Glutamate and dopamine (DA) represent two key contributors to striatal functioning, a region of the brain that is essential to motor coordination and motivated behavior. While electroanalytical techniques can be utilized for rapid, spatially resolved detection of DA in the interferent-rich brain environment, glutamate, a nonelectroactive analyte, cannot be directly detected using electroanalytical techniques. However, it can be probed using enzyme-based sensors, which generate an electroactive reporter in the presence of glutamate. The vast majority of glutamate biosensors have relied on amperometric sensing, which is an inherently nonselective detection technique. This approach necessitates the use of complex and performance-limiting modifications to ensure the desired single-analyte specificity. Here, we present a novel glutamate microbiosensor fabricated on a carbon-fiber microelectrode substrate and coupled with fast-scan cyclic voltammetry (FSCV) to enable the simultaneous quantification of glutamate and DA at single recording sites in the brain, which is impossible when using typical amperometric approaches. The glutamate microbiosensors were characterized for sensitivity, stability, and selectivity by using a voltammetric waveform optimized for the simultaneous detection of both species. The applicability of these sensors for the investigation of neural circuits was validated in the rat ventral striatum. Electrically evoked glutamate and DA release were recorded at single-micrometer-scale locations before and after pharmacological manipulation of glutamatergic signaling. Our novel glutamate microbiosensor advances the state of the art by providing a powerful tool for probing coordination between these two species in a way that has previously not been possible.

Carbon nanotubes (CNTs) are known for their excellent conductive properties. Here, we present two novel methods, "sandwich" (sCNT) and dual deposition (DD CNT), for incorporating CNTs into electrospun polycaprolactone (PCL) and gelatin scaffolds to increase their conductance. Based on CNT percentage, the DD CNT scaffolds contain significantly higher quantities of CNTs than the sCNT scaffolds. The inclusion of CNTs increased the electrical conductance of scaffolds from 0.0 ± 0.00 kS (non-CNT) to 0.54 ± 0.10 kS (sCNT) and 5.22 ± 0.49 kS (DD CNT) when measured parallel to CNT arrays and to 0.25 ± 0.003 kS (sCNT) and 2.85 ± 1.12 (DD CNT) when measured orthogonally to CNT arrays. The inclusion of CNTs increased fiber diameter and pore size, promoting cellular migration into the scaffolds. CNT inclusion also decreased the degradation rate and increased hydrophobicity of scaffolds. Additionally, CNT inclusion increased Young's modulus and failure load of scaffolds, increasing their mechanical robustness. Murine fibroblasts were maintained on the scaffolds for 30 days, demonstrating high cytocompatibility. The increased conductivity and high cytocompatibility of the CNT-incorporated scaffolds make them appropriate candidates for future use in cardiac and neural tissue engineering.

Recyclable and biodegradable microelectronics, i.e., “green” electronics, are emerging as a viable solution to the global challenge of electronic waste. Specifically, flexible circuit boards represent a prime target for materials development and increasing the utility of green electronics in biomedical applications. Circuit board substrates and packaging are good dielectrics, mechanically and thermally robust, and are compatible with microfabrication processes. Poly(octamethylene maleate (anhydride) citrate) (POMaC) – a citric acid-based elastomer with tunable degradation and mechanical properties – presents a promising alternative for circuit board substrates and packaging. Here, we report the characterization of Elastomeric Circuit Boards (ECBs). Synthesis and processing conditions were optimized to achieve desired degradation and mechanical properties for production of stretchable circuits. ECB traces were characterized and exhibited sheet resistance of 0.599 Ω cm⁻², crosstalk distance of <0.6 mm, and exhibited stable 0% strain resistances after 1000 strain cycles to 20%. Fabrication of single layer and encapsulated ECBs was demonstrated.

Microneedle-based platform enables passive, zero-power extraction of interstitial fluid from skin models, demonstrating rapid sampling kinetics, efficient uptake, and accurate quantitative recovery of cortisol for wearable diagnostic applications.

Knee osteoarthritis is a major cause of global disability and is a major cost for the healthcare system. Lower extremity loading is a determinant of knee osteoarthritis onset and progression; however, technology that assists rehabilitative clinicians in optimizing key metrics of lower extremity loading is significantly limited. The peak vertical component of the ground reaction force (vGRF) in the first 50% of stance is highly associated with biological and patient-reported outcomes linked to knee osteoarthritis symptoms. Monitoring and maintaining typical vGRF profiles may support healthy gait biomechanics and joint tissue loading to prevent the onset and progression of knee osteoarthritis. Yet, the optimal number of sensors and sensor placements for predicting accurate vGRF from accelerometry remains unknown. Our goals were to: 1) determine how many sensors and what sensor locations yielded the most accurate vGRF loading peak estimates during walking; and 2) characterize how prescribing different loading conditions affected vGRF loading peak estimates. We asked 20 young adult participants to wear 5 accelerometers on their waist, shanks, and feet and walk on a force-instrumented treadmill during control and targeted biofeedback conditions prompting 5% underloading and overloading vGRFs. We trained and tested machine learning models to estimate vGRF from the various sensor accelerometer inputs and identified which combinations were most accurate. We found that a neural network using one accelerometer at the waist yielded the most accurate loading peak vGRF estimates during walking, with average errors of 4.4% body weight. The waist-only configuration was able to distinguish between control and overloading conditions prescribed using biofeedback, matching measured vGRF outcomes. Including foot or shank acceleration signals in the model reduced accuracy, particularly for the overloading condition. Our results suggest that a system designed to monitor changes in walking vGRF or to deploy targeted biofeedback may only need a single accelerometer located at the waist for healthy participants.