DOI: 10.5061/dryad.v6wwpzh7j
title: Data from: Advancing neural interfaces: A framework for the fabrication and characterization of freestanding micro-nanodevices

description: 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: # 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.