Multifunctional and Reprogrammable Magnetoactive Graphene Oxide Origami
Bibliographic record
Abstract
Magnetoactive materials, which change shape in response to magnetic fields, hold significant potential for applications in soft robotics, biomedical devices, and morphable structures. However, existing systems often suffer from complex fabrication processes, limited geometric customizability, and inefficient magnetization reprogramming strategies, especially for 3D structures. Here, lightweight magnetic graphene oxide (MGO) bilayer films incorporating hard-magnetic microparticles are introduced to enable fast, precise, and stable shape-morphing under magnetic actuation, including in aqueous environments. The paper-like nature of MGO films allows low-cost and straightforward fabrication of customized structures through post-processing steps such as cutting, folding, and assembly. In addition, the hygroscopic properties of GO introduce a humidity-tunable actuation, offering an extra degree of control. To address the reprogramming challenge, a reversible, high-throughput, and energy-efficient strategy is introduced based on the rearrangements of reusable MGO magnetic stickers, enabling multimodal magnetic shape reconfiguration and functional versatility. Their applications are showcased in in situ mechanical state transitions, sequential logic computing, and soft robot locomotion. Finally, a MGO sensoriactuator is demonstrated capable of magnetic actuation and real-time deformation monitoring, paving the way for closed-loop soft robotic systems. This work presents a sustainable, reconfigurable, and multifunctional strategy for advancing next-generation intelligent magnetoactive soft machines.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".