Biological applications of kinetics of wetting and spreading

Forced capillary wetting of viscoelastic fluids

HYPOTHESIS

Achieving rapid capillary wetting is highly desirable in nature and industries. Previous endeavors have primarily concentrated on passive wetting strategies through surface engineering. However, these approaches are inadequate for high-viscosity fluids due to the significant viscous resistance, especially for non-Newtonian fluids. In contrast, forced wetting emerges as a promising method to address the challenges associated with achieving rapid wetting of non-Newtonian fluids in capillaries.

EXPERIMENTS

To investigate the forced wetting behavior of viscoelastic fluids in capillaries, we employ Xanthan Gum (XG) aqueous solutions as target fluids with the storage modulus significantly exceeding the loss modulus. We utilize smooth glass capillaries connected to a syringe pump to achieve high moving speeds of up to 1 m/s.

FINDINGS

Our experiments reveal a significant distinction in the power-law exponent that governs the scaling relationship between the dynamic contact angle and velocity for viscoelastic fluids compared to Newtonian fluids. This exponent is considerably smaller and varies based on the concentration of viscoelastic fluids and the diameter of the capillaries. We suggest that the viscosity dominates the wetting dynamics of viscoelastic fluids, manifested by the contact line morphology-dependent behavior. This insight has significant implications for microfluidics and drug injectability.

Alginate/xanthan gum hydrogels as forensic blood substitutes for bloodstain formation and analysis

Understanding the behaviour of human blood outside of the body has important implications in forensic research, especially related to bloodstain pattern analysis (BPA). The design of forensic blood substitutes (FBSs) can provide many advantages, including the incorporation of multiple physiological components for use as safe and reliable materials for forensic applications. In this work, we present the design of synthetic alginate and xanthan gum-based hydrogels that contain electrosprayed microparticles (MPs) with and without crosslinked DNA. In addition to the MPs, the alginate/xanthan gum FBS materials include fillers to alter the physical appearance and fluid properties of the material. The optimized FBS consisted of alginate (1% w/v) and xanthan gum (5.0 × 10^-3% w/v), 2 mM CaCl₂, ferric citrate (0.5% w/v), magnesium silicate (0.25% w/v), Allura Red dye (2% w/v), 0.025% v/v Tween 20 and 9.5% v/v MPs. The FBS was tested in passive dripping experiments relevant to BPA scenarios at various impact angles. The spreading ratio (D_s/D₀) was determined for 90° stains made on a paper surface and compared to bovine blood where the FBS was shown to simulate accurate and predictable spreading behaviour. In addition, we simulated other common BPA scenarios (e.g., impact patterns) and evidence processing potential. The FBS could be swabbed, and the DNA could be extracted, amplified, and genotyped analogous to human blood evidence. A stability test was also conducted which revealed a shelf-life of over 4 weeks where the material remains relevant to human blood at physiological temperature.

High-Rate Alkaline Water Electrolysis at Industrially Relevant Conditions Enabled by Superaerophobic Electrode Assembly

Alkaline water electrolysis (AWE) is among the most developed technologies for green hydrogen generation. Despite the tremendous achievements in boosting the catalytic activity of the electrode, the operating current density of modern water electrolyzers is yet much lower than the emerging approaches such as the proton-exchange membrane water electrolysis (PEMWE). One of the dominant hindering factors is the high overpotentials induced by the gas bubbles. Herein, the bubble dynamics via creating the superaerophobic electrode assembly is optimized. The patterned Co-Ni phosphide/spinel oxide heterostructure shows complete wetting of water droplet with fast spreading time (≈300 ms) whereas complete underwater bubble repelling with 180° contact angle is achieved. Besides, the current collector/electrode interface is also modified by coating with aerophobic hydroxide on Ti current collector. Thus, in the zero-gap water electrolyzer test, a current density of 3.5 A cm^-2 is obtained at 2.25 V and 85 °C in 6 m KOH, which is comparable with the state-of-the-art PEMWE using Pt-group metal catalyst. No major performance degradation or materials deterioration is observed after 330 h test. This approach reveals the importance of bubble management in modern AWE, offering a promising solution toward high-rate water electrolysis.

Recent advances in eco-friendly fabrics with special wettability for oil/water separation

Considering the serious damage to aquatic ecosystems and marine life caused by oil spills and oily wastewater discharge, efficient, environment-friendly and sustainable oil/water separation technology has become an inevitable trend for current development. Herein, fabrics are recognized as eco-friendly materials for water treatment due to their good degradability and low cost. Particularly, fabrics with rough structures and natural hydrophilicity/oleophilicity enable the construction of superwetting surfaces for the selective separation of oil/water mixtures and even complex emulsions. Therefore, superwetting fabrics for efficiently solving oil spills and purifying oily wastewater have received extensive attention. Especially, Janus and smart fabrics are highly anticipated to enable the on-demand and sustainable treatment of oil spills and oily wastewater due to their changeable wettability. Moreover, the fabrication of superwetting fabrics with multifunctional performances for oily wastewater purification can further promote their practical industrial applications, such as photocatalytic, self-cleaning, and self-healing characteristics. However, some potential challenges still exist, which urgently need to be systematically summarized to guide the future development of this research field. In this review, firstly, the fundamental theories of wettability and the separation mechanisms based on special wettability are discussed. Then, superwetting fabrics for efficient oil/water separation are systematically reviewed, such as superhydrophobic/superoleophilic (SHB/SOL), superhydrophilic/superoleophobic (SHL/SOB), SHL/underwater superoleophobic (SHL/UWSOB), and UWSOB/underoil superoleophobic (UWSOB/UOSHB) fabrics. Most importantly, we highlight Janus, smart, and multifunctional fabrics based on their superwetting property. Correspondingly, the advantages and disadvantages of each superwetting fabric are comprehensively analyzed. Besides, super-antiwetting fabrics with superhydrophobic/superoleophobic (SHB/SOB) property are also introduced. Finally, the challenges and future research directions are explained.

