Sulfate-reducing bacterial (SRB) biofilms are prevalent across natural and engineered environments, mediating biogeochemical sulfur cycling while accelerating biofouling and microbiologically influenced corrosion (MIC). To uncover surface-dependent biofilm adaptations, we performed a comparative transcriptomic analysis of Oleidesulfovibrio alaskensis G20 biofilms formed on copper and...
Dark matter is a mysterious substance that makes up about 25% of the mass-energy of the universe. Its discovery not only helps in understanding the universe itself but also reveals the secrets behind the formation of matter. SuperCDMS SNOLAB, a second-generation physics experiment currently under construction in Sudbury, Ontario, aims to detect dark matter candidate particles that exist beyond...
The objective of our research is to investigate the electrocatalytic properties of novel metal-free quantum dots (QDs) composed of the recently discovered 2D material penta-CN2, with the aim of replacing costly and scarce catalysts such as Pt and Pd. Employing a first-principles density functional theory (DFT) based approach, the geometries of the three penta-CN2 quantum dots (QDs) of...
The pursuit of MeV-scale dark matter detection demands highly sensitive instrumentation capable of resolving energy deposits in the sub-eV range. This study introduces a novel Germanium Internal Charge Amplification (GeICA) detector engineered to internally amplify charge signals, significantly lowering the detection threshold. Leveraging ultra-pure, USD-grown germanium crystals and operating...
Laser-Induced Graphene (LIG) offers a cost-effective, scalable platform for electrochemical sensors, driving advancements in environmental monitoring, agriculture, and health diagnostics. Our studies focus on recent developments in LIG-based ion-selective electrodes for nutrient sensing in soil, supporting precision agriculture, and for non-invasive monitoring of metabolites and electrolytes...
The increasing demand for refractory materials that can withstand high temperature environments necessitates the discovery of advanced alloys for aeronautic and energy applications. This study introduces a computational approach leveraging AFLOW's machine learning frameworks—specifically the Property Labeled Material Fragments (PLMF) and Molar Fragment Descriptor (MFD) methodologies—to...
Abstract
Identification of materials with two-dimensional properties is crucial for the creation of next-generation technologies, yet remains a computationally intensive task. The study presents an automated pipeline designed to predict if a material has two-dimensional characteristics from its physical and structural characteristics. Two-dimensional (2D) materials are a unique category of...
Monitoring pesticide concentration distribution across farm fields is crucial for precise application and minimizing environmental impact. Rapid, on-site detection of pesticide spray is hindered by lack of field-deployable and easy-to-use sensors that circumvent sample transportation to limited laboratories that possess the equipment needed for detection. Laser-induced graphene (LIG) shows...
