Particulate Science and Technology

Papers
(The H4-Index of Particulate Science and Technology is 13. The table below lists those papers that are above that threshold based on CrossRef citation counts [max. 250 papers]. The publications cover those that have been published in the past four years, i.e., from 2022-06-01 to 2026-06-01.)
ArticleCitations
The effect of physical properties of pond ash as a partial replacement of sand in the mortar mix37
Unreacted shrinking core model for non-catalytic ammonia storage in metal amine particle33
Economic particulate transport performance analysis of k-epsilon models in highly concentrated slurry through pipelines25
Numerical investigation of long-distance pneumatic conveying of feed pellets using CFD–DEM22
Geotechnical studies on cemented and untreated Behshahr loess and Amol clay of Iran19
Investigation of catalytic efficiency of nanoparticles of alloys on thermal behavior of AP and AP-based solid propellants19
Robot path planning for unstructured bulk solids cleaning within a ship cabin18
Numerical modelling and experimental validation of an Enhanced multi-force model of free-fall electrostatic separators17
Electrostatic separation process of metal/plastic granular mixtures using a horizontal rotating disk16
Thermal performance of nanofluids in elliptical zigzag tube: a numerical approach15
Silver nanoparticle production mediated by Goniothalamus wightii extract: characterization and their potential biological applications14
Revisiting the enhanced performance in coal gasification fly ash coal flotation by combined grinding process and nano-mixed collectors14
Segregation of binary particles in pulsed gas-solid fluidized bed13
Impact of titanium and silicon metal oxide nanoparticles on surface coatings for marine vehicles to mitigate biofouling13
Effect of charge on the concentration decay and size growth of PAO aerosol in a closed chamber in different charged conditions13
Experimental study of the collection efficiency of three configurations of blades-plates-type electrostatic precipitators13
Prediction of shear strength for granular material under the effect of liquid-powder binder using a PSO–RBF neural network model13
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