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Record W4386844814 · doi:10.1063/5.0156083

Design of a high counting efficiency sensor for 1.0 cubic feet per minute laser particle counter

2023· article· en· W4386844814 on OpenAlexaboutno aff
Yanchang Zheng, Guo Wu, Yesheng Chen, Yuelin Lu, Cheng Li, Guibo Wan, Rongxin Yin

Bibliographic record

VenueReview of Scientific Instruments · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicAir Quality Monitoring and Forecasting
Canadian institutionsnot available
FundersNational Natural Science Foundation of China
KeywordsOpticsParticle counterNozzleScatteringOptical pathMie scatteringLaserParticle (ecology)Light scatteringLens (geology)PhysicsMaterials sciencePath lengthAerosol

Abstract

fetched live from OpenAlex

Based on the theory of Mie scattering, an optical sensor for a 1.0 cubic feet per minute laser particle counter is developed in this paper. First, an illumination optical path relying on aspherical lens and cylindrical lens for laser shaping was designed, and a narrow optical spot with the illumination in the vertical direction of the photosensitive area approximately distributed as a flat top was obtained. Second, the scattering light path was designed by using the theory of Mie scattering and geometric optics method. Then, the structure of the sampling air path was designed with reference to the principle of Laval nozzle, and the particle flow trajectory was verified by simulation using the fluid dynamics software Ansys Fluent. Finally, the performance of the optical sensor was measured with polystyrene latex (PSL) particles, and the results showed that the counting efficiency of the 0.3 μm particle size channel met the requirement of 50% ± 20% and the 0.5 μm particle size channel met the requirement of 100% ± 10%, which complied with the ISO 21501-4 standard.

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)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.540
Threshold uncertainty score0.468

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.051
GPT teacher head0.288
Teacher spread0.237 · 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.

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

Citations2
Published2023
Admission routes1
Has abstractyes

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