Evidence map›Paper›PMID 42534917›Full record

ArticleFrontiers in neuroscience2026

Mechanical characterization of vertically aligned carbon nanotube forest microelectrodes for neural interfacing.

Spencer M Roberts, Joseph S Carter, Brian D Jensen, Richard R Vanfleet, Robert C Davis

Abstract read
In one paragraph

Article in Frontiers in neuroscience, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Spencer M RobertsDepartment of Physics and Astronomy, Brigham Young University, Provo, UT, United States.
Joseph S CarterDepartment of Mechanical Engineering, Brigham Young University, Provo, UT, United States.
Brian D JensenDepartment of Mechanical Engineering, Brigham Young University, Provo, UT, United States.
Richard R VanfleetDepartment of Physics and Astronomy, Brigham Young University, Provo, UT, United States.
Robert C DavisDepartment of Physics and Astronomy, Brigham Young University, Provo, UT, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: This study aims to explore the mechanical properties of porous microelectrodes formed from vertically aligned carbon nanotube (CNT) forests. Specifically, we investigate the range of effective CNT-based microelectrode (ME) moduli that can be fabricated and identify moduli within that range that significantly reduce strain on brain tissue during micromotion. Materials and methods: To address these questions, we developed a micromechanical measurement method, known as the dual deflection (DD) test, which is compatible with microelectrode array (MEA) form factors and can measure a wide range of moduli with a 30% uncertainty. Using the DD test with small deflections, we measured the effective Young's modulus of freestanding CNT microelectrodes (MEs) fabricated with different carbon infiltration times (0, 15, and 30 s) at 900°C. We also developed a static 10 μm deflection finite element analysis (FEA) model to compare the brain tissue strain induced by probes with the maximum (1.7 GPa), median (72 MPa), and minimum (3.9 MPa) measured CNT moduli, along with the modulus of silicon (165 GPa) for comparison. Results: The DD test results showed mean effective moduli of 19.6 ± 14.5 MPa, 67.7 ± 22.7 MPa, and 168 ± 62.3 MPa for arrays fabricated with 0, 15, and 30 s infiltrations, respectively. The FEA model revealed that probes with the maximum CNT modulus induced similar strain to the silicon probes at the tip, while probes with the minimum and median CNT moduli showed minimal strain at the tip. Discussion: These findings suggest that CNT microelectrodes with moduli in the tens of MPa range, achievable through 15 s of carbon infiltration, can significantly reduce brain tissue strain. Additionally, we consistently observed that microelectrodes with 15 s of infiltration were apparently undamaged after deflection, making them mechanically promising candidates for neural probe arrays.

Indexed as

carbon nanotube forestfinite element analysismechanical characterizationmicroelectrode arraymodulusneural implant

Identifiers

PMID42534917
PMCPMC13422402

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.