Evidence map›Paper›PMID 41402536›Full record

ArticleScientific reports2025

Demonstration of particulate matter characterization from HVO-blended diesel using an integrated multi-instrument approach.

Szabolcs Hodovány, Cheng Tung Chong, Viktor Józsa, Muhammad Qasim Mehmood, Jichuan Xiong, Gábor Szabó, Zoltán Bozóki, Abdul Rahman, Tibor Ajtai

Abstract read
In one paragraph

Article in Scientific reports, 2025. 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

9 authors.

Szabolcs HodoványDepartment of Optics and Quantum Electronics, University of Szeged, Dóm Ter 9, Szeged, 6720, Hungary.
Cheng Tung ChongChina-UK Low Carbon College, Shanghai Jiao Tong University, No. 3, Yinlian Rd, Lingang, Shanghai, 201306, China.
Viktor JózsaDepartment of Energy Engineering, Faculty of Mechanical Engineering, Budapest University of Technology and Economics, Műegyetem Rkp. 3., Budapest, 1111, Hungary.
Muhammad Qasim MehmoodDepartment of Electrical Engineering, Information Technology University of Punjab (ITU), Lahore, 54000, Pakistan.
Jichuan XiongSchool of Electronic and Optical Engineering, Nanjing University of Science and Technology, 200 Xiaolingwei Street, Nanjing, 210094, Jiangsu, China.
Gábor SzabóDepartment of Optics and Quantum Electronics, University of Szeged, Dóm Ter 9, Szeged, 6720, Hungary.
Zoltán BozókiDepartment of Optics and Quantum Electronics, University of Szeged, Dóm Ter 9, Szeged, 6720, Hungary.
Abdul RahmanDepartment of Optics and Quantum Electronics, University of Szeged, Dóm Ter 9, Szeged, 6720, Hungary. rahman.abdul@szte.hu.
Tibor AjtaiDepartment of Optics and Quantum Electronics, University of Szeged, Dóm Ter 9, Szeged, 6720, Hungary.

Funding

Magyarország Kormánya ÉZFF/956/2022-ITM_SZERZNemzeti Kutatási Fejlesztési és Innovációs Hivatal 2022-2.1.1-NL-2022-00012
6 · The paper itself

Abstract

We demonstrate the methodology to investigate particulate matter (PM) emissions from a 7 V/V% hydrotreated vegetable oil-diesel fuel blend (HVO7) compared to conventional diesel (B0) using a state-of-the-art integrated real-time measurement system. The methodology combines photoacoustic spectroscopy (using our custom photoacoustic (PA) instrument incorporating deep UV wavelengths, which is crucial for characterizing the organic matter content of exhaust PM), particle sizing, and thermal treatment to provide comprehensive, in situ characterization of PM across different engine loads and temperatures. We present size distribution and spectral measurements coupled with a thermodenuder that enables volatility-based classification of PM, allowing evaluation of spectral responses with respect to particle size variations and the dynamic black carbon (BC) to organic matter (OM) ratio in exhaust emissions. Our results show that HVO7 reduces particle number concentrations compared to B0, particularly under low-load conditions; however, these reductions gradually diminish at higher engine loads. Spectral measurements show that the HVO-blended fuel produces less black carbon than B0 at all engine loads and lower TD temperatures, with HVO7 exhibiting higher sensitivity to engine operating conditions than B0. The proposed integrated methodology offers a robust framework for real-time PM analysis of fuel emission, and the results demonstrate the potential of HVO blends to mitigate PM emissions, contributing to sustainable fuel development and emission control strategies.

Identifiers

PMID41402536
PMCPMC12808684

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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.