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 ...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.展开更多
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 sta...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.展开更多
All biological processes use or produce heat.Traditional microcalorimeters have been utilized to study the metabolic heat output of living organisms and heat production of exothermic chemical processes.Current advance...All biological processes use or produce heat.Traditional microcalorimeters have been utilized to study the metabolic heat output of living organisms and heat production of exothermic chemical processes.Current advances in microfabrication have made possible the miniaturization of commercial microcalorimeters,resulting in a few studies on the metabolic activity of cells at the microscale in microfluidic chips.Here we present a new,versatile,and robust microcalorimetric differential design based on the integration of heat flux sensors on top of microfluidic channels.We show the design,modeling,calibration,and experimental verification of this system by utilizing Escherichia coli growth and the exothermic base catalyzed hydrolysis of methyl paraben as use cases.The system consists of a Polydimethylsiloxane based flow-through microfluidic chip with two 46µl chambers and two integrated heat flux sensors.The differential compensation of thermal power measurements allows for the measurement of bacterial growth with a limit of detection of 1707 W/m^(3),corresponding to 0.021OD(2·10^(7) bacteria).We also extracted the thermal power of a single Escherichia coli of between 1.3 and 4.5 pW,comparable to values measured by industrial microcalorimeters.Our system opens the possibility for expanding already existing microfluidic systems,such as drug testing lab-on-chip platforms,with measurements of metabolic changes of cell populations in form of heat output,without modifying the analyte and minimal interference with the microfluidic channel itself.展开更多
基金supported by the IMG Stiftung and the ETH Zurich Foundation(project number:2021-FS-212).
文摘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.
基金We acknowledge financial support from Strategic Focus Area(SFA)Advanced Manufacturing(Project NanoAssembly)M.L.P.and J.Z.acknowledge funding by the EMPAPOSTDOCS-II program,which has received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska–Curie Grant Agreement no.754364M.L.P.also acknowledges funding from the Swiss National Science Foundation under Spark grant no.196795。
文摘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.
基金This work is a part of and was partially funded by the ETHeart initiative of the Swiss Federal Institute of Technology(ETH Zurich).M.A.was supported as a part of NCCR Microbiomes,a National Centre of Competence in Research,funded by the Swiss National Science Foundation(grant number 180575).We would like to especially acknowledge the support of Prof.Dr.Volkmar Falk and Nikola Cesarovic for the project.We would also like to acknowledge Prof.Emma Wetter Slack of the Laboratory for Food Immunology in the Department of Health Sciences and Technologies at ETHZ for support and the use of equipment.We would also like to acknowledge Lavinia Recchioni for her help in the work on the lumped element model.Furthermore,we would like to acknowledge the help of Alyson Hockenberry for all of her support regarding the microfluidics.Lastly,we would like to acknowledge all of the support by the members of the Micro-and Nanosystems at ETH Zürich.
文摘All biological processes use or produce heat.Traditional microcalorimeters have been utilized to study the metabolic heat output of living organisms and heat production of exothermic chemical processes.Current advances in microfabrication have made possible the miniaturization of commercial microcalorimeters,resulting in a few studies on the metabolic activity of cells at the microscale in microfluidic chips.Here we present a new,versatile,and robust microcalorimetric differential design based on the integration of heat flux sensors on top of microfluidic channels.We show the design,modeling,calibration,and experimental verification of this system by utilizing Escherichia coli growth and the exothermic base catalyzed hydrolysis of methyl paraben as use cases.The system consists of a Polydimethylsiloxane based flow-through microfluidic chip with two 46µl chambers and two integrated heat flux sensors.The differential compensation of thermal power measurements allows for the measurement of bacterial growth with a limit of detection of 1707 W/m^(3),corresponding to 0.021OD(2·10^(7) bacteria).We also extracted the thermal power of a single Escherichia coli of between 1.3 and 4.5 pW,comparable to values measured by industrial microcalorimeters.Our system opens the possibility for expanding already existing microfluidic systems,such as drug testing lab-on-chip platforms,with measurements of metabolic changes of cell populations in form of heat output,without modifying the analyte and minimal interference with the microfluidic channel itself.
