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Record W3132020945 · doi:10.1002/advs.202004235

Solar‐Driven Soft Robots

2021· article· en· W3132020945 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 Science · 2021
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMicro and Nano Robotics
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsRobotSoft roboticsCollimated lightRoboticsArtificial intelligenceComputer scienceLaserMechanical engineeringElectrical engineeringEngineeringOpticsPhysics

Abstract

fetched live from OpenAlex

Stimuli-responsive materials have been lately employed in soft robotics enabling new classes of robots that can emulate biological systems. The untethered operation of soft materials with high power light, magnetic field, and electric field has been previously demonstrated. While electric and magnetic fields can be stimulants for untethered actuation, their rapid decay as a function of distance limits their efficacy for long-range operations. In contrast, light-in the form of sunlight or collimated from an artificial source (e.g., laser, Xenon lamps)-does not decay rapidly, making it suitable for long-range excitation of untethered soft robots. In this work, an approach to harnessing sunlight for the untethered operation of soft robots is presented. By employing a selective solar absorber film and a low-boiling point (34 °C) fluid, light-operated soft robotic grippers are demonstrated, grasping and lifting objects almost 25 times the mass of the fluid in a controllable fashion. The method addresses one of the salient challenges in the field of untethered soft robotics. It precludes the use of bulky peripheral components (e.g., compressors, valves, or pressurized gas tank) and enables the untethered long-range operation of 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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.562
Threshold uncertainty score0.297

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 head0.258
Teacher spread0.250 · 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