuous glucose monitoring (CGM) is well recognized, in this regard. The current review aims at introducing the basic principles of glucose sensing, including electrochemical and optical detection, summarizing CGM technology, its requirements, advantages, and disadvantages. The role of CGM systems in the clinical diagnostics/personal testing, difficulties in their utilization, and recommendations are also discussed. In the end, challenges and prospects in future CGM systems are discussed and non-invasive, wearable glucose biosensors are introduced. Though the scope of this review is CGMs and provides information about medical issues and analytical principles, consideration of broader use will be critical in future if the right systems are to be selected for effective diabetes management.

Al-Sn-Al Bonding Strength Investigation Based on Deep Learning Model

Al-Sn-Al wafer bonding is a new semiconductor manufacturing technology that plays an important role in device manufacturing. Optimization of the bonding process and testing of the bonding strength remain key issues. However, using only physical experiments to study the above problems presents difficulties such as repeating many experiments, high costs, and low efficiency. Deep learning algorithms can quickly simulate complex physical correlations by training large amounts of data, which is a good solution to the difficulties in studying wafer bonding. Therefore, this paper proposes the use of deep learning models (2-layer CNN and 50-layer ResNet) to achieve autonomous recognition of bonding strengths corresponding to different bonding conditions, and the results from a comparative test set show that the ResNet model has an accuracy of 99.17%, outperforming the CNN model with an accuracy of 91.67%. Then, the identified images are analyzed using the Canny edge detector, which showed that the fracture surface morphology of the wafer is a hole-shaped structure, with the smaller the area of hole movement on the wafer surface, the higher the bonding strength. In addition, the effects of bonding time and bonding temperature on bonding strength are verified, showing that relatively short bonding times and relatively low bonding temperatures resulted in better wafer bonding strength. This research demonstrates the potential of using deep learning to accelerate wafer bonding strength identification and process condition optimization.