Evidence map›Paper›PMID 37083533›Full record

ArticleScience advances2023

Rolling of soft microbots with tunable traction.

Yan Gao, Brennan Sprinkle, Ela Springer, David W M Marr, Ning Wu

Abstract read
In one paragraph

Article in Science advances, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed.

  1. Article
  2. Article
  3. Embodied Cross-Domain Intelligence in Biomedical Microrobots: A Review.Cyborg and bionic systems (Washington, D.C.) · 2026
    Review
  4. Article
  5. Soft Microbot Traction on Structured Surfaces.Langmuir : the ACS journal of surfaces and colloids · 2025
    Article
  6. Review
  7. Swarm Autonomy: From Agent Functionalization to Machine Intelligence.Advanced materials (Deerfield Beach, Fla.) · 2025
    Review
  8. Article
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.

Yan GaoMaterials Science and Engineering Program, Colorado School of Mines, Golden, CO, USA.ORCID 0000-0002-5941-4844
Brennan SprinkleDepartment of Applied Mathematics and Statistics, Colorado School of Mines, Golden, CO, USA.ORCID 0000-0001-6005-1668
Ela SpringerDepartment of Chemical and Biological Engineering, Colorado School of Mines, Golden, CO, USA.ORCID 0009-0006-8887-2285
David W M MarrDepartment of Chemical and Biological Engineering, Colorado School of Mines, Golden, CO, USA.ORCID 0000-0002-6820-761X
Ning WuDepartment of Chemical and Biological Engineering, Colorado School of Mines, Golden, CO, USA.ORCID 0000-0002-2167-3621

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Microbot (μbot)-based targeted drug delivery has attracted increasing attention due to its potential for avoiding side effects associated with systemic delivery. To date, most μbots are rigid. When rolling on surfaces, they exhibit substantial slip due to the liquid lubrication layer. Here, we introduce magnetically controlled soft rollers based on Pickering emulsions that, because of their intrinsic deformability, fundamentally change the nature of the lubrication layer and roll like deflated tires. With a large contact area between μbot and wall, soft μbots exhibit tractions higher than their rigid counterparts, results that we support with both theory and simulation. Upon changing the external field, surface particles can be reconfigured, strongly influencing both the translation speed and traction. These μbots can also be destabilized upon pH changes and used to deliver their contents to a desired location, overcoming the limitations of low translation efficiency and drug loading capacity associated with rigid structures.

Identifiers

PMID37083533
PMCPMC10121164

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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.