{"data":{"id":"10.5061/dryad.v6wwpzh7j","type":"dois","attributes":{"doi":"10.5061/dryad.v6wwpzh7j","prefix":"10.5061","suffix":"dryad.v6wwpzh7j","identifiers":[],"alternateIdentifiers":[],"creators":[{"name":"Airaghi Leccardi, Marta","nameType":"Personal","givenName":"Marta","familyName":"Airaghi Leccardi","affiliation":["Massachusetts Institute of Technology"],"nameIdentifiers":[]},{"name":"Desbiolles, Benoît","nameType":"Personal","givenName":"Benoît","familyName":"Desbiolles","affiliation":["Massachusetts Institute of Technology"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0001-6848-3289","nameIdentifierScheme":"ORCID"}]},{"name":"Yadav, Shubham","nameType":"Personal","givenName":"Shubham","familyName":"Yadav","affiliation":["Massachusetts Institute of Technology"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0003-4826-4550","nameIdentifierScheme":"ORCID"}]},{"name":"Yu, Yang","nameType":"Personal","givenName":"Yang","familyName":"Yu","affiliation":["Raith (Germany)"],"nameIdentifiers":[]},{"name":"Sarkar, Deblina","nameType":"Personal","givenName":"Deblina","familyName":"Sarkar","affiliation":["Massachusetts Institute of Technology"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-9662-1666","nameIdentifierScheme":"ORCID"}]}],"titles":[{"title":"Data from: Advancing neural interfaces: A framework for the fabrication and characterization of freestanding micro-nanodevices"}],"publisher":"Dryad","container":{},"publicationYear":2026,"subjects":[{"subject":"FOS: Nano-technology","schemeUri":"https://web-archive.oecd.org/2012-06-15/138575-38235147.pdf","subjectScheme":"fos"},{"subject":"Freestanding micro-nanodevices"},{"subject":"neural interfaces"},{"subject":"micro-nanofabrication"},{"subject":"neuroprosthetics"}],"contributors":[{"name":"Massachusetts Institute of Technology","affiliation":[],"contributorType":"Sponsor","nameIdentifiers":[]}],"dates":[{"date":"2025-05-25T22:00:43Z","dateType":"Created"},{"date":"2025-12-13T15:25:04Z","dateType":"Submitted"},{"date":"2026-01-04T00:00:00Z","dateType":"Issued"},{"date":"2026-01-04T00:00:00Z","dateType":"Available"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsCitedBy","relatedIdentifier":"10.1038/s41378-025-01117-9","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["17752310 bytes"],"formats":[],"version":"2","rightsList":[{"rights":"Creative Commons Zero v1.0 Universal","rightsUri":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc0-1.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"Freestanding micro-nanodevices stand out as excellent candidates for the\n next generation of neural interfaces. Their wireless nature, coupled with\n their subcellular dimensions, promises to enable minimally invasive\n neuromodulation with high spatial resolution within three-dimensional\n tissues. Nevertheless, their practical implementation is hindered by\n technical challenges. Specifically, fabricating and harvesting\n freestanding devices with subcellular sizes proves exceedingly difficult,\n and characterizing their functionality in a representative freestanding\n configuration presents an even greater challenge. In this work, we present\n a comprehensive framework for fabricating, collecting, and characterizing\n freestanding micro-nanodevices to advance progress in neural interfaces.\n We developed three distinct micro-nanofabrication methods tailored for\n manufacturing freestanding micro-nanodevices with varying characteristics.