An in silico design method of a peptide bioreceptor for cortisol using molecular modelling techniques.

Cortisol is established as a reliable biomarker for stress prompting intensified research in developing wearable sensors to detect it via eccrine sweat. Since cortisol is present in sweat in trace quantities, typically 8-140 ng/mL, developing such biosensors necessitates the design of bioreceptors with appropriate sensitivity and selectivity. In this work, we present a systematic biomimetic methodology and a semi-automated high-throughput screening tool which enables rapid selection of bioreceptors as compared to ab initio design of peptides via computational peptidology.

High dynamic-range and portable magnetometer using ensemble nitrogen-vacancy centers in diamond.

Nitrogen vacancy (NV) centers in diamonds have been explored for a wide range of sensing applications in the last decade due to their unique quantum properties. In this work, we report a compact and portable magnetometer with an ensemble of NV centers, which we call the Quantum MagPI (Quantum Magnetometer with Proportional Integral control). Our fully integrated compact sensor assembly and control electronics fit inside a 10 × 10 × 7 cm3 box and a 30 × 25 × 5 cm3 rack-mountable box, respectively.

Unsupervised deep learning framework for temperature-compensated damage assessment using ultrasonic guided waves on edge device.

Fueled by the rapid development of machine learning (ML) and greater access to cloud computing and graphics processing units, various deep learning based models have been proposed for improving performance of ultrasonic guided wave structural health monitoring (GW-SHM) systems, especially to counter complexity and heterogeneity in data due to varying environmental factors (e.g., temperature) and types of damages.

Simultaneous microbial capture and nucleic acid extraction from wastewater with minimal pre-processing and high recovery efficiency.

The COVID-19 pandemic accelerated research towards developing low-cost assays for automated urban wastewater monitoring assay that can be integrated into an environmental surveillance system for early warning of frequent disease outbreaks and future pandemics. Microbial concentration is one of the most challenging steps in wastewater surveillance, due to the sample heterogeneity and low pathogen load. Keeping in mind the requirements of large-scale testing in densely populated low- or middle-income countries (LMICs), such assays would need to be low-cost and have rapid turnaround time with high recovery efficiency.