Evidence map›Paper›PMID 42813469›Full record

ArticleFaraday discussions2026

Optimisation of measurement and data analysis of time-resolved X-ray absorption spectroscopy of thin film organic semiconductors.

Belen Alonso-Garcia, Miguel M Manresa-Nalda, Oliver G Alexander, Kaiyu Hu, Felix S Egun, Zachery Donnellan, Joseph J Broughton, Jacob P Lee, David J Hoffman, Kristjan Kunnus and 5 more

Abstract read
In one paragraph

Article in Faraday discussions, 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

15 authors.

Belen Alonso-GarciaBlackett Laboratory, Prince Consort Road, London, SW7 2BW, UK. m.matthews@imperial.ac.uk.ORCID http://orcid.org/0009-0007-0817-7248
Miguel M Manresa-NaldaBlackett Laboratory, Prince Consort Road, London, SW7 2BW, UK. m.matthews@imperial.ac.uk.ORCID http://orcid.org/0009-0008-6284-1397
Oliver G AlexanderBlackett Laboratory, Prince Consort Road, London, SW7 2BW, UK. m.matthews@imperial.ac.uk.ORCID http://orcid.org/0000-0001-8221-7382
Kaiyu HuBlackett Laboratory, Prince Consort Road, London, SW7 2BW, UK. m.matthews@imperial.ac.uk.ORCID http://orcid.org/0000-0001-6304-4885
Felix S EgunBlackett Laboratory, Prince Consort Road, London, SW7 2BW, UK. m.matthews@imperial.ac.uk.ORCID http://orcid.org/0009-0008-9559-4281
Zachery DonnellanChemical Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720, USA.ORCID http://orcid.org/0000-0002-8707-3012
Joseph J BroughtonBlackett Laboratory, Prince Consort Road, London, SW7 2BW, UK. m.matthews@imperial.ac.uk.ORCID http://orcid.org/0000-0002-1468-0962
Jacob P LeeBlackett Laboratory, Prince Consort Road, London, SW7 2BW, UK. m.matthews@imperial.ac.uk.ORCID http://orcid.org/0009-0009-7187-5625
David J HoffmanSLAC National Accelerator Laboratory, Menlo Park, CA, USA.ORCID http://orcid.org/0000-0001-8518-7676
Kristjan KunnusSLAC National Accelerator Laboratory, Menlo Park, CA, USA.ORCID http://orcid.org/0000-0002-9777-2685
Matt BainSLAC National Accelerator Laboratory, Menlo Park, CA, USA.ORCID http://orcid.org/0000-0001-9779-2021
Douglas GarrattSLAC National Accelerator Laboratory, Menlo Park, CA, USA.ORCID http://orcid.org/0000-0002-8637-9984
Oliver GessnerChemical Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720, USA.ORCID http://orcid.org/0000-0003-4709-2822
Mary R MatthewsBlackett Laboratory, Prince Consort Road, London, SW7 2BW, UK. m.matthews@imperial.ac.uk.ORCID http://orcid.org/0000-0003-3683-1749
Jon P MarangosBlackett Laboratory, Prince Consort Road, London, SW7 2BW, UK. m.matthews@imperial.ac.uk.ORCID http://orcid.org/0000-0001-7169-6955

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Time-resolved X-ray absorption spectroscopy (TR-XAS) at X-ray free-electron lasers enables element-specific probing of ultrafast dynamics, but soft X-ray measurements are often limited by low fluorescence yield, strong shot-to-shot fluctuations, and radiation damage in organic thin films. Here, we present a unified framework combining analytical signal-to-noise ratio (SNR) modelling with regression-based analysis for fluorescence-detected soft X-ray absorption spectroscopy. The SNR model predicts fundamentally different optimal conditions for static and transient measurements, with transient experiments requiring thin films (∼80-100 nm) and substantially higher photon counts. We apply this framework to shot-resolved total fluorescence yield XAS measurements of the non-fullerene acceptor Y6 at the nitrogen K-edge using a high-repetition-rate XFEL source, comparing low-dose and intermediate-dose regimes relevant to transient and static measurements, respectively. A calibration-based flux reconstruction systematically improves the fluorescence-flux correlation, while the dominant improvement arises from reformulating XAS extraction as a shot-resolved regression problem. Compared to the conventional ratio-of-means estimator, the regression-based estimator reduces the run-to-run spectral variability by up to a factor of ∼4.4 in the low-dose regime most relevant for transient measurements. Residual-based outlier rejection was found to systematically degrade reproducibility, indicating that the tails of the shot distribution primarily reflect intrinsic XFEL fluctuations rather than detector artefacts. These results demonstrate that reliable soft X-ray TR-XAS requires the joint optimisation of experimental design and statistical inference, providing a practical framework for extracting robust observables from low-signal XFEL measurements in dose-limited regimes.

Identifiers

PMID42813469
PMCPMC13625176

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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.