@article{turner_twiddy_wilkins_ramesh_kilgour_domingos_nasrallah_menegatti_daniele_2023, title={Biodegradable elastomeric circuit boards from citric acid-based polyesters}, volume={7}, ISSN={["2397-4621"]}, url={https://doi.org/10.1038/s41528-023-00258-z}, DOI={10.1038/s41528-023-00258-z}, abstractNote={AbstractRecyclable 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.}, number={1}, journal={NPJ FLEXIBLE ELECTRONICS}, author={Turner, Brendan L. and Twiddy, Jack and Wilkins, Michael D. and Ramesh, Srivatsan and Kilgour, Katie M. and Domingos, Eleo and Nasrallah, Olivia and Menegatti, Stefano and Daniele, Michael A.}, year={2023}, month={Jun} } @article{yang_nithyanandam_kanetkar_kwon_ma_im_oh_shamsi_wilkins_daniele_et al._2023, title={Liquid Metal Coated Textiles with Autonomous Electrical Healing and Antibacterial Properties}, volume={4}, ISSN={["2365-709X"]}, DOI={10.1002/admt.202202183}, abstractNote={AbstractConductive textiles are promising for human–machine interfaces and wearable electronics. A simple way to create conductive textiles by coating fabric with liquid metal (LM) particles is reported. The coating process involves dip‐coating the fabric into a suspension of LM particles at room temperature. Despite being coated uniformly after drying, the textiles remain electrically insulating due to the native oxide that forms on the LM particles. Yet, they can be rendered conductive by compressing the textile to rupture the oxide and thereby percolate the particles. Thus, compressing the textile with a patterned mold can pattern conductive circuits on the textile. The electrical conductivity of these circuits increases by coating more particles on the textile. Notably, the conductive patterns autonomously heal when cut by forming new conductive paths along the edge of the cut. The textiles prove to be useful as circuit interconnects, Joule heaters, and flexible electrodes to measure ECG signals. Further, the LM‐coated textiles provide antimicrobial protection against Pseudomonas aeruginosa and Staphylococcus aureus. Such simple coatings provide a route to convert otherwise insulating textiles into electrical circuits with the ability to autonomously heal and provide antimicrobial properties.}, journal={ADVANCED MATERIALS TECHNOLOGIES}, author={Yang, Jiayi and Nithyanandam, Praneshnandan and Kanetkar, Shreyas and Kwon, Ki Yoon and Ma, Jinwoo and Im, Sooik and Oh, Ji-Hyun and Shamsi, Mohammad and Wilkins, Mike and Daniele, Michael and et al.}, year={2023}, month={Apr} } @article{martins_wilkins_ligler_daniele_freytes_2021, title={Microphysiological System for High-Throughput Computer Vision Measurement of Microtissue Contraction}, volume={6}, ISSN={["2379-3694"]}, url={https://doi.org/10.1021/acssensors.0c02172}, DOI={10.1021/acssensors.0c02172}, abstractNote={The ability to measure microtissue contraction in vitro can provide important information when modeling cardiac, cardiovascular, respiratory, digestive, dermal, and skeletal tissues. However, measuring tissue contraction in vitro often requires the use of high number of cells per tissue construct along with time-consuming microscopy and image analysis. Here, we present an inexpensive, versatile, high-throughput platform to measure microtissue contraction in a 96-well plate configuration using one-step batch imaging. More specifically, optical fiber microprobes are embedded in microtissues, and contraction is measured as a function of the deflection of optical signals emitted from the end of the fibers. Signals can be measured from all the filled wells on the plate simultaneously using a digital camera. An algorithm uses pixel-based image analysis and computer vision techniques for the accurate multiwell quantification of positional changes in the optical microprobes caused by the contraction of the microtissues. Microtissue constructs containing 20,000-100,000 human ventricular cardiac fibroblasts (NHCF-V) in 6 mg/mL collagen type I showed contractile displacements ranging from 20-200 μm. This highly sensitive and versatile platform can be used for the high-throughput screening of microtissues in disease modeling, drug screening for therapeutics, physiology research, and safety pharmacology.}, number={3}, journal={ACS SENSORS}, author={Martins, Ana Maria Gracioso and Wilkins, Michael D. and Ligler, Frances S. and Daniele, Michael A. and Freytes, Donald O.}, year={2021}, month={Mar}, pages={985–994} } @article{chen_wilkins_barahona_rosenbaum_daniele_lobaton_2021, title={Toward Automated Analysis of Fetal Phonocardiograms: Comparing Heartbeat Detection from Fetal Doppler and Digital Stethoscope Signals}, ISSN={["1558-4615"]}, url={http://dx.doi.org/10.1109/embc46164.2021.9629814}, DOI={10.1109/EMBC46164.2021.9629814}, abstractNote={Longitudinal fetal health monitoring is essential for high-risk pregnancies. Heart rate and heart rate variability are prime indicators of fetal health. In this work, we implemented two neural network architectures for heartbeat detection on a set of fetal phonocardiogram signals captured using fetal Doppler and a digital stethoscope. We test the efficacy of these networks using the raw signals and the hand-crafted energy from the signal. The results show a Convolutional Neural Network is the most efficient at identifying the S1 waveforms in a heartbeat, and its performance is improved when using the energy of the Doppler signals. We further discuss issues, such as low Signal-to-Noise Ratios (SNR), present in the training of a model based on the stethoscope signals. Finally, we show that we can improve the SNR, and subsequently the performance of the stethoscope, by matching the energy from the stethoscope to that of the Doppler signal.}, journal={2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)}, publisher={IEEE}, author={Chen, Yuhan and Wilkins, Michael D. and Barahona, Jeffrey and Rosenbaum, Alan J. and Daniele, Michael and Lobaton, Edgar}, year={2021}, pages={975–979} } @inproceedings{keller_wilkins_reynolds_dieffenderfer_hood_daniele_bozkurt_tunc-ozdemir_2016, title={Nanocellulose electrodes for interfacing plant electrochemistry}, booktitle={2016 ieee sensors}, author={Keller, K. and Wilkins, M. and Reynolds, J. and Dieffenderfer, J. and Hood, C. and Daniele, M. A. and Bozkurt, A. and Tunc-Ozdemir, M.}, year={2016} } @inproceedings{dieffenderfer_wilkins_hood_beppler_daniele_bozkurt_2016, title={Towards a sweat-based wireless and wearable electrochemical sensor}, booktitle={2016 ieee sensors}, author={Dieffenderfer, J. and Wilkins, M. and Hood, C. and Beppler, E. and Daniele, M. A. and Bozkurt, A.}, year={2016} }