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Record W4412935878 · doi:10.1002/aisy.202500051

Advances in 3D Printing Technologies for Fabricating Magnetic Soft Microrobots

2025· article· en· W4412935878 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAdvanced Intelligent Systems · 2025
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMicro and Nano Robotics
Canadian institutionsMcMaster University
Fundersnot available
KeywordsRobotMagnetism3D printingNanotechnologyFabricationComputer scienceSoft roboticsSoft materialsProcess (computing)Systems engineeringEngineeringMechanical engineeringMaterials scienceArtificial intelligencePhysics

Abstract

fetched live from OpenAlex

Magnetic soft robots have garnered interest in recent years due to their various capabilities specifically in biomedical applications. These robots are fabricated by combining magnetic microparticles with soft elastomers to create composite materials, in order to achieve stimuli‐responsive properties. Such structures enable precise and remote actuation for controlled movement. Advancements are currently being made in many aspects of fabrication, such as sensor incorporation, actuation and navigation systems, and design optimization. This review provides a comprehensive summary of the fundamental principles of magnetism and common actuation techniques to help understand how these magnetic soft robots are designed and fabricated using 3D printing technology. Each fabrication technique outlines the general process, advantages, disadvantages, and capabilities such as resolution. Key applications for both biomedical and environmental areas are examined. Finally, current challenges and future research directions are outlined to advance the design and functionality of magnetic soft robots.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.973
Threshold uncertainty score0.815

Codex and Gemma teacher scores by category

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

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 head0.008
GPT teacher head0.273
Teacher spread0.265 · 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