Wetting properties of dehydrated biofilms under different growth conditions

Biofilms are resilient to environmental conditions and often resistant even to strong disinfectants. It is crucial to investigate their interfacial properties, which can be effectively characterized by wetting analysis. Wetting phenomena on biofilm surfaces have been poorly investigated in literature, in particular a systematic study of wetting on real biofilm-coated substrates including the application of external body forces (forced wetting, i.e.: centrifugal and gravitational forces) is missing. The aim of this work is to study the role of nutrient and shear flow conditions on wetting properties of Pseudomonas fluorescens dehydrated biofilms, grown on glass substrates. An innovative device (Kerberos®), capable to study spreading/sliding behavior under the application of external body forces, is used here for a systematic analysis of wetting/de-wetting liquid droplets on horizontal substrates under the action of tangential forces. Results prove that, under different growth conditions, (i.e., nutrients and imposed flow), biofilms exhibit different wetting properties. At lower nutrient/shear flow conditions, biofilms show spreading/sliding behavior close to that of pure glass. At higher nutrient and shear flow conditions, droplets on biofilms show spreading followed by imbibition soon after deposition, which leads to peculiar droplet depinning during the rotation test. Wetting properties are derived as a function of the rotation speed from both top and side views videoframes through a dedicated image analysis technique. A detailed analysis of biofilm formation and morphology/topography is also provided here.

Absorption of surfactant-laden droplets into porous media: A numerical study

HYPOTHESIS

Droplets can absorb into permeable substrates due to capillarity. It is hypothesized that the contact line dynamics influence this process and that an unpinned contact line results in slower absorption than a pinned contact line, since the contact area between the droplet and the substrate will decrease over time for the former. Furthermore, it is expected that surfactants can be used to accelerate the absorption.

SIMULATIONS

Lubrication theory is employed to model the droplet and Darcy's law is combined with the conservation law of mass to describe the absorption dynamics. For the surfactant transport, several convection-diffusion-adsorption equations are solved.

FINDINGS

It is found that moving contact lines result in a parabola-shaped wetted area and a slower absorption and a deeper penetration depth than pinned contact lines. The evolution of the penetration depth was quantitatively validated by comparison with two experimental studies from literature. Surfactants were shown to accelerate the absorption process, but only if their adsorption kinetics are slow compared to the absorption. Otherwise, all surfactant adsorbs onto the pore walls before reaching the wetting front, resulting in the same absorption rate as without surfactants. This behavior agrees with both experimental and analytical literature.

Transient Prediction of Nanoparticle-Laden Droplet Drying Patterns through Dynamic Mode Decomposition

Nanoparticle-laden sessile droplet drying has a wide impact on applications. However, the complexity affected by the droplet evaporation dynamics and particle self-assembly behavior leads to challenges in the accurate prediction of the drying patterns. We initiate a data-driven machine learning algorithm by using a single data collection point via a top-view camera to predict the transient drying patterns of aluminum oxide (Al₂O₃) nanoparticle-laden sessile droplets with three cases according to particle sizes of 5 and 40 nm and Al₂O₃ concentrations of 0.1 and 0.2 wt %. Dynamic mode decomposition is used as the data-driven learning model to recognize each nanoparticle-laden droplet as an individual system and then apply the transfer learning procedure. Along 270 s of droplet drying experiments, the training period of the first 100 s is selected, and then the rest of the 170 s is predicted with less than a 10% error between the predicted and the actual droplet images. The developed data-driven approach has also achieved the acceptable prediction for the droplet diameter with less than 0.13% error and a coffee-ring thickness over a range of 2.0 to 6.7 μm. Moreover, the proposed machine learning algorithm can recognize the volume of the droplet liquid and the transition of the drying regime from one to another according to the predicted contact line and the droplet height.

Machine Learning Analysis for Quantitative Discrimination of Dried Blood Droplets

One of the most interesting and everyday natural phenomenon is the formation of different patterns after the evaporation of liquid droplets on a solid surface. The analysis of dried patterns from blood droplets has recently gained a lot of attention, experimentally and theoretically, due to its potential application in diagnostic medicine and forensic science. This paper presents evidence that images of dried blood droplets have a signature revealing the exhaustion level of the person, and discloses an entirely novel approach to studying human dried blood droplet patterns. We took blood samples from 30 healthy young male volunteers before and after exhaustive exercise, which is well known to cause large changes to blood chemistry. We objectively and quantitatively analysed 1800 images of dried blood droplets, developing sophisticated image processing analysis routines and optimising a multivariate statistical machine learning algorithm. We looked for statistically relevant correlations between the patterns in the dried blood droplets and exercise-induced changes in blood chemistry. An analysis of the various measured physiological parameters was also investigated. We found that when our machine learning algorithm, which optimises a statistical model combining Principal Component Analysis (PCA) as an unsupervised learning method and Linear Discriminant Analysis (LDA) as a supervised learning method, is applied on the logarithmic power spectrum of the images, it can provide up to 95% prediction accuracy, in discriminating the physiological conditions, i.e., before or after physical exercise. This correlation is strongest when all ten images taken per volunteer per condition are averaged, rather than treated individually. Having demonstrated proof-of-principle, this method can be applied to identify diseases.