Application of an Ultrasound-assisted Polymer Surfactant-enhanced Emulsification Microextraction for Determination of Aromatic Amines in Water Sample

Molecular dynamics simulation of the coalescence of surfactant-laden droplets

We investigate the coalescence of surfactant-laden water droplets by using several different surfactant types and a wide range of concentrations by means of a coarse-grained model obtained by the statistical associating fluid theory. Our results demonstrate in detail a universal mass transport mechanism of surfactant across many concentrations and several surfactant types during the process. Coalescence initiation is seen to occur via a single pinch due to aggregation of surface surfactant, and its remnants tend to become engulfed in part inside the forming bridge. Across the board we confirm the existence of an initial thermal regime with constant bridge width followed by a later inertial regime with bridge width scaling roughly as the square root of time, but see no evidence of an intermediate viscous regime. Coalescence becomes slower as surfactant concentration grows, and we see evidence of the appearance of a further slowdown of a different nature for several times the critical concentration. We anticipate that our results provide further insights in the mechanisms of coalescence of surfactant-laden droplets.

Effect of interfacial rheology on drop coalescence in water-oil emulsion

Over the last years several studies have been conducted to understand emulsion formation and its behavior. In some applications, the aim is the phase separation of the emulsions through the coalescence of the drops, as in the oil industry. In this study, the relationship between rheological properties of oil-water interfaces and the drainage time of a thin oil film between two aqueous drops was investigated. Interfacial tension and dilatational rheology were measured using the axisymmetric drop shape analysis. We evaluated different concentrations of a nonionic surfactant (Span 80) dissolved in mineral oil (Primol 352) phase. The results indicate a direct relationship between the properties of the structure formed at the oil-water interface and the absence of droplet coalescence. For low surfactant concentrations, below the critical micelle concentration (CMC), the interface is weakly elastic (fluid-like) and the coalescence process always occurs; the draining time is not to related to the aging time of the interface. For surfactant concentrations above CMC, the elastic and viscous moduli showed significant changes with aging leading to the formation of a solid-like film at the interface preventing further coalescence. We used the characteristic times of change in interfacial rheological behavior to better explain the non-coalescence process.

Unraveling driving regimes for destabilizing concentrated emulsions within microchannels

Coalescence is the most widely demonstrated mechanism for destabilizing emulsion droplets in microfluidic chambers. However, we find that depending on the channel wall surface functionalization, surface zeta potential, type of surfactant, characteristics of the oil as a dispersed phase, or even the presence of externally-induced stress, other different destabilization mechanisms can occur in subtle ways. In general, we observe four regimes leading to destabilization of concentrated emulsions: (i) coalescence, (ii) emulsion bursts, (iii) a combination of the two first mechanisms, attributed to the simultaneous occurrence of coalescence and emulsion bursts; and (iv) compaction of the droplet network that eventually destabilizes to fracture-like behavior. We correlate various physico-chemical properties (zeta potential, contact angle, interfacial tension) to understand their respective influence on the destabilization mechanisms. This work provides insights into possible ways to control or inflict emulsion droplet destabilization for different applications.

Mixing characterization of binary-coalesced droplets in microchannels using deep neural network

Real-time object identification and classification are essential in many microfluidic applications especially in the droplet microfluidics. This paper discusses the application of convolutional neural networks to detect the merged microdroplet in the flow field and classify them in an on-the-go manner based on the extent of mixing. The droplets are generated in PMMA microfluidic devices employing flow-focusing and cross-flow configurations. The visualization of binary coalescence of droplets is performed by a CCD camera attached to a microscope, and the sequence of images is recorded. Different real-time object localization and classification networks such as You Only Look Once and Singleshot Multibox Detector are deployed for droplet detection and characterization. A custom dataset to train these deep neural networks to detect and classify is created from the captured images and labeled manually. The merged droplets are segregated based on the degree of mixing into three categories: low mixing, intermediate mixing, and high mixing. The trained model is tested against images taken at different ambient conditions, droplet shapes, droplet sizes, and binary-fluid combinations, which indeed exhibited high accuracy and precision in predictions. In addition, it is demonstrated that these schemes are efficient in localization of coalesced binary droplets from the recorded video or image and classify them based on grade of mixing irrespective of experimental conditions in real time.

Droplet control technologies for microfluidic high throughput screening (μHTS)

The transition from micro well plate and robotics based high throughput screening (HTS) to chip based screening has already started. This transition promises reduced droplet volumes thereby decreasing the amount of fluids used in these studies. Moreover, it significantly boosts throughput allowing screening to keep pace with the overwhelming number of molecular targets being discovered. In this review, we analyse state-of-the-art droplet control technologies that exhibit potential to be used in this new generation of screening devices. Since these systems are enclosed and usually planar, even some of the straightforward methods used in traditional HTS such as pipetting and reading can prove challenging to replicate in microfluidic high throughput screening (μHTS). We critically review the technologies developed for this purpose in depth, describing the underlying physics and discussing the future outlooks.

Surface acoustic wave enabled pipette on a chip

Mono-disperse droplet formation in microfluidic devices allows the rapid production of thousands of identical droplets and has enabled a wide range of chemical and biological studies through repeat tests performed at pico-to-nanoliter volume samples. However, it is exactly this efficiency of production which has hindered the ability to carefully control the location and quantity of the distribution of various samples on a chip - the key requirement for replicating micro well plate based high throughput screening in vastly reduced volumetric scales. To address this need, here, we present a programmable microfluidic chip capable of pipetting samples from mobile droplets with high accuracy using a non-contact approach. Pipette on a chip (PoaCH) system selectively ejects (pipettes) part of a droplet into a customizable reaction chamber using surface acoustic waves (SAWs). Droplet pipetting is shown to range from as low as 150 pL up to 850 pL with precision down to tens of picoliters. PoaCH offers ease of integration with existing lab on a chip systems as well as a robust and contamination-free droplet manipulation technique in closed microchannels enabling potential implementation in screening and other studies.