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Record W4235623274 · doi:10.26434/chemrxiv.14061680

Assembly and Application of a Low Budget Photo Reactor

2021· preprint· en· W4235623274 on OpenAlexfundno aff
Olov Wallner, Kirill Mamonov, Florian Ortis, Maurice Michel

Bibliographic record

VenueChemRxiv · 2021
Typepreprint
Languageen
FieldEngineering
TopicInnovative Microfluidic and Catalytic Techniques Innovation
Canadian institutionsnot available
FundersFondation pour la Recherche MédicaleEuropean Federation of Pharmaceutical Industries and AssociationsMcGill University
KeywordsScrapLimitingIrradiationNuclear engineeringReactor designField (mathematics)Process engineeringMaterials scienceComputer scienceNanotechnologyEnvironmental scienceChemistryEngineeringMechanical engineeringPhysicsNuclear physics

Abstract

fetched live from OpenAlex

Photochemistry is a fast growing research field and many transformations previously not accessible to chemists now have become part of an ever growing standard repertoire. The limiting factors for a photo reactor system however is the possibility to perform stirring, removal of excess heat and the irradiation with UV or visible light – all that within a secure surrounding. Systems for starters may be as expensive as several thousand Euro. Here we design and assemble a LED photo reactor using scrap and standard materials, only spending less than 30 € for a LED. The system may be adjusted to any required wave length and its assembly is shown for the use of a 400 nm wave length lamp. To demonstrate its application, we then exemplarily use the reactor in the removal of a photo labile protection group during the synthesis of a SARS-CoV-2 spike protein glyco peptide.

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)0.001
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: Methods · Consensus signal: Methods
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.005

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 head0.010
GPT teacher head0.238
Teacher spread0.228 · 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
GenreMethods

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

Citations1
Published2021
Admission routes1
Has abstractyes

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