\n These methods include a very large-scale integration process for\n manufacturing and manipulating freestanding microdevices (2 to 200 µm)\n with high throughput, a cell-friendly approach utilizing only\n biocompatible materials and solvents for rapid microdevice production, and\n a protocol for fabricating and handling freestanding devices with even\n smaller size scale (200 nm to 3 µm). We subsequently devised an effective\n approach to rapidly characterize the electrical modulation capabilities of\n freestanding micro-nanodevices in a cell-like environment, employing\n artificial bilayer lipid membranes. We showcased this method by studying\n the variation of bilayer lipid membrane transmembrane potential in\n response to a light stimulus when sprinkled with organic semiconductor\n devices. Ultimately, we established an analytical model of the\n characterization system to translate experimental findings made with\n bilayer lipid membrane into single cells. By overcoming the technical\n limitations hindering the fabrication, manipulation, and characterization\n of freestanding micro-nanodevices, we hope that our research efforts will\n contribute to accelerating progress in the development of next-generation\n neural interfaces and unlock the full potential of neuromodulation\n technologies in fundamental and clinical research.","descriptionType":"Abstract"},{"description":"# Data from: Advancing neural interfaces: A framework for the fabrication\n and characterization of freestanding micro-nanodevices This dataset\n contains all files required to reproduce the results in the publication\n *\"Advancing neural interfaces: A framework for the fabrication and\n characterization of freestanding micro-nanodevices\".* It is organized\n into three folders: * BLM: Contains experimental data, code, and output\n files related to bilayer lipid membrane (BLM) experiments. *\n Cell_device_interface_model: Contains data, code, and output files related\n to cell-device interface modeling. * Patch_clamp: Contains raw\n experimental data, code, and output files related to patch clamp\n experiments. # BLM Includes all raw and processed BLM data, along with the\n code used for analysis and the resulting output figures. ## Code *\n Peak_analysis.m: Processes raw electrical recordings from photomodulation\n experiments on BLMs. This script extracts the peak amplitudes of\n photovoltages generated upon light stimulation. Used to plot Figure 4-C. *\n Plot_comparison.m: Generates comparative plots of photovoltage amplitudes\n obtained from Peak_analysis.m across different experimental conditions,\n such as illumination wavelength, pulse duration, and membrane coverage.\n Used to plot Figure 4-D to F. ## Data * 561nm_C369: Contains raw\n electrical recordings from photomodulation experiments on BLMs illuminated\n with 561 nm light. The device coverage is 3.69 %, and each file\n corresponds to a different illumination intensity. * 750nm_C073: Contains\n raw electrical recordings from photomodulation experiments on BLMs\n illuminated with 750 nm light. The device coverage is 0.73 %, with each\n file representing a different illumination intensity. * 750nm_C369:\n Contains raw electrical recordings from photomodulation experiments on\n BLMs illuminated with 750 nm light. The device coverage is 3.69 %, and\n each file corresponds to a distinct illumination intensity. ## Output *\n 561/750nm_C369/073_peak.mat: MAT-files containing the average peak\n amplitude and standard deviation for each experimental condition. Rows\n correspond to pulse durations (20 ms, 50 ms, 100 ms, 200 ms), and columns\n represent different illumination intensities. These files are the output\n of Peak_analysis.m. *\n Role_of_wavelength/device_coverage/pulse_duration.pdf: Figures generated\n by Plot_comparison.m summarizing the influence of wavelength, device\n coverage, and pulse duration on the measured photovoltages. *\n 750nm_C369_trace.pdf: Representative raw and processed traces from\n Peak_analysis.m for the 750  nm illumination wavelength and 3.69 % device\n coverage condition. # Cell_device_interface_model Includes all data, along\n with the code used to model the cell-device interface and the resulting\n output figures. ## Code * Model.m: Script implementing the analytical\n model used to predict how photomodulation of the BLM transmembrane\n potential translates into modulation of a single cell’s transmembrane\n potential. This model corresponds to the section *“Theoretical modeling to\n translate experimental results from BLMs to single cells”* in the main\n manuscript. ## Data * NIR_C369.txt: Raw electrical data from BLM\n experiments