Data-driven time-dependent state estimation for interfacial fluid mechanics in evaporating droplets

All-Solution-Processed Electronics with Sub-Microscale Resolution and Nanoscale Fidelity Fabricated Via a Humidity-Controlled, Surface Energy-Directed Assembly Process

Solution-based processes have received considerable attention in the fabrication of electronics and sensors owing to their merits of being low-cost, vacuum-free, and simple in equipment. However, the current solution-based processes either lack patterning capability or have low resolution (tens of micrometers) and low pattern fidelity in terms of line edge roughness (LER, several micrometers). Here, we present a surface energy-directed assembly (SEDA) process to fabricate metal oxide patterns with up to 2 orders of magnitude improvement in resolution (800 nm) and LER (16 nm). Experiment results show that high pattern fidelity can be achieved only at low relative humidities of below 30%. The reason for this phenomenon lies in negligible water condensation on the solution droplet. Employing the SEDA process, all-solution-processed metal oxide thin film transistors (TFTs) are fabricated by using indium oxide as channel layers, indium tin oxide as source/drain electrodes and gate electrodes, and aluminum oxide as gate dielectrics. TFT-based logic gate circuits, including NOT, NOR, NAND, and AND are fabricated as well, demonstrating the applicability of the SEDA process in fabricating large area functional electronics.

Fundamentals of Monitoring Condensation and Frost/Ice Formation in Cold Environments Using Thin-Film Surface-Acoustic-Wave Technology

Moisture condensation, fogging, and frost or ice formation on structural surfaces cause severe hazards in many industrial components such as aircraft wings, electric power lines, and wind-turbine blades. Surface-acoustic-wave (SAW) technology, which is based on generating and monitoring acoustic waves propagating along structural surfaces, is one of the most promising techniques for monitoring, predicting, and also eliminating these hazards occurring on these surfaces in a cold environment. Monitoring condensation and frost/ice formation using SAW devices is challenging in practical scenarios including sleet, snow, cold rain, strong wind, and low pressure, and such a detection in various ambient conditions can be complex and requires consideration of various key influencing factors. Herein, the influences of various individual factors such as temperature, humidity, and water vapor pressure, as well as combined or multienvironmental dynamic factors, are investigated, all of which lead to either adsorption of water molecules, condensation, and/or frost/ice in a cold environment on the SAW devices. The influences of these parameters on the frequency shifts of the resonant SAW devices are systematically analyzed. Complemented with experimental studies and data from the literature, relationships among the frequency shifts and changes of temperature and other key factors influencing the dynamic phase transitions of water vapor on SAW devices are investigated to provide important guidance for icing detection and monitoring.

Drying of bio-colloidal sessile droplets: Advances, applications, and perspectives

Drying of biologically-relevant sessile droplets, including passive systems such as DNA, proteins, plasma, and blood, as well as active microbial systems comprising bacterial and algal dispersions, has garnered considerable attention over the last decades. Distinct morphological patterns emerge when bio-colloids undergo evaporative drying, with significant potential in a wide range of biomedical applications, spanning bio-sensing, medical diagnostics, drug delivery, and antimicrobial resistance. Consequently, the prospects of novel and thrifty bio-medical toolkits based on drying bio-colloids have driven tremendous progress in the science of morphological patterns and advanced quantitative image-based analysis. This review presents a comprehensive overview of bio-colloidal droplets drying on solid substrates, focusing on the experimental progress during the last ten years. We provide a summary of the physical and material properties of relevant bio-colloids and link their native composition (constituent particles, solvent, and concentrations) to the patterns emerging due to drying. We specifically examined the drying patterns generated by passive bio-colloids (e.g., DNA, globular, fibrous, composite proteins, plasma, serum, blood, urine, tears, and saliva). This article highlights how the emerging morphological patterns are influenced by the nature of the biological entities and the solvent, micro- and global environmental conditions (temperature and relative humidity), and substrate attributes like wettability. Crucially, correlations between emergent patterns and the initial droplet compositions enable the detection of potential clinical abnormalities when compared with the patterns of drying droplets of healthy control samples, offering a blueprint for the diagnosis of the type and stage of a specific disease (or disorder). Recent experimental investigations of pattern formation in the bio-mimetic and salivary drying droplets in the context of COVID-19 are also presented. We further summarized the role of biologically active agents in the drying process, including bacteria, algae, spermatozoa, and nematodes, and discussed the coupling between self-propulsion and hydrodynamics during the drying process. We wrap up the review by highlighting the role of cross-scale in situ experimental techniques for quantifying sub-micron to micro-scale features and the critical role of cross-disciplinary approaches (e.g., experimental and image processing techniques with machine learning algorithms) to quantify and predict the drying-induced features. We conclude the review with a perspective on the next generation of research and applications based on drying droplets, ultimately enabling innovative solutions and quantitative tools to investigate this exciting interface of physics, biology, data sciences, and machine learning.

Predicting the effects of environmental parameters on the spatio-temporal distribution of the droplets carrying coronavirus in public transport - A machine learning approach

Human-generated droplets constitute the main route for the transmission of coronavirus. However, the details of such transmission in enclosed environments are yet to be understood. This is because geometrical and environmental parameters can immensely complicate the problem and turn the conventional analyses inefficient. As a remedy, this work develops a predictive tool based on computational fluid dynamics and machine learning to examine the distribution of sneezing droplets in realistic configurations. The time-dependent effects of environmental parameters, including temperature, humidity and ventilation rate, upon the droplets with diameters between 1 and 250 μ m are investigated inside a bus. It is shown that humidity can profoundly affect the droplets distribution, such that 10% increase in relative humidity results in 30% increase in the droplets density at the farthest point from a sneezing passenger. Further, ventilation process is found to feature dual effects on the droplets distribution. Simple increases in the ventilation rate may accelerate the droplets transmission. However, carefully tailored injection of fresh air enhances deposition of droplets on the surfaces and thus reduces their concentration in the bus. Finally, the analysis identifies an optimal range of temperature, humidity and ventilation rate to maintain human comfort while minimising the transmission of droplets.