Evidence map›Paper›PMID 41748543›Full record

ArticleMicrosystems & nanoengineering2026

Robotic cell transportation system based on micropipette resistance modeling.

Qili Zhao, Mengya Liu, Ripeng Zhu, Jinyu Qiu, Shaojie Fu, Ruimin Li, Xin Zhao

Abstract read
In one paragraph

Article in Microsystems & nanoengineering, 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

7 authors.

Qili ZhaoNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Engineering Research Center of Trusted Behavior Intelligence, Ministry of Education, Tianjin Key Laboratory of Intelligent Robotic (tjKLIR), Institute of Robotics and Automatic Information System (IRAIS), Nankai University, Tianjin, 300350, China.
Mengya LiuNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Engineering Research Center of Trusted Behavior Intelligence, Ministry of Education, Tianjin Key Laboratory of Intelligent Robotic (tjKLIR), Institute of Robotics and Automatic Information System (IRAIS), Nankai University, Tianjin, 300350, China.
Ripeng ZhuNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Engineering Research Center of Trusted Behavior Intelligence, Ministry of Education, Tianjin Key Laboratory of Intelligent Robotic (tjKLIR), Institute of Robotics and Automatic Information System (IRAIS), Nankai University, Tianjin, 300350, China.
Jinyu QiuNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Engineering Research Center of Trusted Behavior Intelligence, Ministry of Education, Tianjin Key Laboratory of Intelligent Robotic (tjKLIR), Institute of Robotics and Automatic Information System (IRAIS), Nankai University, Tianjin, 300350, China.
Shaojie FuNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Engineering Research Center of Trusted Behavior Intelligence, Ministry of Education, Tianjin Key Laboratory of Intelligent Robotic (tjKLIR), Institute of Robotics and Automatic Information System (IRAIS), Nankai University, Tianjin, 300350, China.
Ruimin LiNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Engineering Research Center of Trusted Behavior Intelligence, Ministry of Education, Tianjin Key Laboratory of Intelligent Robotic (tjKLIR), Institute of Robotics and Automatic Information System (IRAIS), Nankai University, Tianjin, 300350, China.
Xin ZhaoNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Engineering Research Center of Trusted Behavior Intelligence, Ministry of Education, Tianjin Key Laboratory of Intelligent Robotic (tjKLIR), Institute of Robotics and Automatic Information System (IRAIS), Nankai University, Tianjin, 300350, China. zhaoxin@nankai.edu.cn.ORCID http://orcid.org/0000-0003-0631-4628

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Cell transportation, using a micropipette to pick and place cells from one droplet to another, is a key step in many cell engineering applications. However, most of the current cell transportation operations rely on microscope vision guidance, unsuitable for the future integrated and automated cell engineering applications where microscopic views are usually missed. The presenting microscopic view-free cell transportation systems are only applicable for special giant cells due to the bilayer structure of operation micropipettes. In this paper, a robotic cell transportation system based on micropipette resistance modeling was developed to transport common-sized cells without a microscopic view. First, a narrow-necked micropipette (NNM) was fabricated for holding the target cell inside the micropipette during transportation. Then, a gap resistance model, an aspiration resistance model, and an injection resistance model of the micropipette were developed to land on the cell plane, pick and release the cell without a microscopic view, respectively. Based on the above work, a robotic transportation process was established to transport the common-sized cells without a microscopic view. Finally, experimental results demonstrate that the proposed system can land on the cell plane with 100% success rate. It can transport 10 μm-level sized HeLa cells and 100 μm-level sized porcine oocytes with efficiencies comparable to common microscopic view-based methods and without harm to cell survival rate. Our microscopic cell transportation system can be upgraded to a high-throughput version for integrated automated cell engineering applications in the future.

Identifiers

PMID41748543
PMCPMC12946348

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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 Socratic graph.