used as the starting point for the analytical model. ## Output\n * Device_cell_interface_model.pdf: Output figure from Model.m illustrating\n the influence of device yield on the predicted modulation of neuronal\n transmembrane potential. # Patch_clamp Includes all raw and processed\n patch-clamp data (as shown in the Supplementary Information), along with\n the analysis code and resulting output figures. ## Code * Current_clamp.m:\n Script for analyzing and plotting current-clamp recordings from three\n different neurons, as presented in Section 7 of the Supplementary\n Information. ## Data * Data_IC.mat: MAT-file containing the raw\n current-clamp recordings used for analysis in Current_clamp.m. ## Output *\n Current_clamp.pdf: Figure generated by Current_clamp.m, showing the\n analyzed current-clamp traces from the three neurons. # Code/Software The\n analyses were performed using MATLAB 2023.","descriptionType":"TechnicalInfo"}],"geoLocations":[],"fundingReferences":[{"schemeUri":"https://ror.org","funderName":"Swiss National Science Foundation","awardNumber":"P2ELP2_191707","funderIdentifier":"https://ror.org/00yjd3n13","funderIdentifierType":"ROR"},{"schemeUri":"https://ror.org","funderName":"Swiss National Science Foundation","awardNumber":"P2ELP2_191218","funderIdentifier":"https://ror.org/00yjd3n13","funderIdentifierType":"ROR"},{"schemeUri":"https://ror.org","awardTitle":"MRI: Acquisition of a Next Generation Nanofabrication Dual-beam Platform","funderName":"U.S. National Science Foundation","awardNumber":"2117609","funderIdentifier":"https://ror.org/021nxhr62","funderIdentifierType":"ROR"}],"xml":"<?xml version="1.0" encoding="UTF-8"?>
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  <identifier identifierType="DOI">10.5061/DRYAD.V6WWPZH7J</identifier>
  <creators>
    <creator>
      <creatorName nameType="Personal">Airaghi Leccardi, Marta</creatorName>
      <affiliation affiliationIdentifier="https://ror.org/042nb2s44" affiliationIdentifierScheme="ROR" schemeURI="https://ror.org">Massachusetts Institute of Technology</affiliation>
    </creator>
    <creator>
      <creatorName nameType="Personal">Desbiolles, Benoît</creatorName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0001-6848-3289</nameIdentifier>
      <affiliation affiliationIdentifier="https://ror.org/042nb2s44" affiliationIdentifierScheme="ROR" schemeURI="https://ror.org">Massachusetts Institute of Technology</affiliation>
    </creator>
    <creator>
      <creatorName nameType="Personal">Yadav, Shubham</creatorName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0003-4826-4550</nameIdentifier>
      <affiliation affiliationIdentifier="https://ror.org/042nb2s44" affiliationIdentifierScheme="ROR" schemeURI="https://ror.org">Massachusetts Institute of Technology</affiliation>
    </creator>
    <creator>
      <creatorName nameType="Personal">Yu, Yang</creatorName>
      <affiliation affiliationIdentifier="https://ror.org/03yag1532" affiliationIdentifierScheme="ROR" schemeURI="https://ror.org">Raith (Germany)</affiliation>
    </creator>
    <creator>
      <creatorName nameType="Personal">Sarkar, Deblina</creatorName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0002-9662-1666</nameIdentifier>
      <affiliation affiliationIdentifier="https://ror.org/042nb2s44" affiliationIdentifierScheme="ROR" schemeURI="https://ror.org">Massachusetts Institute of Technology</affiliation>
    </creator>
  </creators>
  <titles>
    <title>
      Data from: Advancing neural interfaces: A framework for the fabrication
      and characterization of freestanding micro-nanodevices
    </title>
  </titles>
  <publisher publisherIdentifier="https://ror.org/00x6h5n95" publisherIdentifierScheme="ROR" schemeURI="https://ror.org/">Dryad</publisher>
  <resourceType resourceTypeGeneral="Dataset">dataset</resourceType>
  <publicationYear>2026</publicationYear>
  <subjects>
    <subject subjectScheme="fos" schemeURI="https://web-archive.oecd.org/2012-06-15/138575-38235147.pdf">FOS: Nano-technology</subject>
    <subject>Freestanding micro-nanodevices</subject>
