Chad V. Mashuga

1.3k total citations
46 papers, 1.1k citations indexed

About

Chad V. Mashuga is a scholar working on Aerospace Engineering, Safety, Risk, Reliability and Quality and Statistics, Probability and Uncertainty. According to data from OpenAlex, Chad V. Mashuga has authored 46 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 36 papers in Aerospace Engineering, 18 papers in Safety, Risk, Reliability and Quality and 14 papers in Statistics, Probability and Uncertainty. Recurrent topics in Chad V. Mashuga's work include Combustion and Detonation Processes (35 papers), Fire dynamics and safety research (18 papers) and Risk and Safety Analysis (14 papers). Chad V. Mashuga is often cited by papers focused on Combustion and Detonation Processes (35 papers), Fire dynamics and safety research (18 papers) and Risk and Safety Analysis (14 papers). Chad V. Mashuga collaborates with scholars based in United States, Greece and China. Chad V. Mashuga's co-authors include Daniel A. Crowl, M. Sam Mannan, Noor Quddus, Shuai Yuan, Andrés Mejı́a, Remi Trottier, Yue Sun, Ashok G. Dastidar, Zeren Jiao and Waruna D. Kulatilaka and has published in prestigious journals such as Journal of Hazardous Materials, Chemical Engineering Journal and Fuel.

In The Last Decade

Chad V. Mashuga

45 papers receiving 1.1k citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Chad V. Mashuga United States 20 751 399 341 336 176 46 1.1k
Yonghao Zhou China 18 866 1.2× 484 1.2× 389 1.1× 185 0.6× 82 0.5× 37 1.2k
Ajay V. Singh India 16 350 0.5× 445 1.1× 148 0.4× 78 0.2× 69 0.4× 60 820
Shuai Yuan China 21 184 0.2× 134 0.3× 124 0.4× 144 0.4× 99 0.6× 65 944
Luc Véchot Qatar 17 219 0.3× 91 0.2× 143 0.4× 85 0.3× 172 1.0× 52 663
Frédéric Heymes France 19 279 0.4× 97 0.2× 222 0.7× 56 0.2× 134 0.8× 36 714
Gianmaria Pio Italy 18 350 0.5× 173 0.4× 152 0.4× 40 0.1× 185 1.1× 64 781
Zhe Liang China 17 279 0.4× 147 0.4× 88 0.3× 83 0.2× 208 1.2× 73 958
Xinyu Zhao United States 22 330 0.4× 339 0.8× 78 0.2× 91 0.3× 377 2.1× 81 1.5k
Song Guo China 13 217 0.3× 169 0.4× 61 0.2× 101 0.3× 267 1.5× 28 635

Countries citing papers authored by Chad V. Mashuga

Since Specialization
Citations

This map shows the geographic impact of Chad V. Mashuga's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Chad V. Mashuga with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Chad V. Mashuga more than expected).

Fields of papers citing papers by Chad V. Mashuga

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Chad V. Mashuga. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Chad V. Mashuga. The network helps show where Chad V. Mashuga may publish in the future.

