An in vitro demonstration of a passive,acoustic metamaterial as a temperature sensor with mK resolution for implantable applications

Wireless medical sensors typically utilize electromagnetic coupling or ultrasound for energy transfer and sensor interrogation.Energy transfer and management is a complex aspect that often limits the applicability of implantable sensor systems.In this work,we report a new passive temperature sensing scheme based on an acoustic metamaterial made of silicon embedded in a polydimethylsiloxane matrix.Compared to other approaches,this concept is implemented without additional electrical components in situ or the need for a customized receiving unit.A standard ultrasonic transducer is used for this demonstration to directly excite and collect the reflected signal.The metamaterial resonates at a frequency close to a typical medical value(5 MHz)and exhibits a high-quality factor.Combining the design features of the metamaterial with the high-temperature sensitivity of the polydimethylsiloxane matrix,we achieve a temperature resolution of 30 mK.This value is below the current standard resolution required in infrared thermometry for monitoring postoperative complications(0.1 K).We fabricated,simulated,in vitro tested,and compared three acoustic sensor designs in the 29-43℃(~302-316 K)temperature range.With this concept,we demonstrate how our passive metamaterial sensor can open the way toward new zero-power smart medical implant concepts based on acoustic interrogation.

High-speed identification of suspended carbon nanotubes using Raman spectroscopy and deep learning

The identification of nanomaterials with the properties required for energy-efficient electronic systems is usually a tedious human task.A workflow to rapidly localize and characterize nanomaterials at the various stages of their integration into large-scale fabrication processes is essential for quality control and,ultimately,their industrial adoption.In this work,we develop a high-throughput approach to rapidly identify suspended carbon nanotubes(CNTs)by using high-speed Raman imaging and deep learning analysis.Even for Raman spectra with extremely low signal-to-noise ratios(SNRs)of 0.9,we achieve a classification accuracy that exceeds 90%,while it reaches 98%for an SNR of 2.2.By applying a threshold on the output of the softmax layer of an optimized convolutional neural network(CNN),we further increase the accuracy of the classification.Moreover,we propose an optimized Raman scanning strategy to minimize the acquisition time while simultaneously identifying the position,amount,and metallicity of CNTs on each sample.Our approach can readily be extended to other types of nanomaterials and has the potential to be integrated into a production line to monitor the quality and properties of nanomaterials during fabrication.