Electrochemical measurements play a crucial role across various domains including air quality assessment, biological analysis, and the food industry. Miniaturized and power-efficient electrochemical potentiostats, facilitated by integrated circuits, have been instrumental in enabling wearable devices. However, the utilization of modern CMOS technologies with low supply voltage limits the applicability of electrochemical reactions requiring higher potential windows. This paper introduces an innovative circuit architecture that extends the electrochemical cell voltage range by 46% for positive voltages and 88% for negative voltages compared to conventional designs. Consequently, this advancement widens the spectrum of supported bias voltages within an electrochemical cell, thereby expanding the scope of integrated potentiostat to encompass a broader array of electrochemical reactions. Implemented in CMOS 180 nm technology, the circuit consumes 2.047 mW of power. It supports a bias potential range from 1.1 V to -2.12 V and a cell potential range from 2.41 V to -3.11 V.

This work presents a microfluidic flow cell for the electrochemical detection of bioaerosols using functionalized, N-hydroxy succinimide (NHS) ester-coated, planar microfabricated Ti/Au electrodes. Experimental validation of biological detection has been displayed in this work by using bovine serum albumin (BSA) as an airborne biologic model, and specificity was validated using black carbon as a common aerosolized particle.

The impact of suspended particles on health, climate, and industrial applications is highly size-dependent. Thus, regulations are typically based on particles with diameters below a specific size, such as particulate matter less than 2.5 mu m (PM2.5). For over a century, cyclones have been employed to isolate particles below a certain diameter by removing large particles from a gas stream, but cyclones are typically relatively large, heavy, and expensive to fabricate compared to objects made with low-cost 3-dimensional (3D) printers. Herein, we present one-piece 3D-printed micro-cyclones (PM2.5 and PM1) to isolate particles smaller than a specific diameter. The collection efficiencies and 50% cutoff diameters (d(50)) of multiple cyclones were evaluated with both monodisperse and polydisperse standards ranging from 0.1 to 3 mu m, as well as ambient aerosols. By altering the inlet orientation relative to the micro-cyclone centerline (orthogonal, 50% offset, and fully offset), we show that shifting the inlet radially outward increased the steepness of the transmission curve, resulting in a sharper cut-point. The d(50) also decreased below the designed for diameter (PM1 = 1.4, 1.0, and 0.9 mu m; PM2.5 = 3.2, 2.0, and 1.9 mu m), which was attributed to imperfect models, internal surface roughness, and print errors versus machining. These single-piece, 3D-printed cyclones provide a cheaper (<$1), faster, and more accessible approach to manufacture micro-cyclones for use in a range of aerosol applications.

Electrochemical measurements are vital to a wide range of applications such as air quality monitoring, biological testing, food industry, and more. Integrated circuits have been used to implement miniaturized and low-power electrochemical potentiostats that are suitable for wearable devices. However, employing modern integrated circuit technologies with low supply voltage precludes the utilization of electrochemical reactions that require a higher potential window. In this paper, we present a novel circuit architecture that utilizes dynamic voltage at the working electrode of an electrochemical cell to effectively enhance the supported voltage range compared to traditional designs, increasing the cell voltage range by 46% and 88% for positive and negative cell voltages, respectively. In return, this facilitates a wider range of bias voltages in an electrochemical cell, and, therefore, opens integrated microsystems to a broader class of electrochemical reactions. The circuit was implemented in 180 nm technology and consumes 2.047 mW of power. It supports a bias potential range of 1.1 V to −2.12 V and cell potential range of 2.41 V to −3.11 V that is nearly double the range in conventional designs.

A long-standing challenge in lab-on-chip and biomicrofluidic sensing modes is the formation of reliable, leak-free bonding on the surface of wafers or CMOS chips having sensing electrodes typically formed by thin film metal deposition. This challenge is particularly evident in the design of polydimethylsiloxane (PDMS) flow channels for electrochemical analysis where the need for high-density electrodes increases the number of metal-PDMS interface points. This work presents a fabrication method for creating leak free bonding of PDMS on Au electrodes by coating the substrate in a low-temperature plasma enhanced chemical vapor deposited dielectric material, thereby exposing only the sensing area within the channel. Furthermore, this fabrication process was used to create the first-known impact electrochemistry flow cell capable of continuous measurement of air-borne particulate matter using microfluidics over a surface containing microfabricated gold electrodes. Functionality of the device for air pollution monitoring was validated by detecting black carbon particles using impact electrochemistry at 0.8V.

Modern sensor technologies have been employed to monitor aspects of social interactions, such as human emotions, that are known to influence human health. To reduce the negative impact that some behaviors could have on our health, real-time monitoring of social interactions is desired to bring awareness of human behavior through real-time feedback. Still, the design of systems for the real-time monitoring of social interactions poses considerable challenges that range from multi-sensor integration to signal analysis. Intending to overcome these challenges, this paper presents a study of a variety of sensor modalities and the design of a multi-sensor framework that allows the study and real-time analysis of both in-person and virtual social interaction environments. The framework consists of three multi-sensor nodes and a central unit. Results show validation of the variety of sensor data collected from a single sensor node and behavioral information that can be identified due to data synchronization from multiple sensor nodes.