Data from: Advancing neural interfaces: A framework for the fabrication and characterization of freestanding micro-nanodevices
Data files
Jan 04, 2026 version files 17.75 MB
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Dataset.zip
17.75 MB
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README.md
4.12 KB
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.
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.