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Record W4386772763 · doi:10.1002/solr.202300538

Hyperspectral Photoluminescence Imaging for Spatially Resolved Determination of Electrical Parameters of Laser‐Patterned Perovskite Solar Cells

2023· article· en· W4386772763 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueSolar RRL · 2023
Typearticle
Languageen
FieldEngineering
TopicSilicon and Solar Cell Technologies
Canadian institutionsPhoton Etc (Canada)
Fundersnot available
KeywordsMaterials scienceHyperspectral imagingPhotoluminescenceLaserInterconnectionOptoelectronicsDiodePicosecondLaser diodeOpticsNanosecondRemote sensingComputer sciencePhysicsTelecommunicationsGeology

Abstract

fetched live from OpenAlex

Absolute calibrated hyperspectral photoluminescence (PL) imaging is utilized to access, in a simple and fast way, the spatial distribution of relevant solar cell parameters such as quasi‐Fermi level splitting, optical diode factor, Urbach energies E u , and shunt resistances R sh , without the need for electrical measurements. Since these metrics play a significant role in evaluating the process windows for electrical series interconnection by laser patterning, this approach is followed to systematically locate and quantify electrical losses that may occur as a result of the laser‐patterning process for monolithic series interconnection. It is shown that both picosecond and nanosecond laser pulses can be used for successful series interconnection. In both cases, only minor lateral material alterations occur, localized in a few μm wide region adjacent to the edges of the scribe lines. Furthermore, the acquisition and analysis of these hyperspectral PL datasets provide insights in the material removal process, from which it is concluded that the perovskite is rather resilient against the thermal impact of the laser.

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.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.212
Threshold uncertainty score0.725

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)0.0000.000

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 head0.013
GPT teacher head0.229
Teacher spread0.216 · 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