Evidence map›Paper›PMID 41636058›Full record

ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2026

Transparent Transfer-Free Ultrasmall Multilayer Graphene Microelectrodes Enable High Quality Recordings in Brain Slices.

Nerea de Alvarez de Eulate, Christos Pavlou, Gonzalo León González, María Camarena Pérez, Lukas Holzapfel, Zhenyu Gao, Sten Vollebregt, Vasiliki Giagka

Abstract read
In one paragraph

Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

8 authors.

Nerea de Alvarez de EulateDepartment of Microelectronics, Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, Delft, The Netherlands.ORCID https://orcid.org/0009-0009-9031-3388
Christos PavlouDepartment of Microelectronics, Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, Delft, The Netherlands.ORCID https://orcid.org/0000-0001-7345-161X
Gonzalo León GonzálezDepartment of Microelectronics, Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, Delft, The Netherlands.ORCID https://orcid.org/0009-0000-1081-9608
María Camarena PérezDepartment of Microelectronics, Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, Delft, The Netherlands.ORCID https://orcid.org/0000-0001-7075-5968
Lukas HolzapfelDepartment of System Integration and Interconnection Technologies, Fraunhofer Institute for Reliability and Microintegration IZM, Berlin, Germany.ORCID https://orcid.org/0000-0001-8245-1195
Zhenyu GaoDepartment of Neuroscience, Erasmus MC, Rotterdam, The Netherlands.ORCID https://orcid.org/0000-0002-4979-2366
Sten VollebregtDepartment of Microelectronics, Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, Delft, The Netherlands.ORCID https://orcid.org/0000-0001-6012-6180
Vasiliki GiagkaDepartment of Microelectronics, Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, Delft, The Netherlands.ORCID https://orcid.org/0000-0002-0822-2121

Funding

Fraunhofer-Gesellschaft AttractMinisterie van Onderwijs, Cultuur en Wetenschap 024.005.022Rijksdienst voor Ondernemend Nederland XECS241001
6 · The paper itself

Abstract

Resolving the underlying mechanisms of complex brain functions and associated disorders remains a major challenge in neuroscience, largely due to the difficulty in mapping large-scale neural network dynamics with high spatiotemporal resolution. Multimodal neural platforms that integrate optical and electrical modalities offer a promising approach that surpasses resolution limits. Over the last decade, transparent graphene microelectrodes have been proposed as highly suitable multimodal neural interfaces. However, their fabrication commonly relies on the manual transfer process of pre-grown graphene sheets which introduces reliability and scalability issues. In this study, multilayer graphene microelectrode arrays (MEAs) with electrode sizes as small as 10-50 µm in diameter, are fabricated using a transfer-free process on a transparent substrate for in vitro multimodal platforms. For the first time, the capability of transparent graphene electrodes with a diameter of just 10 µm to reliably capture extracellular spiking activity with high signal-to-noise ratios (up to ∼25 dB) is demonstrated. The recorded signal quality is found to be more limited by the electrode-tissue coupling than the MEA technology itself. Overall, this study shows the potential of transfer-free multilayer graphene MEAs to interface with neural tissue, paving the way to advancing neuroscientific research through the next-generation of multimodal neural interfaces.

Indexed as

BrainGraphiteAction PotentialsAnimalsMicroelectrodesNerve NetNeuronsGraphiteelectrophysiologygraphenein vitro neural interfacesmicroelectrode arraysmultimodal applications

Identifiers

PMID41636058
PMCPMC13067786

What OpenQuestion holds

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Registered trials

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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.