journal article·2019-05

Rotibot: Use of Rotifers as Self‐Propelling Biohybrid Microcleaners

Fernando Soto, Miguel Angel Lopez‐Ramirez, Itthipon Jeerapan, Berta Esteban‐Fernandez de Avila, Rupesh Kumar Mishra, Xiaolong Lu, Ingrid Chai, Chuanrui Chen, Daniel Kupor, Amir Nourhani, Joseph Wang

Advanced Functional Materials· Volume 29· Issue 22
Publisher page

Abstract

Abstract Self‐propelled biohybrid microrobots, employing marine rotifers as their engine, named “rotibot,” are presented and their practical utility and advantages for environmental remediation are demonstrated. Functionalized microbeads are attached electrostatically within the rotifer mouth and aggregated inside their inner lip. The high fluid flow toward the mouth, generated by the strokes of rotifer cilia bands, forces an extremely efficient transport of the contaminated sample over the active surfaces of the functionalized microbeads. The reactive particles confined around the rotifer's lip are thus exposed to a high flow rate of the pollutant solution, resulting in dramatically accelerated decontamination processes, without external mixing or harmful fuels. Theoretical simulations, modeling the greatly enhanced fluid dynamic associated with such built‐in mixing effect, correlate well with the experimental observations. The rotibot thus proves to be an effective, versatile, and robust dynamic microcleaning platform for removing diverse environmental pollutants. Microbeads functionalized with lysozyme and organophosphorus hydrolase enzymes are shown to be extremely useful for enzymatic biodegradation of Escherichia coli and the nerve agent methyl paraoxon, respectively, while ligand (meso‐2,3‐dimercaptosuccinic acid) modified beads are used for removing heavy metal contaminants. Rotifer‐based biohybrid microrobots hold considerable promise as self‐propelling dynamic pumps for diverse large‐scale environmental remediation applications.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Micro and Nano Robotics · Modular Robots and Swarm Intelligence · Pickering emulsions and particle stabilization

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Department of Nanoengineering University of California San Diego, La Jolla CA 92093 USA

Funding

Defense Threat Reduction Agency

References (62)

Show source-provided references
1. 10.1002/978352765145010.1002/9783527651450 ↗
2. 10.1016/j.nantod.2013.08.00910.1016/j.nantod.2013.08.009 ↗
3. 10.1002/adfm.20170610010.1002/adfm.201706100 ↗
4. 10.1002/adfm.20180629010.1002/adfm.201806290 ↗
5. 10.1039/C3TB21451F10.1039/C3TB21451F ↗
6. 10.1002/adfm.20170586710.1002/adfm.201705867 ↗
7. 10.1016/j.apmt.2017.08.00410.1016/j.apmt.2017.08.004 ↗
8. 10.1021/jacs.8b0576210.1021/jacs.8b05762 ↗
9. 10.1039/C5TA09850E10.1039/C5TA09850E ↗
10. 10.1021/nn500077a10.1021/nn500077a ↗
11. 10.1002/adma.20170391010.1002/adma.201703910 ↗
12. 10.1016/j.apmt.2017.09.00510.1016/j.apmt.2017.09.005 ↗
13. 10.1002/chem.20140501110.1002/chem.201405011 ↗
14. 10.1002/anie.20150197110.1002/anie.201501971 ↗
15. 10.1002/adfm.20160038110.1002/adfm.201600381 ↗
16. 10.1039/C5RA16615B10.1039/C5RA16615B ↗
17. 10.1021/acs.nanolett.5b0503210.1021/acs.nanolett.5b05032 ↗
18. 10.1002/chem.20150389210.1002/chem.201503892 ↗
19. 10.1002/adfm.20180276210.1002/adfm.201802762 ↗
20. 10.1002/adfm.20170425610.1002/adfm.201704256 ↗
21. 10.1002/adfm.20170233810.1002/adfm.201702338 ↗
22. 10.1021/acsnano.6b0625610.1021/acsnano.6b06256 ↗
23. 10.1021/acsami.7b0300610.1021/acsami.7b03006 ↗
24. 10.1021/nn505029k10.1021/nn505029k ↗
25. 10.1021/acsami.6b1208110.1021/acsami.6b12081 ↗
26. 10.1039/C6NR06603H10.1039/C6NR06603H ↗
27. 10.1002/adfm.20150446310.1002/adfm.201504463 ↗
28. 10.1126/scirobotics.aat048510.1126/scirobotics.aat0485 ↗
29. 10.1021/acsnano.5b0414210.1021/acsnano.5b04142 ↗
30. 10.1038/s41578-018-0016-910.1038/s41578-018-0016-9 ↗
31. 10.1002/adfm.20170542110.1002/adfm.201705421 ↗
32. 10.1002/smll.20170437410.1002/smll.201704374 ↗
33. 10.1016/j.addr.2016.09.00710.1016/j.addr.2016.09.007 ↗
34. 10.1021/acsnano.7b0639810.1021/acsnano.7b06398 ↗
35. 10.1299/mej.2014mn003810.1299/mej.2014mn0038 ↗
36. 10.1021/acsnano.7b0412810.1021/acsnano.7b04128 ↗
37. 10.1021/acsnano.7b0208210.1021/acsnano.7b02082 ↗
38. 10.1021/acsnano.7b0320710.1021/acsnano.7b03207 ↗
39. 10.1038/nnano.2016.13710.1038/nnano.2016.137 ↗
40. 10.1002/smll.20160367910.1002/smll.201603679 ↗
41. Alapan Y. · Annu. Rev. Control Rob. Auton. Syst. · 2018 · 2
42. 10.1002/adma.20180413010.1002/adma.201804130 ↗
43. 10.3389/fbioe.2018.0017010.3389/fbioe.2018.00170 ↗
44. 10.1038/nchem.189510.1038/nchem.1895 ↗
45. 10.1021/acsnano.8b0661010.1021/acsnano.8b06610 ↗
46. 10.1126/science.117925210.1126/science.1179252 ↗
47. 10.1073/pnas.111976210910.1073/pnas.1119762109 ↗
48. 10.1038/nnano.2008.11010.1038/nnano.2008.110 ↗
49. 10.1016/j.aquatox.2010.09.00610.1016/j.aquatox.2010.09.006 ↗
50. 10.1007/s10750-016-2919-z10.1007/s10750-016-2919-z ↗
51. 10.1126/science.288.5469.121110.1126/science.288.5469.1211 ↗
52. 10.1126/science.114724110.1126/science.1147241 ↗
53. 10.1073/pnas.140526011110.1073/pnas.1405260111 ↗
54. 10.1098/rsif.2009.041910.1098/rsif.2009.0419 ↗
55. 10.1038/nphys397010.1038/nphys3970 ↗
56. 10.1002/biot.20120031310.1002/biot.201200313 ↗
57. 10.1038/nbt1097-98410.1038/nbt1097-984 ↗
58. 10.1002/admt.20170021010.1002/admt.201700210 ↗
59. 10.1039/C5CC02222C10.1039/C5CC02222C ↗
60. 10.1039/C6EN00043F10.1039/C6EN00043F ↗
61. 10.1039/C6CC06632A10.1039/C6CC06632A ↗
62. 10.1242/jeb.16251110.1242/jeb.162511 ↗