Co-authorship network of co-authors of Chad V. Mashuga

This figure shows the co-authorship network connecting the top 25 collaborators of Chad V. Mashuga. A scholar is included among the top collaborators of Chad V. Mashuga based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Chad V. Mashuga. Chad V. Mashuga is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Mashuga, Chad V., et al.. (2024). Functional group analysis and machine learning techniques for MIE prediction. Journal of Loss Prevention in the Process Industries. 89. 105289–105289.
2.
Mashuga, Chad V., et al.. (2022). Reconsidering autoignition in the context of modern process safety: Literature review and experimental analysis. Journal of Loss Prevention in the Process Industries. 81. 104963–104963. 2 indexed citations
3.
Kolis, Stanley P., et al.. (2022). Minimum ignition energy of amino acids and their Fmocs. Journal of Loss Prevention in the Process Industries. 77. 104763–104763. 3 indexed citations
4.
Mashuga, Chad V., et al.. (2022). Characterizing the autoignition behavior of simple paraffins and alcohols; comparisons and implications. Journal of Loss Prevention in the Process Industries. 77. 104773–104773. 4 indexed citations
5.
Schweizer, Christian, Chad V. Mashuga, & Waruna D. Kulatilaka. (2022). Investigation of aluminum dust cloud dispersion characteristics in an explosion hazard testing device using laser-based particle and flow diagnostics. Process Safety and Environmental Protection. 166. 310–319. 5 indexed citations
6.
Pérez, Lisa M., et al.. (2020). Minimum Ignition Energy (MIE) prediction models for ignition sensitive fuels using machine learning methods. Journal of Loss Prevention in the Process Industries. 69. 104343–104343. 15 indexed citations
7.
Sun, Yue, Lei Ni, Maria Papadaki, et al.. (2020). Reaction hazard and mechanism study of H2O2 oxidation of 2-butanol to methyl ethyl ketone using DSC, Phi-TEC II and GC-MS. Journal of Loss Prevention in the Process Industries. 66. 104177–104177. 17 indexed citations
8.
Papadaki, Maria, et al.. (2019). Calorimetric Studies on the Thermal Stability of 2-Nitrotoluene Explosives with Incompatible Substances. Industrial & Engineering Chemistry Research. 58(29). 13366–13375. 4 indexed citations
9.
Yuan, Shuai, et al.. (2019). Liquid flammability ratings predicted by machine learning considering aerosolization. Journal of Hazardous Materials. 386. 121640–121640. 24 indexed citations
10.
Yuan, Shuai, et al.. (2019). Developing Quantitative Structure–Property Relationship Models To Predict the Upper Flammability Limit Using Machine Learning. Industrial & Engineering Chemistry Research. 58(8). 3531–3537. 55 indexed citations
11.
Yuan, Shuai, et al.. (2019). Multi-functional SERS substrate: Collection, separation, and identification of airborne chemical powders on a single device. Sensors and Actuators B Chemical. 297. 126765–126765. 10 indexed citations
12.
Quddus, Noor, et al.. (2019). Bayesian network and game theory risk assessment model for third-party damage to oil and gas pipelines. Process Safety and Environmental Protection. 134. 178–188. 85 indexed citations
13.
Li, Qiang, et al.. (2018). Classification of particle breakage due to dust dispersion. Powder Technology. 342. 204–213. 23 indexed citations
14.
Zhang, Jiaqi, et al.. (2017). Carbon nanofiber explosion violence and thermal stability. Journal of Thermal Analysis and Calorimetry. 129(1). 221–231. 2 indexed citations
15.
Zhang, Jiaqi, et al.. (2016). Effect of dust dispersion on particle integrity and explosion hazards. Journal of Loss Prevention in the Process Industries. 44. 424–432. 23 indexed citations
16.
Jiang, Jiaojun, Yi Liu, Chad V. Mashuga, & M. Sam Mannan. (2015). Validation of a new formula for predicting the lower flammability limit of hybrid mixtures. Journal of Loss Prevention in the Process Industries. 35. 52–58. 24 indexed citations
17.
Zhang, Jiaqi, et al.. (2015). Dust Explosion of Carbon Nanofibers Promoted by Iron Nanoparticles. Industrial & Engineering Chemistry Research. 54(15). 3989–3995. 9 indexed citations
18.
Mashuga, Chad V. & Daniel A. Crowl. (2000). Derivation of Le Chatelier's mixing rule for flammable limits. Process Safety Progress. 19(2). 112–117. 83 indexed citations
19.
Mashuga, Chad V.. (1999). Determination of the combustion behavior for pure components and mixtures using a 20-liter sphere. PhDT. 12 indexed citations
20.
Mashuga, Chad V. & Daniel A. Crowl. (1999). Flammability zone prediction using calculated adiabatic flame temperatures. Process Safety Progress. 18(3). 127–134. 72 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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2026