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Record W4409694724 · doi:10.47852/bonviewjopr52025116

Nano-Engineering Versatile Core-Shell Nanoplatforms for Tuning Enhanced Opto-electronic Matter Interactions

2025· article· en· W4409694724 on OpenAlexfundno aff
A. Guillermo Bracamonte

Bibliographic record

VenueJournal of Optics and Photonics Research · 2025
Typearticle
Languageen
FieldEngineering
TopicPhotonic and Optical Devices
Canadian institutionsnot available
FundersGillings School of Public HealthUniversidad de CórdobaNASA Astrobiology InstituteNational Aeronautics and Space AdministrationUniversität RegensburgUniversity of AkronUniversité Laval
KeywordsMaterials scienceShell (structure)NanotechnologyCore (optical fiber)Nano-Composite material

Abstract

fetched live from OpenAlex

In this communication, it is presented the concept of optics based on Core-shell architectures controlling the nanoscale and beyond. It is demonstrated how these types of architectures can be designed, from prototyping to synthesis, using colloidal and laser-based techniques. This particular nanoarchitecture was presented as a fundamental structure that can be tuned as needed through the use of various materials, enabling control over size, shape, and topological features. Then, the impact of Core-shell nanoarchitecture to tune photon and electron matter interactions to enhance physics and chemistry with perspectives of non-classical light generation is shown. Therefore, fundamental wet chemistry to advanced nano-optics studies by the use of Core-shell nanoplatforms is discussed. In this regard, varied phenomena such as enhanced and amplified approaches are presented. Enhanced based techniques such as metal-enhanced fluorescence (MEF), phosphorescence (MEP), enhanced quantum phenomena (EQ), and amplified signaling are of interest. In this context, the control of nanoarchitecture should be accurately controlled to place the different optical components. Optical active materials are varied to tune photon matter interactions accompanied by the generation of new modes of energies. These emerging non-classical light pathways involve strong electromagnetic field interactions, which significantly influence the electronic and optical properties of all participating materials. Moreover, photonics pathways could be modified to stabilize the excited state, increase quantum yields, and improve performances. In this context, the tuning of high-intense electromagnetic fields related with plasmonics and pseudo-electromagnetics from other semiconductors such as carbon-based materials was presented. Thus, targeted enhanced nano-emitters are presented and highlighted in this article; however, further discussion towards enhanced optics is discussed. In this manner, highlights from the nanoscale to far-field optical studies and applications are opened. So, Core-shell nanoarchitectures are presented as important optical nanoplatforms with high impact within varied optical set-ups. Received: 30 December 2024 | Revised: 21 February 2025 | Accepted: 26 March 2025 Conflicts of Interest The author declares that he has no conflicts of interest to this work. Data Availability Statement Data are available from the corresponding author upon reasonable request. Author Contribution Statement A. Guillermo Bracamonte: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration, Funding acquisition.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.029
GPT teacher head0.331
Teacher spread0.302 · 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 source (direct Gemma or distilled Codex), 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".

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Citations1
Published2025
Admission routes1
Has abstractyes

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