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Record W4388069425 · doi:10.14233/ajchem.2023.28287

MgAl2O4:Ho3+ Nanophosphors: Electrochemical Sensor, Photoluminescence and Photocatalytic Applications

2023· article· en· W4388069425 on OpenAlexaff
S.N. MANJULA, M. Chandrasekhar, M.R. Anil Kumar, N. Raghavendra, C.R. Ravikumar, H. C. Ananda Murthy, H. Nagabhushana

Bibliographic record

VenueAsian Journal of Chemistry · 2023
Typearticle
Languageen
FieldEngineering
TopicGas Sensing Nanomaterials and Sensors
Canadian institutionsConcordia University
Fundersnot available
KeywordsPhotoluminescencePhotocatalysisOrange (colour)ChemistryElectrochemistryAnalytical Chemistry (journal)DopingPhotochemistryElectrodeMaterials scienceOptoelectronicsPhysical chemistryCatalysisEnvironmental chemistry

Abstract

fetched live from OpenAlex

Ho3+-doped MgAl2O4 nanophosphors (1-11 mol%) was synthesised via solution combustion method using oxalyl dihydrazide (ODH) as fuel. The findings revealed a range of band gap energies (Eg) between 4.86 and 5.42 eV. It was confirmed that these values correspond extremely well with the experimental data using the DFT approach. The Ho3+ ions in the host undergo f-f transitions that are triggered at 406 nm and as a result, exhibit discrete photoluminescence emission peaks between 406 and 605 nm. Enhanced MgAl2O4 have CIE coordinates from orange-red to yellow region and 97% colour purity. Photocatalytic properties of nanophosphors under UV light led to the discovery that a rapid orange-red dye is activated at 493 nm. Removal of fast orange red (F-OR) dye using the new photocatalysts and MgAl2O4:Ho3+ nanophosphors has been demonstrated. The investigation showed that 89.02% of the dye lost its colour after being exposed to radiation for 120 min. The modified MgAl2O4:Ho3+ carbon paste electrode used in the cyclic voltametric (CV) technique for lead pollution detection. Based on their electrochemical performance, it is concluded that MgAl2O4:Ho3+ nanophosphors are a viable material for lead detecting electrodes.

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)0.000
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 score0.004
Threshold uncertainty score0.583

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 head0.004
GPT teacher head0.188
Teacher spread0.183 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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