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Record W4288715931 · doi:10.1038/s41598-022-17053-x

Macrophage-compatible magnetic achiral nanorobots fabricated by electron beam lithography

2022· article· en· W4288715931 on OpenAlexaff
Teng Jiang, Xiaoxia Song, Xueliang Mu, U Kei Cheang

Bibliographic record

VenueScientific Reports · 2022
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMicro and Nano Robotics
Canadian institutionsUniversity of Calgary
FundersSouthern University of Science and TechnologyDepartment of Education of Guangdong ProvinceNational Natural Science Foundation of China
KeywordsNanoroboticsNanotechnologyMaterials scienceDrug deliveryElectron-beam lithographyResist

Abstract

fetched live from OpenAlex

With the development and progress of nanotechnology, the prospect of using nanorobots to achieve targeted drug delivery is becoming possible. Although nanorobots can potentially improve nano-drug delivery systems, there remains a significant challenge to fabricating magnetically controllable nanorobots with a size suitable for drug delivery in complex in vivo environments. Most of the current research focused on the preparation and functionalization of microscale and milliscale robots due to the relative difficulties in fabricating nanoscale robots. To address this problem and move towards in vivo applications, this study uses electron beam lithography to fabricate achiral planar L-shaped nanorobots that are biocompatible with immune cells. Their minimal planar geometry enabled nanolithography to fabricate nanorobots with a minimum feature size down to 400 nm. Using an integrated imaging and control system, the locomotive behavior of the L-shaped nanorobots in a fluidic environment was studied by examining their velocity profiles and trajectories. Furthermore, the nanorobots exhibit excellent cell compatibility with various types of cells, including macrophage cells. Finally, the long-term cell culture medium immersion test demonstrated that the L-shaped nanorobots have robust stability. This work will demonstrate the potential to use these nanorobots to operate in vivo without triggering immune cell responses.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.285
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.004
GPT teacher head0.208
Teacher spread0.204 · 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.

Study designNot applicable
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

Citations8
Published2022
Admission routes1
Has abstractyes

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