ACS applied materials & interfaces [ACS Appl Mater Interfaces] 2022 Mar 30; Vol. 14 (12), pp. 14774-14782. Date of Electronic Publication: 2022 Mar 17.
This paper demonstrates laser forming, localized heating with a laser to induce plastic deformation, can self-fold 2D printed circuit boards (PCBs) into 3D structures with electronic function. There are many methods for self-folding but few are compatible with electronic materials. We use a low-cost commercial laser writer to both cut and fold a commercial flexible PCB. Laser settings are tuned to select between cutting and folding with higher power resulting in cutting and lower power resulting in localized heating for folding into 3D shapes. Since the thin copper traces used in commercial PCBs are highly reflective and difficult to directly fold, two approaches are explored for enabling folding: plating with a nickel/gold coating or using a single, high-power laser exposure to oxidize the surface and improve laser absorption. We characterized the physical effect of the exposure on the sample as well as the fold angle as a function of laser passes and demonstrate the ability to lift weights comparable with circuit packages and passive components. This technique can form complex, multifold structures with integrated electronics; as a demonstrator, we fold a commercial board with a common timing circuit. Laser forming to add a third dimension to printed circuit boards is an important technology to enable the rapid prototyping of complex 3D electronics.
Gan R, Cabezas MD, Pan M, Zhang H, Hu G, Clark LG, Jewett MC, and Nicol R
ACS synthetic biology [ACS Synth Biol] 2022 May 12. Date of Electronic Publication: 2022 May 12.
Engineering regulatory parts for improved performance in genetic programs has played a pivotal role in the development of the synthetic biology cell programming toolbox. Here, we report the development of a novel high-throughput platform for regulatory part prototyping and analysis that leverages the advantages of engineered DNA libraries, cell-free protein synthesis (CFPS), high-throughput emulsion droplet microfluidics, standard flow sorting adapted to screen droplet reactions, and next-generation sequencing (NGS). With this integrated platform, we screened the activity of millions of genetic parts within hours, followed by NGS retrieval of the improved designs. This in vitro platform is particularly valuable for engineering regulatory parts of nonmodel organisms, where in vivo high-throughput screening methods are not readily available. The platform can be extended to multipart screening of complete genetic programs to optimize yield and stability.
We present a low-cost, accessible, and rapid fabrication process for electrochemical microfluidic sensors. This work leverages the accessibility of consumer-grade electronic craft cutters as the primary tool for patterning of sensor electrodes and microfluidic circuits, while commodity materials such as gold leaf, silver ink pen, double-sided tape, plastic transparency films, and fabric adhesives are used as its base structural materials. The device consists of three layers, the silver reference electrode layer at the top, the PET fluidic circuits in the middle and the gold sensing electrodes at the bottom. Separation of the silver reference electrode from the gold sensing electrodes reduces the possibility of cross-contamination during surface modification. A novel approach in mesoscale patterning of gold leaf electrodes can produce generic designs with dimensions as small as 250 μm. Silver electrodes with dimensions as small as 385 μm were drawn using a plotter and a silver ink pen, and fluid microchannels as small as 300 μm were fabricated using a sandwich of iron-on adhesives and PET. Device layers are then fused together using an office laminator. The integrated microfluidic electrochemical platform has electrode kinetics/performance of Δ Ep = 91.3 mV, Ipa / Ipc = 0.905, characterized by cyclic voltammetry using a standard ferrocyanide redox probe, and this was compared against a commercial screen-printed gold electrode (Δ Ep = 68.9 mV, Ipa / Ipc = 0.984). To validate the performance of the integrated microfluidic electrochemical platform, a catalytic hydrogen peroxide sensor and enzyme-coupled glucose biosensors were developed as demonstrators. Hydrogen peroxide quantitation achieves a limit of detection of 0.713 mM and sensitivity of 78.37 μA mM -1 cm -2 , while glucose has a limit of detection of 0.111 mM and sensitivity of 12.68 μA mM -1 cm -2 . This rapid process allows an iterative design-build-test cycle in under 2 hours. The upfront cost to set up the system is less than USD 520, with each device costing less than USD 0.12, making this manufacturing process suitable for low-resource laboratories or classroom settings.
Lim SW, Choi IS, Lee BN, Ryu J, Park HJ, and Cho JH
American journal of orthodontics and dentofacial orthopedics : official publication of the American Association of Orthodontists, its constituent societies, and the American Board of Orthodontics [Am J Orthod Dentofacial Orthop] 2022 Mar 11. Date of Electronic Publication: 2022 Mar 11.
Human gait is a unique behavioral characteristic that can be used to recognize individuals. Collecting gait information widely by the means of wearable devices and recognizing people by the data has become a topic of research. While most prior studies collected gait information using inertial measurement units, we gather the data from 40 people using insoles, including pressure sensors, and precisely identify the gait phases from the long time series using the pressure data. In terms of recognizing people, there have been a few recent studies on neural network-based approaches for solving the open set gait recognition problem using wearable devices. Typically, these approaches determine decision boundaries in the latent space with a limited number of samples. Motivated by the fact that such methods are sensitive to the values of hyper-parameters, as our first contribution, we propose a new network model that is less sensitive to changes in the values using a new prototyping encoder-decoder network architecture. As our second contribution, to overcome the inherent limitations due to the lack of transparency and interpretability of neural networks, we propose a new module that enables us to analyze which part of the input is relevant to the overall recognition performance using explainable tools such as sensitivity analysis (SA) and layer-wise relevance propagation (LRP).