Light-Fueled Liquid Crystal Networks for Aquatic Soft Robotics
Bibliographic record
Abstract
The field of soft robotics has developed in response to the need for mechanisms that can operate safely in interaction with humans. Within the field of soft robotics, there is a growing demand for the development of small-scale soft devices capable of non-invasive medical interventions in a variety of technologies, including medical microrobotics, biosensing, and biomedical engineering. Among the many materials used for small-scale soft robotics, liquid crystal networks (LCNs) are of particular interest. LCNs are molecularly anisotropic and demonstrate reversible, programmable shape change upon exposure to external stimuli such as light or heat. However, the actuation of LCNs is often triggered by light, either photochemically or photothermally, which is typically less efficient when operating in flooded environments. Actuation in confined and flooded environments is an important challenge that must be overcome for the implementation of LCNs in real-world biomedical applications. Decoupling the mechanisms of powering, locomotion, and control from robotic functions is a strong solution for achieving efficient operation in flooded media. This work showcases two examples of decoupling locomotion and robotic function for the efficient use of small-scale devices at the air-water interface. This is done through the use of LCNs for control in conjunction with protein motors for powering. An LCN that responds to visible light, rather than UV, is also fabricated and characterized, and will be used for underwater robotic applications in future studies. \nIn the first case study, protein motors and LCNs are used to power and control a multi-component mechanical device. A milli-scale gear train with integrated motor and clutch functionalities is fabricated and operated at the air-water interface. The driving gear has a protein motor coating and generates propulsive force using the Marangoni effect. This force is transmitted through the gear train unless the clutch gear is activated. The clutch gear is fabricated from a photothermally responsive LCN that has been plasticized through the addition of a nematogenic solvent to improve actuation efficiency. The teeth of the clutch gear bend downwards to disengage from the gear train upon exposure to light, halting the chain of motion on demand. The second case study utilizes photochemical LCNs with applied protein motor coating to construct a V-shaped swimmer that moves across the surface of the water. Photochemical LCNs are used for their meta-stable state that allows for deformations to be held without constant exposure to stimuli. The protein motors are integrated directly onto the LCN swimmer for a single device with orthogonal mechanisms for powering and control. The protein motors generate force that propels the swimmer forward while deformation of the LCN is used for directional control. \nIn both case studies, UV light is required for shape-change. Looking forward to biomedical applications, a photochemical LCN that is responsive to visible light is also developed and characterized in both air and water. The design of LCN-based small-scale devices with a focus on safe and efficient operation in wet environments will open up new and exciting applications.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| 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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".