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Record W2408507323 · doi:10.1038/srep26163

Ultra-high density optical data storage in common transparent plastics

2016· article· en· W2408507323 on OpenAlexafffund
Deepak L. N. Kallepalli, A.M. Alshehri, Daniela T. Marquez, Lukasz Andrzejewski, J. C. Scaiano, V. R. Bhardwaj

Bibliographic record

VenueScientific Reports · 2016
Typearticle
Languageen
FieldMaterials Science
TopicPhase-change materials and chalcogenides
Canadian institutionsUniversity of Ottawa
FundersCanada Research ChairsUniversity of Ottawa
KeywordsMultiplexingComputer data storageStack (abstract data type)3D optical data storageOptical storageLaserOptical discMaterials scienceOptoelectronicsOptical recordingVolume (thermodynamics)Area densityDetectorComputer scienceOpticsComputer hardwareTelecommunicationsPhysics

Abstract

fetched live from OpenAlex

The ever-increasing demand for high data storage capacity has spurred research on development of innovative technologies and new storage materials. Conventional GByte optical discs (DVDs and Bluray) can be transformed into ultrahigh capacity storage media by encoding multi-level and multiplexed information within the three dimensional volume of a recording medium. However, in most cases the recording medium had to be photosensitive requiring doping with photochromic molecules or nanoparticles in a multilayer stack or in the bulk material. Here, we show high-density data storage in commonly available plastics without any special material preparation. A pulsed laser was used to record data in micron-sized modified regions. Upon excitation by the read laser, each modified region emits fluorescence whose intensity represents 32 grey levels corresponding to 5 bits. We demonstrate up to 20 layers of embedded data. Adjusting the read laser power and detector sensitivity storage capacities up to 0.2 TBytes can be achieved in a standard 120 mm disc.

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 score0.002
Threshold uncertainty score0.006

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.072
GPT teacher head0.291
Teacher spread0.219 · 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".

Quick stats

Citations79
Published2016
Admission routes2
Has abstractyes

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Same venueScientific ReportsSame topicPhase-change materials and chalcogenidesFrench-language works237,207