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Record W2067909091 · doi:10.1515/plm-2012-0020

Ablation and thermal effects in treatment of hard and soft materials and biotissues using ultrafast-laser pulse-train bursts

2012· article· en· W2067909091 on OpenAlexaff
R. S. Marjoribanks, Christian Dille, Joshua E. Schoenly, Luke McKinney, Aghapi Mordovanakis, Patrick Kaifosh, Paul Forrester, Zuoming Qian, Andrés Covarrubias, Yuanfeng Feng, Lothar Lilge

Bibliographic record

VenuePhotonics and Lasers in Medicine · 2012
Typearticle
Languageen
FieldMedicine
TopicLaser Applications in Dentistry and Medicine
Canadian institutionsOntario Institute for Cancer ResearchUniversity of Toronto
Fundersnot available
KeywordsLaserAblationUltrashort pulseMaterials scienceOpticsPulse (music)Laser ablationPulse durationOptoelectronicsPhysics

Abstract

fetched live from OpenAlex

Abstract Ultrafast laser pulses (≤1 ps) are qualitatively different in the nature of their interaction with materials, including biotissues, as compared to nanosecond or longer pulses. This can confer pronounced advantages in outcomes for tissue therapy or laser surgery. At the same time, there are distinct limitations of their strong-field mode of interaction. As an alternative, it is shown here that ultrafast laser pulses delivered in a pulse-train burst mode of radiant exposure can access new degrees of control of the interaction process and of the heat left behind in tissues. Using a laser system that delivers 1 ps pulses in 20 μs pulse-train bursts at 133 MHz repetition rates, a range of heat and energy-transfer effects on hard and soft tissue have been studied. The ablation of tooth dentin and enamel under various conditions, to assess the ablation rate and characterize chemical changes that occur, are reported. This is compared to ablation in agar gels, useful live-cell-culture phantom of soft tissues, and presenting different mechanical strength. Study of aspects of the optical science of laser-tissue interaction promises to make qualitative improvements to medical treatments using lasers as cutting and ablative tools.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.391
Threshold uncertainty score0.520

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.025
GPT teacher head0.316
Teacher spread0.292 · 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 designObservational
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

Citations38
Published2012
Admission routes1
Has abstractyes

Explore more

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