Evidence map›Paper›PMID 39733160›Full record

ArticleScientific reports2024

Enhanced high-sensitivity multi-layer neutron detector based on LiF:ZnS(Ag) scintillator.

Ilan Cohen Zada, Alon Osovizky, Itzhak Orion

Abstract read
In one paragraph

Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Ilan Cohen ZadaNuclear Engineering Unit, Ben-Gurion University of the Negev, Beer-Sheva, Israel.
Alon OsovizkyElectronics & Control Laboratories, Nuclear Research Center Negev, Dimona, Israel.
Itzhak OrionNuclear Engineering Unit, Ben-Gurion University of the Negev, Beer-Sheva, Israel. iorion@bgu.ac.il.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study proposes a novel, highly sensitive neutron detector design utilizing a unique multi-layered configuration. Each layer consists of a LiF: ZnS(Ag) scintillator coupled with a transparent neutron moderator that also functions as a light guide for the Silicon Photomultiplier (SiPM) light sensor. This design offers a cost-effective and readily available alternative for existing neutron detectors. The research focused on optimizing a single layer for maximum sensitivity and proper gamma rejection capabilities. This optimized configuration is then replicated to form the multi-layer detector. A primary challenge with the LiF:ZnS(Ag) scintillator is its inherent opacity, limiting its width and consequently, its detection efficiency. Our proposed multi-layer structure addresses this limitation by employing a thin scintillator in each layer. This strategic design minimizes emitted light attenuation while simultaneously enhancing sensitivity through the cumulative effect of multiple layers. Our experiments demonstrate a significant improvement in detection efficiency compared to the single-layer setup. Additionally, our architecture offers an actual improvement in differentiating between gamma and neutron signals. By analyzing count rates across the detector's layers, we gain valuable operational insights, such as the ability to predict the source direction. Our finding demonstrates an improved sensitivity achieved by minimal loss of neutrons during the moderation of 329% compared to a single layer, aligned with the potential range of improvement while maintaining extremely high gamma rejection. Supported by the presented findings, this design represents a noticeable advancement over existing solutions, offering scalable customization to user requirements. Notably, it outperforms traditional

Indexed as

LiF:ZnS(Ag)Neutron detectionSilicon photomultiplier

Identifiers

PMID39733160
PMCPMC11682068

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.