Evidence map›Paper›PMID 40097419›Full record

ArticleMicrosystems & nanoengineering2025

Automated and collision-free navigation of multiple micro-objects in obstacle-dense microenvironments using optoelectronic tweezers.

Lixiang Zheng, Gong Li, Henan Du, Zonghao Li, Bingrui Xu, Fan Yang, Yanan Mao, Jing Wei, Hainan Xie, Wei Xie and 6 more

Abstract read
In one paragraph

Article in Microsystems & nanoengineering, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

16 authors.

Lixiang Zheng *School of Mechatronics Engineering and Automation, Shanghai University, Shanghai, 200444, China.
Gong Li *School of Integrated Circuits and Electronics, Beijing Institute of Technology, Beijing, 100081, China.ORCID http://orcid.org/0009-0002-6190-0988
Henan DuSchool of Integrated Circuits and Electronics, Beijing Institute of Technology, Beijing, 100081, China.
Zonghao LiSchool of Integrated Circuits and Electronics, Beijing Institute of Technology, Beijing, 100081, China.
Bingrui XuBeijing Advanced Innovation Center for Intelligent Robots and Systems, School of Mechatronical Engineering, Beijing Institute of Technology, Beijing, 100081, China.
Fan YangBeijing Advanced Innovation Center for Intelligent Robots and Systems, School of Mechatronical Engineering, Beijing Institute of Technology, Beijing, 100081, China.ORCID http://orcid.org/0009-0004-1290-4418
Yanan MaoBeijing Advanced Innovation Center for Intelligent Robots and Systems, School of Mechatronical Engineering, Beijing Institute of Technology, Beijing, 100081, China.
Jing WeiSchool of Integrated Circuits and Electronics, Beijing Institute of Technology, Beijing, 100081, China.
Hainan XieOptoseeker Biotechnology (Shenzhen) Co., Ltd, Shenzhen, 518055, China.
Wei XieOptoseeker Biotechnology (Shenzhen) Co., Ltd, Shenzhen, 518055, China.
Rongxin FuZhengzhou Research Institute, Beijing Institute of Technology, Henan, 450000, China.ORCID http://orcid.org/0000-0002-4547-2269
Na LiuSchool of Mechatronics Engineering and Automation, Shanghai University, Shanghai, 200444, China. liuna_sia@shu.edu.cn.ORCID http://orcid.org/0009-0003-9872-3476
Shuailong ZhangSchool of Integrated Circuits and Electronics, Beijing Institute of Technology, Beijing, 100081, China. shuailong.zhang@bit.edu.cn.ORCID http://orcid.org/0000-0003-1004-021X
Lianqing LiuState Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, 110016, China.
Wen Jung LiDepartment of Mechanical Engineering, City University of Hong Kong, Hong Kong, China.ORCID http://orcid.org/0000-0001-9616-6213
Yu SunDepartment of Mechanical and Industrial Engineering, University of Toronto, Toronto, ON, M5S 3G8, Canada.

Funding

National Natural Science Foundation of China (National Science Foundation of China) 62103050Natural Science Foundation of Beijing Municipality (Beijing Natural Science Foundation) 4242060Natural Science Foundation of Beijing Municipality (Beijing Natural Science Foundation) L246030Natural Science Foundation of Chongqing (Natural Science Foundation of Chongqing Municipality) 2024NSCQ-JQX0192
6 · The paper itself

Abstract

Automated parallel manipulation of multiple micro-objects with optoelectronic tweezers (OET) has brought significant research interests recently. However, the parallel manipulation of multiple objects in complex obstacle-dense microenvironment using OET technology based on negative dielectrophoresis (nDEP) remain a big technical challenge. In this work, we proposed an adaptive light pattern design strategy to achieve automated parallel OET manipulation of multiple micro-objects and navigate them through obstacles to target positions with high precision and no collision. We first developed a multi-micro-object parallel manipulation OET system, capable of simultaneous image processing and microparticles path planning. To overcome microparticle collisions caused by overlapping light patterns, we employed a novel adaptive light pattern design that can dynamically adjust the layout of overlapping light patterns according to surrounding environment, ensuring enough space for each microparticle and preventing unintended escapes from the OET trap. The efficacy of this approach has been verified through systematic simulations and experiments. Utilizing this strategy, multiple polystyrene microparticles were autonomously navigated through obstacles and microchannels to their intended destinations, demonstrating the strategy's effectiveness and potential for automated parallel micromanipulation of multiple microparticles in complex and confined microenvironments.

Identifiers

PMID40097419
PMCPMC11914063

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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.