Evidence map›Paper›PMID 42086554›Full record

ArticleNature communications2026

Optimized mechano-fluidic metamaterials inspired by deep-sea sponges.

Timon Meier, Sergey Litvinov, Runxuan Li, Brian W Blankenship, Andrew Kokubun, David Hahn, Stefanos Mavrikos, Zacharias Vangelatos, M Erden Yildizdag, Simo A Mäkiharju and 3 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Nature communications, 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

13 authors.

Timon Meier *Laser Thermal Laboratory, Department of Mechanical Engineering, University of California, Berkeley, CA, USA.ORCID http://orcid.org/0009-0009-6572-680X
Sergey Litvinov *School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA.
Runxuan LiLaser Thermal Laboratory, Department of Mechanical Engineering, University of California, Berkeley, CA, USA.ORCID http://orcid.org/0000-0001-6892-3766
Brian W BlankenshipLaser Thermal Laboratory, Department of Mechanical Engineering, University of California, Berkeley, CA, USA.ORCID http://orcid.org/0000-0003-4212-6835
Andrew KokubunFLOW Lab, Department of Mechanical Engineering, University of California, Berkeley, CA, USA.
David HahnDepartment of Materials Science and Engineering, University of California, Berkeley, CA, USA.
Stefanos MavrikosLaser Thermal Laboratory, Department of Mechanical Engineering, University of California, Berkeley, CA, USA.
Zacharias VangelatosLaser Thermal Laboratory, Department of Mechanical Engineering, University of California, Berkeley, CA, USA.ORCID http://orcid.org/0000-0001-9513-3671
M Erden YildizdagFaculty of Naval Architecture and Ocean Engineering, Istanbul Technical University, Istanbul, Türkiye.
Simo A MäkiharjuFLOW Lab, Department of Mechanical Engineering, University of California, Berkeley, CA, USA.ORCID http://orcid.org/0000-0002-3818-8649
Xiaoyu ZhengDepartment of Materials Science and Engineering, University of California, Berkeley, CA, USA.ORCID http://orcid.org/0000-0001-8685-5728
Petros KoumoutsakosSchool of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA. petros@seas.harvard.edu.ORCID http://orcid.org/0000-0001-8337-2122
Costas P GrigoropoulosLaser Thermal Laboratory, Department of Mechanical Engineering, University of California, Berkeley, CA, USA. cgrigoro@berkeley.edu.ORCID http://orcid.org/0000-0002-8505-4037

Funding

NSF | Directorate for Mathematical & Physical Sciences | Division of Mathematical Sciences (DMS) 2134534NSF | ENG/OAD | Division of Civil, Mechanical and Manufacturing Innovation (CMMI) 2124826Stavros Niarchos Foundation (SNF) 9578
6 · The paper itself

Abstract

Multifunctional materials that balance mechanical resilience and fluid dynamic efficiency are critical in engineering applications, yet their synergistic optimization remains challenging due to inherent trade-offs, computational expense, and high-dimensional design spaces. Inspired by the skeleton of the deep-sea sponge Euplectella aspergillum, this work presents an automated framework integrating Finite Element Analysis for mechanics, Computational Fluid Dynamics for flow behavior, and multi-objective Bayesian optimization. Leveraging high-performance computing, the framework efficiently explores complex design spaces to identify Pareto-optimal solutions. Optimized lattices achieve an average 140% increase in critical buckling load across a range of volume fractions relative to baseline designs, while simultaneously reducing drag, lift, and vortex shedding at porosities as low as 5%. We fabricate selected designs via stereolithography and validate them through compression experiments and particle image velocimetry, showing agreement with simulations. By jointly optimizing mechanics and fluidics, this work establishes a scalable methodology for designing lightweight, high-performance architected materials.

Identifiers

PMID42086554
PMCPMC13350808

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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.