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Record W4386542750 · doi:10.1002/anbr.202300018

Development of 3D‐Printed Magnetic Micro‐Nanorobots for Targeted Therapeutics: the State of Art

2023· article· en· W4386542750 on OpenAlexaff
Ningning Hu, Lujia Ding, Yuyi Liu, Kemin Wang, Bing Zhang, Ruixue Yin, Wenju Zhou, Zhuming Bi, Wenjun Zhang

Bibliographic record

VenueAdvanced NanoBiomed Research · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMicro and Nano Robotics
Canadian institutionsUniversity of Saskatchewan
FundersChina Scholarship Council
KeywordsNanoroboticsRobotComputer scienceNanotechnologyState of artSystems engineeringEngineeringMaterials scienceArtificial intelligenceData science

Abstract

fetched live from OpenAlex

Micro‐/nanorobots (mn‐robots), inspired by the versatile mechanisms found in natural microorganisms, show great potential in enabling innovative bio‐applications. The 3D‐printed magnetic mn‐robots are substantially advanced to swim in vivo and to carry and release therapeutic agents in a controlled manner. To understand the state of the art of such robots and identify their development trend, this article presents a comprehensive and systematic review of the recent works on development of magnetic robots and their applications in biomedical engineering, with a particular focus on targeted therapeutic delivery. The developments in materials, fabrications, actuations, and applications with design for magnetic mn‐robots are reviewed, and it is aimed to discover the limitations of the existing works and to identify the knowledge gap, thereby deriving future research directions on developing magnetic mn‐robots, especially for their applications in targeted therapeutic delivery.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.050
GPT teacher head0.357
Teacher spread0.306 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations17
Published2023
Admission routes1
Has abstractyes

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