Evidence map›Paper›PMID 42077884›Full record

ArticleACS omega2026

Fabrication of Silicon Monolithic Tubes with High-Aspect-Ratio Cavities Using Langmuir-Blodgett Colloidal Lithography and Bayesian-Optimized Continuous Plasma Etching.

Artem Osipov, Alina Fumina, Ilya Belyanov, Anastasiya Speshilova, Ekaterina Endiiarova, Alexandr Gerdt

Abstract read
In one paragraph

Article in ACS omega, 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

6 authors.

Artem OsipovPeter the Great St. Petersburg Polytechnic University, St. Petersburg 195251, Russian Federation.ORCID https://orcid.org/0000-0002-0622-6479
Alina FuminaPeter the Great St. Petersburg Polytechnic University, St. Petersburg 195251, Russian Federation.ORCID https://orcid.org/0000-0002-2401-8710
Ilya BelyanovPeter the Great St. Petersburg Polytechnic University, St. Petersburg 195251, Russian Federation.
Anastasiya SpeshilovaPeter the Great St. Petersburg Polytechnic University, St. Petersburg 195251, Russian Federation.
Ekaterina EndiiarovaPeter the Great St. Petersburg Polytechnic University, St. Petersburg 195251, Russian Federation.
Alexandr GerdtPeter the Great St. Petersburg Polytechnic University, St. Petersburg 195251, Russian Federation.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The fabrication of semiconductor devices using submicron- and nanometer-scale silicon structures is based on lithography (patterning on a substrate) and etching (transferring a pattern onto the substrate) technologies. These processes typically require complex and expensive equipment, as well as extensive experimental optimization of etching parameters, especially for structures with an aspect ratio greater than 10. This work demonstrates a productive and relatively simple approach for fabricating an ordered array of vertically oriented monolithic silicon tubes with high-aspect-ratio internal cavities. The creation of these structures is based on Langmuir-Blodgett colloidal lithography, vacuum magnetron sputtering, and a continuous plasma etching process performed at "room" temperature of the substrate holder. To develop the fabrication process for high-aspect-ratio (>10) structures, we employed Bayesian optimization (a machine learning method), which proved highly efficient in reducing the number of experiments compared to a full factorial analysis. The resulting silicon tubes exhibited an average total reflectance of 3.7% over the 200-1600 nm wavelength range.

Identifiers

PMID42077884
PMCPMC13129831

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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.