Abstract: Electrochemical biosensors require electrode materials that are scalable, affordable, and effective for detecting target analyte. Laser-induced graphene (LIG) has emerged as a promising electrode material, yet traditional fabrication methods using expensive CO2 lasers limit accessibility. In this work, we report
Abstract: Microplastics are common environmental contaminants that pose serious threats to ecosystems and human health. Detecting microplastics is challenging and time-consuming, and often requires specialized laboratories, expert knowledge, and lengthy procedures. Here, we introduce “SensorPlast,” a microwave sensor based on
Abstract: Ensuring global food security remains a pressing challenge due to the growing world population and accelerating climate change. Genetic modification provides a pathway to developing crops with improved traits, yet conventional approaches rely heavily on tissue culture regeneration, which
Abstract: AI-powered monitoring platforms can significantly enhance the accessibility and responsiveness of water quality assessment in decentralized and resource-limited settings. Conventional methods for detecting heavy metal ions, such as atomic absorption spectroscopy (AAS), offer high accuracy but require expensive instrumentation,
Being a very simple and cost-effective way to improve crop growth, nanopriming has been widely adopted as an efficient and advanced technology in agriculture. In recent years, there has been a drive to use green synthesized metal oxide nanoparticles (NPs)
Abstract: Microplastic (MP) contamination has become a major concern in recent times, posing a significant threat to the environment and human health. Existing techniques for MP detection require access to expensive and specialized microscopy setups and often demand long turnaround
Abstract: Due to the growing concerns over heavy metal contamination and its adverse effects on environmental and public health, we propose a novel approach for non-contact detection of heavy metals, integrating microwave technology with bacterial inhibition sensing. Our sensor platform
Abstract: Tandem neural networks for inverse design can only make single predictions, which limits the diversity of predicted structures. Here, we use conditional variational autoencoder (cVAE) for the inverse design of core–shell nanoparticles. cVAE is a type of generative neural
Abstract: Basal-bolus insulin therapy is associated with frequent injections, dosing errors, and hypoglycemia risks. Integrating continuous glucose monitoring (CGM) with insulin pumps offers several advantages. However, current CGM systems lack accuracy and have high costs. To overcome these challenges, a
Abstract: In nanophotonics, nanohole arrays (NHAs) are periodic arrangements of nanoscale apertures in thin films that provide diverse optical functionalities essential for various applications. Fully studying NHAs’ optical properties and optimizing performance demands understanding both materials and geometric parameters, which