ArticleMRS bulletin2026
On-demand biofilm removal by shape-memory triggered local changes in surface topography.
Article in MRS bulletin, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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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.
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.
Who cites it
1 citing paper in PubMed.
- Nature-inspired biomaterial innovations to counter medical device-associated infections.Biomaterials science · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Abstract: Bacterial pathogens can form biofilms on implanted biomedical devices, causing persistent infections that are highly tolerant to antibiotics. Previously, we reported a strategy of biofilm control based on dynamic topography, which effectively removes biofilms via horizontal contraction of the substrate surface of a shape-memory polymer (SMP) upon triggered shape recovery. This method is effective and species nonspecific; however, alterations in the bulk material profile limit its applications. In this study, we tested the hypothesis that biofilm can be removed by changes in local topography without altering the shape of the bulk material. Acrylate-based SMPs were prepared to obtain transition temperature of 40℃ to trigger shape recovery in aqueous environment within 10 min. Micron-scale square patterns that are about 6-µm tall with varying width and spacing were prepared by hot compression against PDMS with complementary patterns, while maintaining the bulk shape of the material unchanged. The results demonstrated effective on-demand biofilm removal (e.g., 48 h biofilms of Impact statement: Bacterial biofilms are the root cause of persistent infections associated with implanted biomaterials. Conventional treatments with antibiotics are often ineffective and promote the development of bacterial drug resistance. Thus, we are motivated to engineer new biomaterials that are self-defensive against bacterial colonization. Previously, we reported that shape-memory polymers (SMPs) can be programed to change the bulk shape (via horizontal stretch) on-demand and effectively remove bacterial biofilms. In this study, we further developed this strategy to control shape change of surface topography alone. The SMP surfaces programmed with microscale square-shaped features were fabricated, which were able to revert to flat surfaces upon triggering with moderate temperature change and disrupt bacterial biofilms (~70%). The shape recovery was limited to surface topography with the bulk shape unchanged. In addition to biofilm removal, shape recovery also enhanced the antibiotic susceptibility of remaining biofilm cells. Further research could explore various forms of surface topographies and different stimuli to enable more effective and reversible changes. In summary, this study reports a new strategy for biofilm control. With further development, it could help reduce medical device-associated infections and biofouling in industrial settings. Graphical abstractOn-demand biofilm removal through microscale shape recovery: On-demand biofilm removal through microscale shape recovery. Supplementary information: The online version contains supplementary material available at 10.1557/s43577-025-01024-4.
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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.