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Record W4403716488 · doi:10.1021/accountsmr.4c00187

From Anisotropic Molecules and Particles to Small-Scale Actuators and Robots: An Account of Polymerized Liquid Crystals

2024· article· en· W4403716488 on OpenAlexafffund
Negar Rajabi, Matthew Gene Scarfo, Cole Martin Fredericks, Azin Adibi, Hamed Shahsavan

Bibliographic record

VenueAccounts of Materials Research · 2024
Typearticle
Languageen
FieldEngineering
TopicAdvanced Materials and Mechanics
Canadian institutionsNational Institute for NanotechnologyUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsLiquid crystalPolymerizationActuatorScale (ratio)AnisotropyRobotMaterials scienceChemical physicsMoleculeNanotechnologyChemical engineeringComposite materialChemistryPolymerPhysicsComputer scienceOptoelectronicsOpticsEngineeringOrganic chemistryArtificial intelligenceQuantum mechanics

Abstract

fetched live from OpenAlex

Conspectus Untethered small-scale (milli-, micro-, and nano-) soft robots promise minimally invasive and targeted medical procedures in tiny, flooded, and confined environments like inside the human body. Despite such potentials, small-scale robots have not yet found their way to real-world applications. This can be mainly attributed to the fundamental and technical challenges in the fabrication, powering, navigation, imaging, and closed-loop control of robots at submillimiter scales. Pertinent to this Account, the selection of building block materials of small-scale robots also poses a challenge that is directly related to their fabrication and function. Early work in microrobotics focused on the mechanism of locomotion in fluids with low Reynolds number ( Re ≪ 1), which was mainly inspired by the motility of cells and microorganisms. Looking closely at the motile cells and microorganisms, one can find both order and anisotropy within their microstructure, driving out-of-equilibrium asymmetric deformations of their soft bodies and appendages like cilia and flagella, resulting in locomotion and function in environments with low Re number. Microroboticists aim to mimic microorganisms’ locomotion and function in developing mobile small-scale robots. It is known that soft, ordered, and anisotropic microstructures of microorganisms are examples of liquid crystalline systems. With this in mind, we believe that liquid crystals are underutilized in the design of small-scale robots, even though they have remarkable similarities to biological materials and constructs. In this Account, we have shed light on the role liquid crystals have played and can play in the design of small-scale robots. For this, we have first elaborated on the fundamentals of liquid crystals, which include a discussion of the various types of liquid crystals and their characteristics, their mesophase behavior, and their anisotropic properties. Then, we have discussed the applicability of anisotropic elastic networks of liquid crystals in the design of actuators which must satisfy all four programming pillars, including elasticity, alignment, responsiveness, and initial geometry. We have highlighted landmark reports where anisotropic elastic networks of liquid crystals, such as liquid crystal elastomers (LCEs), networks (LCNs), and hydrogels, are utilized as structural materials in the design of soft, small-scale actuators and robots. We point out the prevalence of the nematic phase and thermotropic liquid crystals utilized in these constructs over other mesophases and liquid crystal types as part of our discussion on the pros and cons of liquid crystals for microrobotics research. Finally, paths forward for the widespread applicability of liquid crystal microrobotics are envisaged. Specifically, the potential of soft robots constructed from elastic networks of chromonic and micellar lyotropic liquid crystals provides a substantial, yet daunting, opportunity for research. Furthermore, miniaturizing these constructs through innovative, combinatorial alignment–fabrication strategies on the microscale could realize liquid crystal soft robots suitable for biomedical applications, unlike those made from thermotropic liquid crystals. Additionally, programming alignment in alternative mesophases, such as smectic, may portray new research avenues in this emerging technology.

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 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: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.589

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 head0.031
GPT teacher head0.314
Teacher spread0.283 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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

Citations6
Published2024
Admission routes2
Has abstractyes

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