Thaseem Thajudeen
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Previous Organizations / Institutes
The organizations / institutes from the scholar's other articles in the past ten years
Research Subjects
Thaseem Thajudeen has a total of 54 co-authors. Here are the top co-authors by number of shared publications:
- Christopher Hogan
University of Minnesota
11 shared publications - Wolfgang Peukert
Institute of Particle Technology (LFG), Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Cauerstraße 4, 91058 Erlangen, Germany
9 shared publications - Johannes Walter
Institute of Particle Technology (LFG), Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Cauerstraße 4, 91058 Erlangen, Germany
7 shared publications Abhishek Singh
4 shared publications- Anirudha Ambekar
School of Mechanical Sciences Indian Institute of Technology Goa Ponda India
4 shared publications Ranganathan Gopalakrishnan
3 shared publications
Latest Publications
- Open Access
- 13 March 2026
- Published by Royal Society of Chemistry (RSC) in RSC Advances
- Vol. 16 (16) , 14350-14360
Effect of oxygenated ferrocene derivatives on soot formation and nanoparticle emissions in n -heptane diffusion flames
This study reports an experimental investigation of soot formation in laminar diffusion flames of n-heptane doped with organometallic additives, including ferrocene, ferrocene methanol, and ferrocene carboxaldehyde, at concentrations of 100 and 500 ppm. Measurements of flame temperature, flame Soot Volume Fraction (SVF), and aerosol concentration in the flame plume were obtained. The addition of organometallics lowered the SVF compared to pure n-heptane. The oxygenated ferrocene derivatives were found to be significantly more effective at suppressing soot compared to pure ferrocene. At 100 ppm, ferrocene methanol and ferrocene carboxaldehyde reduced peak SVF by 24% and 22%, compared to only 8% for ferrocene. Increasing the concentration to 500 ppm provided only a marginal additional improvement. However, the measurements of aerosol concentration in the flame plume showed ultrafine particulate emissions for additive-doped flames. These emissions were attributed to iron-containing nanoparticles. Total Number Concentration (TNC) of particulate emissions was comparable for ferrocene and ferrocene carboxaldehyde, while ferrocene methanol showed a marginally lower value. The study demonstrates that organometallic additives reduce soot concentration within the flame while increasing nanoparticle emissions. This highlights a trade-off between soot suppression and nanoparticle emissions in the context of the selection and use of such additives. Furthermore, the study highlights the advantage of combined diagnostics, including aerosol measurement, over simple in-flame soot measurement to assess the overall emissions.
- 13 March 2026
- Published by Royal Society of Chemistry (RSC) in RSC Advances
- Vol. 16 (16) , 14350-14360
Open AccessEffect of oxygenated ferrocene derivatives on soot formation and nanoparticle emissions in n -heptane diffusion flames
This study reports an experimental investigation of soot formation in laminar diffusion flames of n-heptane doped with organometallic additives, including ferrocene, ferrocene methanol, and ferrocene carboxaldehyde, at concentrations of 100 and 500 ppm. Measurements of flame temperature, flame Soot Volume Fraction (SVF), and aerosol concentration in the flame plume were obtained. The addition of organometallics lowered the SVF compared to pure n-heptane. The oxygenated ferrocene derivatives were found to be significantly more effective at suppressing soot compared to pure ferrocene. At 100 ppm, ferrocene methanol and ferrocene carboxaldehyde reduced peak SVF by 24% and 22%, compared to only 8% for ferrocene. Increasing the concentration to 500 ppm provided only a marginal additional improvement. However, the measurements of aerosol concentration in the flame plume showed ultrafine particulate emissions for additive-doped flames. These emissions were attributed to iron-containing nanoparticles. Total Number Concentration (TNC) of particulate emissions was comparable for ferrocene and ferrocene carboxaldehyde, while ferrocene methanol showed a marginally lower value. The study demonstrates that organometallic additives reduce soot concentration within the flame while increasing nanoparticle emissions. This highlights a trade-off between soot suppression and nanoparticle emissions in the context of the selection and use of such additives. Furthermore, the study highlights the advantage of combined diagnostics, including aerosol measurement, over simple in-flame soot measurement to assess the overall emissions.
- Top Cited Papers
- 13 January 2026
- Published by Royal Society of Chemistry (RSC) in Nanoscale
- Vol. 18 (9) , 4622-4660
Advances in nanostructured chemiresistive sensors for formaldehyde detection
Formaldehyde (HCHO) is deleterious both as an indoor volatile organic compound, contributing to respiratory and carcinogenic risks, and as an adulterant in food products such as fish, where ingestion poses severe systemic toxicity. Sub-ppm monitoring is essential, given that exposure limits are ≤0.1 ppm and adverse health effects emerge at 50-100 ppb. Chemiresistive sensors address this demand through adsorption-driven modulation of surface depletion layers and charge-transfer dynamics, thereby enabling real-time, ultrasensitive detection. Owing to their architectural simplicity, miniaturisation potential, and cost-effectiveness, these sensors have emerged as a dominant platform for HCHO detection with detection limits in few ppbs, response times of 5-60 seconds and selectivity factors typically 5 times over interfering VOCs under optimised conditions. This review highlights recent progress in nanostructured materials as sensing layers of chemiresistive sensors including single and binary metal oxides, two-dimensional nanomaterials, conjugated polymers, and their heterostructural hybrids, emphasizing how surface chemistry, defect modulation, and heterointerface engineering enhance sensitivity(104-106 %/ppm), selectivity, operating temperature(from 250-350 °C to 80-150 °C), humidity resistance and detection limits. In addition, critical assessment of morphology modification (0D-3D architecture) and the role of noble-metal decoration in modulating surface charge transfer and accelerating adsorption-desorption kinetics typically shows reduction of response/recovery cycles by 20-60%. Emerging strategies such as light activation, plasma treatment, vacancy engineering, and gas treatments to enhance sensor metrics have been studied for their role in stabilizing sensor performance in humid and variable environments. The integration of machine learning (ML) is also discussed as a complementary approach to improve selectivity(>90% class accuracy) and optimizing feature extraction, and enabling predictive modelling of sensor behaviour across varying temperatures, humidities, and interfering gases. Finally, the review outlines current challenges and envisages future opportunities toward scalable, humidity-resilient, and intelligent chemiresistive platforms for next-generation HCHO monitoring. By uniting nanoscale material design with intelligent data analytics, next-generation chemiresistive sensors are poised to transition from laboratory prototypes to robust, scalable technologies that address urgent societal needs in environmental monitoring and public health.
