Capillary Forces between Concave Gripper and Spherical Particle for Micro-Objects Gripping

Fan, Zenghua; Liu, Zixiao; Huang, Congcong; Zhang, Wei; Lv, Zijian; Wang, Lefeng

doi:10.3390/mi12030285

Cited by 7 publications

(4 citation statements)

References 31 publications

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“…In SE and theoretical models, the contact angles are set to a constant, while the contact angles are varied with the stretching process of the liquid bridge [40]. Additionally, the results of SE solutions are consistent well with theoretical solutions, as demonstrated in reference [41]. Predictions of the GA-ANN, gANN, SE and theoretical solutions in terms of contact diameter are plotted in Figure 9b.…”

Section: Comparison Of Ga-ann Gann Simulation and Theoretical Solutionssupporting

confidence: 71%

A Predictive Model of Capillary Forces and Contact Diameters between Two Plates Based on Artificial Neural Network

Huang

Fan

et al. 2023

Micromachines

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Many efforts have been devoted to the forecasting of the capillary force generated by capillary adsorption between solids, which is fundamental and essential in the fields of micro-object manipulation and particle wetting. In this paper, an artificial neural network (ANN) model optimized by a genetic algorithm (GA-ANN) was proposed to predict the capillary force and contact diameter of the liquid bridge between two plates. The mean square error (MSE) and correlation coefficient (R2) were employed to evaluate the prediction accuracy of the GA-ANN model, theoretical solution method of the Young–Laplace equation and simulation approach based on the minimum energy method. The results showed that the values of MSE of capillary force and contact diameter using GA-ANN were 10.3 and 0.0001, respectively. The values of R2 were 0.9989 and 0.9977 for capillary force and contact diameter in regression analysis, respectively, demonstrating the accuracy of the proposed predictive model. The sensitivity analysis was conducted to investigate the influence of input parameters, including liquid volume and separation distance, on the capillary force and contact diameter. The liquid volume and separation distance played dominant roles in affecting the capillary force and contact diameter.

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Section: Comparison Of Ga-ann Gann Simulation and Theoretical Solutionssupporting

confidence: 71%

A Predictive Model of Capillary Forces and Contact Diameters between Two Plates Based on Artificial Neural Network

Huang

Fan

et al. 2023

Micromachines

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“…Concave surfaces can exert strong capillary forces on microscale objects suspended in water 20 , 21 . We hypothesized that convoluted porous metal foams with hydrophobic surfaces could play a dual role of trapping MPs from solution to facilitate the omission of PCPT techniques while generating SPs to enable SERS.…”

Section: Resultsmentioning

confidence: 99%

Pretreatment-free SERS sensing of microplastics using a self-attention-based neural network on hierarchically porous Ag foams

Guselnikova,

Trelin,

Kang

et al. 2024

Nat Commun

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Low-cost detection systems are needed for the identification of microplastics (MPs) in environmental samples. However, their rapid identification is hindered by the need for complex isolation and pre-treatment methods. This study describes a comprehensive sensing platform to identify MPs in environmental samples without requiring independent separation or pre-treatment protocols. It leverages the physicochemical properties of macroporous-mesoporous silver (Ag) substrates templated with self-assembled polymeric micelles to concurrently separate and analyze multiple MP targets using surface-enhanced Raman spectroscopy (SERS). The hydrophobic layer on Ag aids in stabilizing the nanostructures in the environment and mitigates biofouling. To monitor complex samples with multiple MPs and to demultiplex numerous overlapping patterns, we develop a neural network (NN) algorithm called SpecATNet that employs a self-attention mechanism to resolve the complex dependencies and patterns in SERS data to identify six common types of MPs: polystyrene, polyethylene, polymethylmethacrylate, polytetrafluoroethylene, nylon, and polyethylene terephthalate. SpecATNet uses multi-label classification to analyze multi-component mixtures even in the presence of various interference agents. The combination of macroporous-mesoporous Ag substrates and self-attention-based NN technology holds potential to enable field monitoring of MPs by generating rich datasets that machines can interpret and analyze.

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“…The microgripper with a concave-shaped surface exhibited a larger capillary force than a plane, remarkably improving the ability of pickup, as demonstrated by Saito et al [24]. A static liquid bridge between a sphere and a spherical concave was studied by Fan et al [25]. Detailed discussion was conducted about the effects of radius ratio, liquid bridge volume, and contact angles on the capillary force.…”

Section: Introductionmentioning

confidence: 98%

Experimental investigations of liquid bridge rupture between a sphere and a spherical concave

Huang

Fan

Wang

et al. 2023

J. Phys. D: Appl. Phys.

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Liquid bridges formation and rupture between solid surfaces have widespread applications in micro gripping, self-alignment, particles wetting. In the present study, an investigation of the axisymmetric liquid bridge rupture between a sphere and a spherical concave was performed systematically. Detailed analysis was conducted on the influence of the radius ratio, liquid bridge volume, and contact angles on the rupture distance and transfer ratio. When the radius ratio was smaller than 2, it had a substantial impact on the rupture distance and transfer ratio. The experimental studies supported the effectiveness of the simulation modeling based on a minimal energy approach. Theoretical findings by a shooting method and simulated results showed great agreement. It was demonstrated that the maxima absolute error for rupture distance and transfer ratio were 0.001 and 0.0175, respectively. The simulated and theoretical results are helpful to predict the rupture distance and transfer ratio.

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Capillary Forces between Concave Gripper and Spherical Particle for Micro-Objects Gripping

Cited by 7 publications

References 31 publications

A Predictive Model of Capillary Forces and Contact Diameters between Two Plates Based on Artificial Neural Network

A Predictive Model of Capillary Forces and Contact Diameters between Two Plates Based on Artificial Neural Network

Pretreatment-free SERS sensing of microplastics using a self-attention-based neural network on hierarchically porous Ag foams

Experimental investigations of liquid bridge rupture between a sphere and a spherical concave

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