    <subject>neural interfaces</subject>
    <subject>micro-nanofabrication</subject>
    <subject>neuroprosthetics</subject>
  </subjects>
  <fundingReferences>
    <fundingReference>
      <funderName>Swiss National Science Foundation</funderName>
      <funderIdentifier funderIdentifierType="ROR">https://ror.org/00yjd3n13</funderIdentifier>
      <awardNumber>P2ELP2_191707</awardNumber>
    </fundingReference>
    <fundingReference>
      <funderName>Swiss National Science Foundation</funderName>
      <funderIdentifier funderIdentifierType="ROR">https://ror.org/00yjd3n13</funderIdentifier>
      <awardNumber>P2ELP2_191218</awardNumber>
    </fundingReference>
    <fundingReference>
      <funderName>U.S. National Science Foundation</funderName>
      <funderIdentifier funderIdentifierType="ROR">https://ror.org/021nxhr62</funderIdentifier>
      <awardNumber>2117609</awardNumber>
      <awardTitle>MRI: Acquisition of a Next Generation Nanofabrication Dual-beam Platform</awardTitle>
    </fundingReference>
  </fundingReferences>
  <contributors>
    <contributor contributorType="Sponsor">
      <contributorName>Massachusetts Institute of Technology</contributorName>
    </contributor>
  </contributors>
  <dates>
    <date dateType="Created">2025-05-25T22:00:43Z</date>
    <date dateType="Submitted">2025-12-13T15:25:04Z</date>
    <date dateType="Issued">2026-01-04T00:00:00Z</date>
    <date dateType="Available">2026-01-04T00:00:00Z</date>
  </dates>
  <language>en</language>
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  <descriptions>
    <description descriptionType="Abstract">
      Freestanding micro-nanodevices stand out as excellent candidates for the
      next generation of neural interfaces. Their wireless nature, coupled with
      their subcellular dimensions, promises to enable minimally invasive
      neuromodulation with high spatial resolution within three-dimensional
      tissues. Nevertheless, their practical implementation is hindered by
      technical challenges. Specifically, fabricating and harvesting
      freestanding devices with subcellular sizes proves exceedingly difficult,
      and characterizing their functionality in a representative freestanding
      configuration presents an even greater challenge. In this work, we present
      a comprehensive framework for fabricating, collecting, and characterizing
      freestanding micro-nanodevices to advance progress in neural interfaces.
      We developed three distinct micro-nanofabrication methods tailored for
      manufacturing freestanding micro-nanodevices with varying characteristics.
      These methods include a very large-scale integration process for
      manufacturing and manipulating freestanding microdevices (2 to 200 µm)
      with high throughput, a cell-friendly approach utilizing only
      biocompatible materials and solvents for rapid microdevice production, and
      a protocol for fabricating and handling freestanding devices with even
      smaller size scale (200 nm to 3 µm). We subsequently devised an effective
      approach to rapidly characterize the electrical modulation capabilities of
      freestanding micro-nanodevices in a cell-like environment, employing
      artificial bilayer lipid membranes. We showcased this method by studying
      the variation of bilayer lipid membrane transmembrane potential in
      response to a light stimulus when sprinkled with organic semiconductor
      devices. Ultimately, we established an analytical model of the
      characterization system to translate experimental findings made with
      bilayer lipid membrane into single cells. By overcoming the technical
      limitations hindering the fabrication, manipulation, and characterization
      of freestanding micro-nanodevices, we hope that our research efforts will
      contribute to accelerating progress in the development of next-generation
      neural interfaces and unlock the full potential of neuromodulation
      technologies in fundamental and clinical research.
