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Record W2966692708 · doi:10.1002/jrs.5679

One‐step facile synthesis of PbS quantum dots/Pb (DMDC)<sub>2</sub> hybrids and their application as a low‐cost SERS substrate

2019· article· en· W2966692708 on OpenAlexaff
Aili Liu, Huile Jin, Jun Li, Liyun Chen, Haoyuan Zheng, Xinnan Mao, Dajie Lin, Jichang Wang, Shun Wang, Weizhong Jiang

Bibliographic record

VenueJournal of Raman Spectroscopy · 2019
Typearticle
Languageen
FieldMaterials Science
TopicGold and Silver Nanoparticles Synthesis and Applications
Canadian institutionsUniversity of Windsor
FundersNatural Science Foundation of Zhejiang ProvinceNational Natural Science Foundation of China
KeywordsNanorodNanotechnologyRaman spectroscopyQuantum dotSubstrate (aquarium)Raman scatteringMaterials scienceLead sulfideSurface-enhanced Raman spectroscopyNanoparticleChemistryOptics

Abstract

fetched live from OpenAlex

Abstract A microwave‐ and ultrasound‐assisted reduction and assembly of lead dimethyldithiocarbamate (Pb (DMDC)2) led to the formation of rod‐like Pb (DMDC)2 embedded with PbS quantum dots (QDs). Transmission electron microscopy measurements show the presence of numerous isolated PbS QDs on the Pb (DMDC)2 microrod surface, which may potentially act as hot spots for enhanced Raman spectroscopy. A case study using mercaptopyridine as the analyte demonstrates that the rod‐like hybrids have outstanding performance as a surface‐enhanced Raman scattering (SERS) substrate, comparable with that of Au nanorods. The excellent SERS performance of the as‐fabricated PbS QDs/Pb (DMDC)2 hybrids may be attributed to the unique dispersion of PbS QDs as well as the intimate contacts between PbS QDs and Pb (DMDC)2. This study therefore presents a new approach for the development of low‐cost and highly efficient semiconductor‐based SERS substrates.

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 head0.007
GPT teacher head0.222
Teacher spread0.214 · 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

Citations5
Published2019
Admission routes1
Has abstractyes

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