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On the stability of morphology and performance of neural interfacing electrodes fabricated via CO2-snow-assisted hierarchical surface restructuring.

Overview

Authors: Alexander Blagojevic1, Wesley Seche2, Pouya Tavousi1,3, Sina Shahbazmohamadi1,3, Shahram Amini1,2
  1. Department of Biomedical Engineering, University of Connecticut, Storrs, Connecticut, United States of America
  2. Pulse Technologies Inc. (An Integer Holdings Company), Research & Development, Quakertown, Pennsylvania, United States of America
  3. Tescan Orsay Holding, Libušina tř., Brno-Kohoutovice, Czechia
Institutions: University of Connecticut (United States); Tescan (Czechia) (Czechia)
Journal: PloS one, volume 21, issue 7, article e0349598
Dates: received 9 July 2025; accepted 2 May 2026; published online 9 July 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1371/journal.pone.0349598 · PMID 42424375 · PMCID PMC13349186 · OpenAlex W7167789778
Open access: gold, a free copy (OpenAlex)
Status: data only
Categories: methods / tools (subfield)
MeSH: Carbon Dioxide*, Electrodes, Implanted*, Electrodes, Iridium, Microelectrodes, Neurons, Platinum, Surface Properties (* major topic)
Journal subjects: Physical Sciences, Chemistry, Electrochemistry, Engineering and Technology, Equipment, Optical Equipment, Lasers, Electrode Potentials, Industrial Engineering, Process Engineering, Industrial Processes, Manufacturing Processes, Materials Science, Material Properties, Capacitance, Nanotechnology, Nanoparticles, Materials, Mixtures, Particulates, Medicine and Health Sciences, Surgical and Invasive Medical Procedures, Functional Electrical Stimulation
Topic: Neuroscience and Neural Engineering (Cellular and Molecular Neuroscience, Neuroscience), according to OpenAlex
Citations: not cited yet (Europe PMC); 31 references in the paper

Abstract

Long-term implantable neural interfacing devices play a critical role in treating various neurological disorders, with their functionality largely dependent on the performance of electrodes and microelectrode arrays. Femtosecond laser Hierarchical Surface Restructuring (HSR™) is an advanced surface treatment technology that significantly enhances a platinum-10% iridium (Pt-10Ir) electrode’s electrochemical performance, improving energy efficiency, specificity, and signal-to-noise ratio. Additionally, HSR™ facilitates electrode miniaturization, allowing them to be manufactured smaller, for a less invasive profile. Electrode surfaces produced via HSR™ technology contain multiscale structures, including nanoscale features that, while contributing to superior performance, are sometimes weakly bonded and may detach due to mechanical agitation during testing or implantation. This detachment could lead to a high initial performance, that may gradually decline during prolonged use. Preemptively removing these nanostructures stabilizes the electrode surface, enhancing the stability of its morphology and potentially electrochemical performance. This study introduces a novel, in-operando, CO₂-snow-assisted HSR™ process and benchmarks it against other prevalent surface cleaning methods post-fabrication, such as ultrasonic cleaning, on improving electrode stability and performance. Both qualitative and quantitative analyses indicate that all cleaning methods enhance electrode stability. However, ultrasonic cleaning was found to be more destructive compared to CO₂-snow-assisted HSR™ processing, resulting in reduced electrochemical performance. In contrast, in-operando CO₂-snow-assisted processing provided similar or superior improvements in surface stability, while preserving higher electrochemical performance in vitro and enabling a faster processing time. This study is the gateway to further assess the stability in vivo, which is the intended next step of the research.

Reproduced under the paper's license (CC BY), from the paper cited above.

Code

The paper links to its data, not to its authors' code: see the Data section.

Tracing map

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Data

Datasets cited

Data Availability

The relevant dataset is publicly accessible at the following link: https://datadryad.org/dataset/doi:10.5061/dryad.ht76hdrwv.

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 5 authors, 8 MeSH terms, 23 references.

Cite

This paper

Blagojevic, A., Seche, W., Tavousi, P., Shahbazmohamadi, S., & Amini, S. (2026). On the stability of morphology and performance of neural interfacing electrodes fabricated via CO2-snow-assisted hierarchical surface restructuring. PloS one, 21(7), e0349598. https://doi.org/10.1371/journal.pone.0349598

BibTeX

@article{blagojevic2026stability,
author = {Blagojevic, Alexander and Seche, Wesley and Tavousi, Pouya and Shahbazmohamadi, Sina and Amini, Shahram},
title = {{On the stability of morphology and performance of neural interfacing electrodes fabricated via CO2-snow-assisted hierarchical surface restructuring}},
journal = {PloS one},
year = {2026},
month = jul,
volume = {21},
number = {7},
pages = {e0349598},
publisher = {PLOS},
issn = {1932-6203},
doi = {10.1371/journal.pone.0349598},
url = {https://doi.org/10.1371/journal.pone.0349598},
pmid = {42424375},
pmcid = {PMC13349186}
}

RIS

TY - JOUR
AU - Blagojevic, Alexander
AU - Seche, Wesley
AU - Tavousi, Pouya
AU - Shahbazmohamadi, Sina
AU - Amini, Shahram
TI - On the stability of morphology and performance of neural interfacing electrodes fabricated via CO2-snow-assisted hierarchical surface restructuring
T2 - PloS one
J2 - PLoS One
PY - 2026
DA - 2026/07/09
VL - 21
IS - 7
SP - e0349598
SN - 1932-6203
PB - PLOS
DO - 10.1371/journal.pone.0349598
UR - https://doi.org/10.1371/journal.pone.0349598
LA - en
ER -

CSL-JSON

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