Matches in SemOpenAlex for { <https://semopenalex.org/work/W2885222561> ?p ?o ?g. }
- W2885222561 endingPage "56" @default.
- W2885222561 startingPage "45" @default.
- W2885222561 abstract "Purpose The importance of maximizing the particle packing fraction in a suspension by maximizing average particle size ratio of D 5 /D 1 has been adequately shown to be important as previously reported in the literature. This study aims to extend that analysis to include the best formulation approach to maximize the packing fraction with a minimum number of monodisperse particle sizes. Design/methodology/approach An existing model previously developed by this author was modified theoretically to optimize the ratio used between consecutive monodisperse particle sizes. This process was found to apply to a broad range of particle configurations and applications. In addition, five different approaches for maximizing average particle size ratio D̅ 5 /D̅ 1 were addressed for blending several different particle size distributions. Maximizing average particle size ratio D̅ 5 /D̅ 1 has been found to result in an optimization of the packing fraction. Several new concepts were also introduced in the process of maximizing the packing fraction for these different approaches. Findings The critical part of the analysis to maximize the packing fraction with a minimum number of particles was the theoretical optimization of the ratio used between consecutive monodisperse particle sizes. This analysis was also found to be effectively independent of the maximum starting particle size. This study also clarified the recent incorrect claim in the literature that Furnas in 1931 was the first to generate the maximum theoretical packing fraction possible for n different particles that was actually originally developed in conjunction with the Sudduth generalized viscosity equation. In addition, the Furnas generated equation was also shown to give significantly different results from the Sudduth generated equation. Research limitations/implications Experimental data involving monodisperse particles of different blends with a minimum number of particle sizes that are truly monodisperse are often extremely difficult to obtain. However, the theoretical general concepts can still be applicable. Practical implications The expanded model presented in this article provides practical guidelines for blending pigments using a minimum number of monodisperse particle sizes that can yield much higher ratios of the particle size averages D̅ 5 /D̅ 1 and thus potentially achieve significantly improved properties such as viscosity. Originality/value The model presented in this article provides the first apparent guidelines to control the blending of pigments in coatings by the optimization of the ratio used between consecutive monodisperse particle sizes. This analysis was also found to be effectively independent of the maximum starting particle size." @default.
- W2885222561 created "2018-08-22" @default.
- W2885222561 creator A5009339110 @default.
- W2885222561 date "2019-01-07" @default.
- W2885222561 modified "2023-09-23" @default.
- W2885222561 title "Optimum formulation derivation for the ultimate packing fraction using monodispersed particle sizes when optimizing suspension viscosities" @default.
- W2885222561 cites W1555050360 @default.
- W2885222561 cites W1793996632 @default.
- W2885222561 cites W1971668954 @default.
- W2885222561 cites W1975466860 @default.
- W2885222561 cites W2000302956 @default.
- W2885222561 cites W2001305537 @default.
- W2885222561 cites W2016550627 @default.
- W2885222561 cites W2019067149 @default.
- W2885222561 cites W2020619811 @default.
- W2885222561 cites W2035326765 @default.
- W2885222561 cites W2052640557 @default.
- W2885222561 cites W2055009642 @default.
- W2885222561 cites W2059841547 @default.
- W2885222561 cites W2069087598 @default.
- W2885222561 cites W2090235274 @default.
- W2885222561 cites W2140475560 @default.
- W2885222561 cites W2147769700 @default.
- W2885222561 cites W2168247269 @default.
- W2885222561 cites W2312555127 @default.
- W2885222561 cites W2330876482 @default.
- W2885222561 cites W2332683161 @default.
- W2885222561 cites W2520790534 @default.
- W2885222561 cites W2563502323 @default.
- W2885222561 cites W2605605130 @default.
- W2885222561 cites W3101666386 @default.
- W2885222561 doi "https://doi.org/10.1108/prt-01-2018-0006" @default.
- W2885222561 hasPublicationYear "2019" @default.
