Muhammad Shariq Vohra

Prof. Muhammad Shariq Vohra

King Fahd University of Petroleum and Minerals, Dhahran, Ash Sharqīyah, Saudi Arabia
orcid

Research Focus & Metrics

Academic Career Length

33Years (1994-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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1 August 2003 in Journal of Photochemistry and Photobiology A: Chemistry
Verified
Journal Article
Closed access
MVMuhammad Shariq Vohra
Muhammad Shariq Vohra
SKSoonhyun Kim
Soonhyun Kim
Wonyong ChoiWonyong Choi
Wonyong Choi


1 September 2003 in Water Research
Verified
Journal Article
Closed access
MVMuhammad Shariq Vohra
Muhammad Shariq Vohra
KTKeiichi Tanaka
Keiichi Tanaka

18 April 2023 in Nanomaterials
Verified
Journal Article
Open access
PDF file
UIUsman Muhammad Ismail
Usman Muhammad Ismail
Sagheer OnaiziSagheer Onaizi
Sagheer Onaizi
MVMuhammad Shariq Vohra
Muhammad Shariq Vohra
Zeolitic imidazolate frameworks (ZIFs) are increasingly gaining attention in many application fields due to their outstanding porosity and thermal stability, among other exceptional characteristics. However, in the domain of water purification via adsorption, scientists have mainly focused on ZIF-8 and, to a lesser extent, ZIF-67. The performance of other ZIFs as water decontaminants is yet to be explored. Hence, this study applied ZIF-60 for the removal of lead from aqueous solutions; this is the first time ZIF-60 has been used in any water treatment adsorption study. The synthesized ZIF-60 was subjected to characterization using FTIR, XRD and TGA. A multivariate approach was used to investigate the effect of adsorption parameters on lead removal and the findings revealed that ZIF-60 dose and lead concentration are the most significant factors affecting the response (i.e., lead removal efficiency). Further, response surface methodology-based regression models were generated. To further explore the adsorption performance of ZIF-60 in removing lead from contaminated water samples, adsorption kinetics, isotherm and thermodynamic investigations were conducted. The findings revealed that the obtained data were well-fitted by the Avrami and pseudo-first-order kinetic models, suggesting that the process is complex. The maximum adsorption capacity (qmax) was predicted to be 1905 mg/g. Thermodynamic studies revealed an endothermic and spontaneous adsorption process. Finally, the experimental data were aggregated and used for machine learning predictions using several algorithms. The model generated by the random forest algorithm proved to be the most effective on the basis of its significant correlation coefficient and minimal root mean square error (RMSE).

9 December 2000 in Environmental Science & Technology
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Journal Article
Closed access
MVMuhammad Shariq Vohra
Muhammad Shariq Vohra
KTK. Tanaka
K. Tanaka

1 October 1997 in Journal of Colloid and Interface Science
Verified
Journal Article
Closed access
MVMuhammad Shariq Vohra
Muhammad Shariq Vohra
ADAllen P. Davis
Allen P. Davis