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Record W4386600831 · doi:10.3390/radiation3030014

Early and Transient Formation of Highly Acidic pH Spikes in Water Radiolysis under the Combined Effect of High Dose Rate and High Linear Energy Transfer

2023· article· en· W4386600831 on OpenAlexafffund
Md Ibrahim Bepari, Jintana Meesungnoen, Jean‐Paul Jay‐Gerin

Bibliographic record

VenueRadiation · 2023
Typearticle
Languageen
FieldMedicine
TopicRadiation Therapy and Dosimetry
Canadian institutionsUniversité de Sherbrooke
FundersNatural Sciences and Engineering Research Council of CanadaUniversité de Sherbrooke
KeywordsRadiolysisLinear energy transferIrradiationChemistryIonMonte Carlo methodRadiationRadiation chemistryDose rateIonizing radiationRadiochemistryPhysicsNuclear physics

Abstract

fetched live from OpenAlex

(1) Background: Water radiolysis leads to the formation of hydronium ions H3O+ in less than 50 fs, resulting in the formation of transient acidic pH spikes in the irradiated water. The purpose of this study is to examine the time evolution of these spikes of acidity under irradiation conditions combining both high absorbed dose rate and high-LET radiation. (2) Methods: The early space–time history of the distributions of the various reactive species was obtained using our Monte Carlo multitrack chemistry simulation code IONLYS-IRT. To simulate different LETs, we used incident protons of varying energies as radiation sources. The “instantaneous pulse” (or Dirac) model was used to investigate the effect of dose rate. (3) Results: One major finding is that the combination of high dose rates and high LETs is clearly additive, with a very significant impact on the pH of the solution. For example, at 1 ns and for a dose rate of ~107 Gy/s, the pH drops from ~4.7 to 2.7 as the LET increases from ~0.3 to 60 keV/μm. (4) Conclusions: Confirming previous work, this purely radiation chemical study raises the question of the possible importance and role of these spikes of acidity in underpinning the physical chemistry and biology of the “FLASH effect”.

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)0.001
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 score0.386
Threshold uncertainty score0.266

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.006
GPT teacher head0.222
Teacher spread0.215 · 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

Citations9
Published2023
Admission routes2
Has abstractyes

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