- W2885222561 type Work @default.
- W2885222561 sameAs 2885222561 @default.
- W2885222561 citedByCount "2" @default.
- W2885222561 countsByYear W28852225612021 @default.
- W2885222561 countsByYear W28852225612023 @default.
- W2885222561 crossrefType "journal-article" @default.
- W2885222561 hasAuthorship W2885222561A5009339110 @default.
- W2885222561 hasConcept C105341887 @default.
- W2885222561 hasConcept C110161667 @default.
- W2885222561 hasConcept C111368507 @default.
- W2885222561 hasConcept C11432220 @default.
- W2885222561 hasConcept C121332964 @default.
- W2885222561 hasConcept C121864883 @default.
- W2885222561 hasConcept C126255220 @default.
- W2885222561 hasConcept C127313418 @default.
- W2885222561 hasConcept C127413603 @default.
- W2885222561 hasConcept C149629883 @default.
- W2885222561 hasConcept C159985019 @default.
- W2885222561 hasConcept C185592680 @default.
- W2885222561 hasConcept C187530423 @default.
- W2885222561 hasConcept C188027245 @default.
- W2885222561 hasConcept C192562407 @default.
- W2885222561 hasConcept C202444582 @default.
- W2885222561 hasConcept C204323151 @default.
- W2885222561 hasConcept C2778517922 @default.
- W2885222561 hasConcept C33923547 @default.
- W2885222561 hasConcept C42360764 @default.
- W2885222561 hasConcept C43617362 @default.
- W2885222561 hasConcept C46141821 @default.
- W2885222561 hasConcept C5961521 @default.
- W2885222561 hasConcept C82558694 @default.
- W2885222561 hasConceptScore W2885222561C105341887 @default.
- W2885222561 hasConceptScore W2885222561C110161667 @default.
- W2885222561 hasConceptScore W2885222561C111368507 @default.
- W2885222561 hasConceptScore W2885222561C11432220 @default.
- W2885222561 hasConceptScore W2885222561C121332964 @default.
- W2885222561 hasConceptScore W2885222561C121864883 @default.
- W2885222561 hasConceptScore W2885222561C126255220 @default.
- W2885222561 hasConceptScore W2885222561C127313418 @default.
- W2885222561 hasConceptScore W2885222561C127413603 @default.
- W2885222561 hasConceptScore W2885222561C149629883 @default.
- W2885222561 hasConceptScore W2885222561C159985019 @default.
- W2885222561 hasConceptScore W2885222561C185592680 @default.
- W2885222561 hasConceptScore W2885222561C187530423 @default.
- W2885222561 hasConceptScore W2885222561C188027245 @default.
- W2885222561 hasConceptScore W2885222561C192562407 @default.
- W2885222561 hasConceptScore W2885222561C202444582 @default.
- W2885222561 hasConceptScore W2885222561C204323151 @default.
- W2885222561 hasConceptScore W2885222561C2778517922 @default.
- W2885222561 hasConceptScore W2885222561C33923547 @default.
- W2885222561 hasConceptScore W2885222561C42360764 @default.
- W2885222561 hasConceptScore W2885222561C43617362 @default.
- W2885222561 hasConceptScore W2885222561C46141821 @default.
- W2885222561 hasConceptScore W2885222561C5961521 @default.
- W2885222561 hasConceptScore W2885222561C82558694 @default.
- W2885222561 hasIssue "1" @default.
- W2885222561 hasLocation W28852225611 @default.
- W2885222561 hasOpenAccess W2885222561 @default.
- W2885222561 hasPrimaryLocation W28852225611 @default.
- W2885222561 hasRelatedWork W1979336952 @default.
- W2885222561 hasRelatedWork W1995576254 @default.
- W2885222561 hasRelatedWork W2117727186 @default.
- W2885222561 hasRelatedWork W2168247269 @default.
- W2885222561 hasRelatedWork W2279666905 @default.
- W2885222561 hasRelatedWork W2329971239 @default.