Method of quantifying surface roughness for accurate adhesive force predictions

LaMarche, Casey Q.; Leadley, Stuart R.; Liu, Peiyuan; Kellogg, Kevin M.; Hrenya, Christine M.

doi:10.1016/j.ces.2016.09.024

Cited by 73 publications

(61 citation statements)

References 73 publications

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“…As illustrated by the schematic drawings in Table 1, Hamaker 17 computes the van der Waal force for smooth particles. [49][50][51] Since most particles handled in industry have some degree of roughness, the Rumpf and Rabinovich models, which explicitly consider surface roughness, [22][23][24]26 are given priority here in order to assess the validity of the square-force model in capturing the effects of roughness on cohesion. Rabinovich et al 23,24 treat surface asperities as a superposition of spherical caps with two scales exposing large and small wavelengths and root-mean-square (rms) amplitudes.…”

Section: Resultsmentioning

confidence: 99%

“…Although the cohesive force between two smooth spheres is well understood for both van der Waals cohesion 17 and capillary cohesion in pendular regime, 18 more realistic particles found in nature and industry are rough. 26 By resolving the Gibbs and "atomic-scale-noise" artifacts, a standard method is proposed to extract the roughness wavelengths and rms amplitudes for the Rabinovich et al cohesion model, 23,24 which resulted in strong agreement between model predictions and particle-particle force measurements. [19][20][21] Furthermore, such roughness is typically irregular, making its incorporation into DEM nontrivial.…”

Section: Introductionmentioning

confidence: 99%

“…We recently conducted AFM measurement on the surface of glass powders. 26 By resolving the Gibbs and "atomic-scale-noise" artifacts, a standard method is proposed to extract the roughness wavelengths and rms amplitudes for the Rabinovich et al cohesion model, 23,24 which resulted in strong agreement between model predictions and particle-particle force measurements. However, the geometry of surface roughness for arbitrary particles is difficult to generalize.…”

Section: Introductionmentioning

confidence: 99%

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A square‐force cohesion model and its extraction from bulk measurements

et al. 2018

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Accurate modeling of interparticle forces in DEM is critical to predicting the rheology of cohesive particles. Rigorous cohesion models usually include parameters associated with particle surface roughness. However, both roughness measurement and its distillation into appropriate model parameters remain challenging. We propose a square‐force cohesion model, where cohesive force remains constant until a cutoff separation, above which cohesion vanishes. We demonstrate the square‐force model is a valid surrogate of more rigorous models. Specifically, when two parameters of square‐force model are chosen to match the two key quantities governing dense and dilute flows, namely maximum cohesive force and critical cohesive energy, respectively, DEM results using square‐force and more rigorous models show good agreement. For practical application of the square‐force model to lightly cohesive systems, a method is established to extract its parameters via defluidization, enabling determination of particle–particle cohesion from simpler bulk measurements than complicated and expensive scans on individual grains. © 2018 American Institute of Chemical Engineers AIChE J, 64: 2329–2339, 2018

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A square‐force cohesion model and its extraction from bulk measurements

et al. 2018

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“…These were prepared immediately prior to experimental use. TNT, PETN, and RDX particles with nominal diameters of 5 mm d 15 mm were mounted onto a MLCT-10 (Bruker AFM Probes) cantilever using a method previously discussed [28]. The nominal spring constant of these cantilevers is 0.6 N m À1 .…”

Section: Materials and Experimental Proceduresmentioning

confidence: 99%

“…Additionally, several models have been developed to describe the impact of surface roughness on adhesion [20,[24][25][26][27]. Through these developments, precise descriptions of particle-substrate interactions allow for material properties to be extracted from CP-AFM measurements [24,28]. However, to describe the particle-substrate interaction in a precise manner is experimentally and computationally arduous [29,30].…”

Section: Introductionmentioning

confidence: 99%

Energetic Microparticle Adhesion to Functionalized Surfaces

Hoss

Mukherjee

Boudouris

et al. 2018

Propellants Explo Pyrotec

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Surface chemistry influences interfacial interactions, and while these interactions have been evaluated in many synthetic and biological systems, they have important but unexplored implications in trace explosives detection. Specifically, the detection of energetic materials is a challenging, urgent goal, and one of the most common means by which this effort is implemented at air transportation checkpoints is using methods based on contact sampling. Elucidating the molecular and interfacial interactions of energetic materials with functionalized surfaces provides fundamental knowledge and also advances the goal of improved materials for trace detection. Here, in order to evaluate the effects of specific functional groups on adhesion, atomic force microscopy (AFM) pull‐off force measurements were performed using nitrate‐based energetic (and non‐energetic) particles against self‐assembled monolayers (SAMs) of representative chemical functionalities. These SAMs‐on‐gold substrates were selected to evaluate surface chemistry effects due to their reproducibility, facile production, and versatile tunability. In addition to the experimental results, stabilization energies for the optimized most‐stable configurations for a coupled receptor‐analyte system were determined using density functional theory (DFT). From these combined experimental and computational efforts, it is established that the adhesion between detection surfaces and common energetic materials at the macroscopic scales is correlated to the interaction energies at the molecular level. Moreover, the electron deficient nature of nitro‐rich energetic compounds results in stronger interactions with surfaces functionalized with electron‐donating units. Ultimately, these results will facilitate the rational design of energetic particle collection materials through chemical tailoring in order to enhance the detection and defeat of explosive materials.

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AFM‐Based Hamaker Constant Determination with Blind Tip Reconstruction

Wetering

Bolten

et al. 2022

Adv Materials Technologies

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Particle contamination of extreme ultraviolet (EUV) photomasks is one of the numerous challenges in nanoscale semiconductor fabrication, since it can lead to systematic device failures when disturbed patterns are projected repeatedly onto wafers during EUV exposure.Understanding adhesion of particle contamination is key in devising a strategy for cleaning of photomasks. In this work, particle contamination is treated as a particle-plane problem in which surface roughness and the interacting materials have major influences. For this purpose, we perform vacuum atomic force microscopy (AFM) contact measurements to quantify the van der Waals (vdW) forces between tip and sample. We introduce this as a vacuum AFM-based methodology that combines numerical Hamaker theory and Blind Tip Reconstruction (BTR).We have determined the Hamaker constants of 15×10 -20 J and 13×10 -20 J for the material systems of a silicon (Si) tip with both aluminum oxide (Al2O3) and native silicon dioxide (SiO2) on Si substrates, respectively. Our methodology allows an alternative, quick and low-cost approach to characterize the Hamaker constant within the right order of magnitude for any material combination.

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Method of quantifying surface roughness for accurate adhesive force predictions

Cited by 73 publications

References 73 publications

A square‐force cohesion model and its extraction from bulk measurements

A square‐force cohesion model and its extraction from bulk measurements

Energetic Microparticle Adhesion to Functionalized Surfaces

AFM‐Based Hamaker Constant Determination with Blind Tip Reconstruction

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