Evidence map›Paper›PMID 42817492›Full record

ArticleSensors (Basel, Switzerland)2026

LuminaOSL: Development and Experimental Validation of a Modular Automated Platform for Optically Stimulated Luminescence Measurements.

Eren Cihan Karsu Asal, Mehmet Taştan, Hayrettin Gökozan

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Eren Cihan Karsu AsalDepartment of Electric, Manisa Celal Bayar University, Manisa 45030, Turkey.ORCID 0000-0001-6392-2668
Mehmet TaştanDepartment of Electronics and Automation, Manisa Celal Bayar University, Manisa 45030, Turkey.ORCID 0000-0003-3712-9433
Hayrettin GökozanDepartment of Electric, Manisa Celal Bayar University, Manisa 45030, Turkey.ORCID 0000-0002-8917-4001

Funding

European Union 2023-1-RO01-KA220-HED-000159985
6 · The paper itself

Abstract

Optically stimulated luminescence (OSL) is widely used in radiation dosimetry and luminescence research, creating a continuing need for flexible measurement systems capable of supporting diverse experimental protocols. In this study, LuminaOSL was designed and developed as a modular, automated, computer-controlled research platform for implementing different OSL measurement protocols. The platform integrates programmable LED-based optical stimulation, photomultiplier tube (PMT)-based photon counting, an automated eight-position sample mechanism, microcontroller-based control, and a Python-based user interface within a unified architecture. System performance was evaluated through repeated measurements using beryllium oxide (BeO), low-signal control tests, and CW-OSL and LM-OSL experiments. A cycle-to-cycle coefficient of variation of 0.41% was obtained over successive measurement cycles, with no missing data observed during data acquisition and transfer. BeO measurements yielded monotonically decreasing CW-OSL responses under constant optical stimulation and peak-shaped LM-OSL responses under linearly increasing optical stimulation. These findings indicate that LuminaOSL can support stable and reproducible measurements through the integration of programmable optical stimulation, automated sample handling, and time-controlled photon counting, with potential applicability to radiation dosimetry and smart healthcare engineering research.

Indexed as

BeOCW-OSLLM-OSLOSL readerphoton countingsmart healthcare engineering

Identifiers

PMID42817492
PMCPMC13611056

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.