    </description>
    <description descriptionType="TechnicalInfo">
      # Data from: Advancing neural interfaces: A framework for the fabrication
      and characterization of freestanding micro-nanodevices This dataset
      contains all files required to reproduce the results in the publication
      *"Advancing neural interfaces: A framework for the fabrication and
      characterization of freestanding micro-nanodevices".* It is organized
      into three folders: * BLM: Contains experimental data, code, and output
      files related to bilayer lipid membrane (BLM) experiments. *
      Cell_device_interface_model: Contains data, code, and output files related
      to cell-device interface modeling. * Patch_clamp: Contains raw
      experimental data, code, and output files related to patch clamp
      experiments. # BLM Includes all raw and processed BLM data, along with the
      code used for analysis and the resulting output figures. ## Code *
      Peak_analysis.m: Processes raw electrical recordings from photomodulation
      experiments on BLMs. This script extracts the peak amplitudes of
      photovoltages generated upon light stimulation. Used to plot Figure 4-C. *
      Plot_comparison.m: Generates comparative plots of photovoltage amplitudes
      obtained from Peak_analysis.m across different experimental conditions,
      such as illumination wavelength, pulse duration, and membrane coverage.
      Used to plot Figure 4-D to F. ## Data * 561nm_C369: Contains raw
      electrical recordings from photomodulation experiments on BLMs illuminated
      with 561 nm light. The device coverage is 3.69 %, and each file
      corresponds to a different illumination intensity. * 750nm_C073: Contains
      raw electrical recordings from photomodulation experiments on BLMs
      illuminated with 750 nm light. The device coverage is 0.73 %, with each
      file representing a different illumination intensity. * 750nm_C369:
      Contains raw electrical recordings from photomodulation experiments on
      BLMs illuminated with 750 nm light. The device coverage is 3.69 %, and
      each file corresponds to a distinct illumination intensity. ## Output *
      561/750nm_C369/073_peak.mat: MAT-files containing the average peak
      amplitude and standard deviation for each experimental condition. Rows
      correspond to pulse durations (20 ms, 50 ms, 100 ms, 200 ms), and columns
      represent different illumination intensities. These files are the output
      of Peak_analysis.m. *
      Role_of_wavelength/device_coverage/pulse_duration.pdf: Figures generated
      by Plot_comparison.m summarizing the influence of wavelength, device
      coverage, and pulse duration on the measured photovoltages. *
      750nm_C369_trace.pdf: Representative raw and processed traces from
      Peak_analysis.m for the 750  nm illumination wavelength and 3.69 % device
      coverage condition. # Cell_device_interface_model Includes all data, along
      with the code used to model the cell-device interface and the resulting
      output figures. ## Code * Model.m: Script implementing the analytical
      model used to predict how photomodulation of the BLM transmembrane
      potential translates into modulation of a single cell’s transmembrane
      potential. This model corresponds to the section *“Theoretical modeling to
      translate experimental results from BLMs to single cells”* in the main
      manuscript. ## Data * NIR_C369.txt: Raw electrical data from BLM
      experiments used as the starting point for the analytical model. ## Output
      * Device_cell_interface_model.pdf: Output figure from Model.m illustrating
      the influence of device yield on the predicted modulation of neuronal
      transmembrane potential. # Patch_clamp Includes all raw and processed
      patch-clamp data (as shown in the Supplementary Information), along with
      the analysis code and resulting output figures. ## Code * Current_clamp.m:
      Script for analyzing and plotting current-clamp recordings from three
      different neurons, as presented in Section 7 of the Supplementary
      Information. ## Data * Data_IC.mat: MAT-file containing the raw
      current-clamp recordings used for analysis in Current_clamp.m. ## Output *
      Current_clamp.pdf: Figure generated by Current_clamp.m, showing the
      analyzed current-clamp traces from the three neurons. # Code/Software The
      analyses were performed using MATLAB 2023.
    </description>
  </descriptions>
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