- 13 January 2026
- Published by Royal Society of Chemistry (RSC) in Nanoscale
- Vol. 18 (9) , 4622-4660
Top Cited PapersAdvances in nanostructured chemiresistive sensors for formaldehyde detection
Formaldehyde (HCHO) is deleterious both as an indoor volatile organic compound, contributing to respiratory and carcinogenic risks, and as an adulterant in food products such as fish, where ingestion poses severe systemic toxicity. Sub-ppm monitoring is essential, given that exposure limits are ≤0.1 ppm and adverse health effects emerge at 50-100 ppb. Chemiresistive sensors address this demand through adsorption-driven modulation of surface depletion layers and charge-transfer dynamics, thereby enabling real-time, ultrasensitive detection. Owing to their architectural simplicity, miniaturisation potential, and cost-effectiveness, these sensors have emerged as a dominant platform for HCHO detection with detection limits in few ppbs, response times of 5-60 seconds and selectivity factors typically 5 times over interfering VOCs under optimised conditions. This review highlights recent progress in nanostructured materials as sensing layers of chemiresistive sensors including single and binary metal oxides, two-dimensional nanomaterials, conjugated polymers, and their heterostructural hybrids, emphasizing how surface chemistry, defect modulation, and heterointerface engineering enhance sensitivity(104-106 %/ppm), selectivity, operating temperature(from 250-350 °C to 80-150 °C), humidity resistance and detection limits. In addition, critical assessment of morphology modification (0D-3D architecture) and the role of noble-metal decoration in modulating surface charge transfer and accelerating adsorption-desorption kinetics typically shows reduction of response/recovery cycles by 20-60%. Emerging strategies such as light activation, plasma treatment, vacancy engineering, and gas treatments to enhance sensor metrics have been studied for their role in stabilizing sensor performance in humid and variable environments. The integration of machine learning (ML) is also discussed as a complementary approach to improve selectivity(>90% class accuracy) and optimizing feature extraction, and enabling predictive modelling of sensor behaviour across varying temperatures, humidities, and interfering gases. Finally, the review outlines current challenges and envisages future opportunities toward scalable, humidity-resilient, and intelligent chemiresistive platforms for next-generation HCHO monitoring. By uniting nanoscale material design with intelligent data analytics, next-generation chemiresistive sensors are poised to transition from laboratory prototypes to robust, scalable technologies that address urgent societal needs in environmental monitoring and public health.
- Open Access
- 23 October 2025
- Published by Royal Society of Chemistry (RSC) in Environmental Science: Advances
- Vol. 5 (1) , 59-77
Occupational health risks from welding emissions: exposure and deposition of PM 10 , PM 2.5 , and ultrafine particles across welding methods
In SMAW, an increase in current leads to higher UFP and PM deposition. Excess cancer risk from SMAW and WAAM exceeds the USEPA limit by ∼100×. Existing guidelines are inadequate, stressing the urgent need for UFP-specific regulations.
- 23 October 2025
- Published by Royal Society of Chemistry (RSC) in Environmental Science: Advances
- Vol. 5 (1) , 59-77
Open AccessOccupational health risks from welding emissions: exposure and deposition of PM 10 , PM 2.5 , and ultrafine particles across welding methods
In SMAW, an increase in current leads to higher UFP and PM deposition. Excess cancer risk from SMAW and WAAM exceeds the USEPA limit by ∼100×. Existing guidelines are inadequate, stressing the urgent need for UFP-specific regulations.
- 21 July 2025
- Published by Elsevier in Atmospheric Pollution Research
- Vol. 16 (12)
Comprehensive analysis of the fine and ultrafine particulate emissions from various welding sources in an industrial environment
- 21 July 2025
- Published by Elsevier in Atmospheric Pollution Research
- Vol. 16 (12)
Comprehensive analysis of the fine and ultrafine particulate emissions from various welding sources in an industrial environment
- 3 March 2025
- Published by Elsevier in Journal of Aerosol Science
- Vol. 186
Multi-objective Bayesian optimization for the retrieval of aggregated aerosol structures from microscopic images
- 3 March 2025
- Published by Elsevier in Journal of Aerosol Science
- Vol. 186
Multi-objective Bayesian optimization for the retrieval of aggregated aerosol structures from microscopic images
- 9 May 2024
- Published by Elsevier in Journal of Aerosol Science
- Vol. 180
Machine learning based tool for the efficient estimation of geometric features of aggregated aerosol particles
- 9 May 2024
- Published by Elsevier in Journal of Aerosol Science
- Vol. 180
Machine learning based tool for the efficient estimation of geometric features of aggregated aerosol particles
- 29 December 2023
- Published by Elsevier in Journal of Aerosol Science
- Vol. 177
ML-assisted optimization method for the prediction of 3-dimensional fractal structures from microscopic images
- 29 December 2023
- Published by Elsevier in Journal of Aerosol Science
- Vol. 177
ML-assisted optimization method for the prediction of 3-dimensional fractal structures from microscopic images