MétaCan
Menu
Back to cohort
Record W2528946637 · doi:10.1021/acsphotonics.8b00325

Shaping Polaritons to Reshape Selection Rules

2018· article· en· W2528946637 on OpenAlexfundno aff
Francisco Machado, Nicholas Rivera, Hrvoje Buljan, Marin Soljačić, Ido Kaminer

Bibliographic record

VenueACS Photonics · 2018
Typearticle
Languageen
FieldEngineering
TopicPlasmonic and Surface Plasmon Research
Canadian institutionsnot available
FundersFP7 People: Marie-Curie ActionsArmy Research OfficeAzrieli FoundationU.S. Department of Energy
KeywordsPolaritonSelection (genetic algorithm)Computer sciencePhysicsOptoelectronicsArtificial intelligence

Abstract

fetched live from OpenAlex

The discovery of orbital angular momentum (OAM) in light established a new degree of freedom by which to control not only its flow but also its interaction with matter. Here, we show that by shaping extremely subwavelength polariton modes, for example by imbuing plasmon and phonon polaritons with OAM, we engineer which transitions are allowed or forbidden in electronic systems such as atoms, molecules, and artificial atoms. Crucial to the feasibility of these engineered selection rules is the access to conventionally forbidden transitions afforded by subwavelength polaritons. We also find that the position of the absorbing atom provides a surprisingly rich parameter for controlling which absorption processes dominate over others. Additional tunability can be achieved by altering the polaritonic properties of the substrate, for example by tuning the carrier density in graphene, potentially enabling electronic control over selection rules. Our findings are best suited to OAM-carrying polaritonic modes that can be created in graphene, monolayer conductors, thin metallic films, and thin films of polar dielectrics such as boron nitride. By building on these findings we foresee the complete engineering of spectroscopic selection rules through the many degrees of freedom in the shape of optical fields.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.573
Threshold uncertainty score0.999

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)0.0000.002

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 head0.027
GPT teacher head0.272
Teacher spread0.245 · 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 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

Citations29
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueACS PhotonicsSame topicPlasmonic and Surface Plasmon ResearchFrench-language works237,207