Metal Surface Engineering for Extreme Sustenance of Jumping Droplet Condensation.

Water vapor condensation on metallic surfaces is critical to a broad range of applications, ranging from power generation to the chemical and pharmaceutical industries. Enhancing simultaneously the heat transfer efficiency, scalability, and durability of a condenser surface remains a persistent challenge. Coalescence-induced condensing droplet jumping is a capillarity-driven mechanism of self-ejection of microscopic condensate droplets from a surface. This mechanism is highly desired due to the fact that it continuously frees up the surface for new condensate to form directly on the surface, enhancing heat transfer without requiring the presence of the gravitational field. However, this condensate ejection mechanism typically requires the fabrication of surface nanotextures coated by an ultrathin (<10 nm) conformal hydrophobic coating (hydrophobic self-assembled monolayers such as silanes), which results in poor durability. Here, we present a scalable approach for the fabrication of a hierarchically structured superhydrophobic surface on aluminum substrates, which is able to withstand adverse conditions characterized by condensation of superheated steam shear flow at pressure and temperature up to ≈1.42 bar and ≈111 °C, respectively, and velocities in the range ≈3-9 m/s. The synergetic function of micro- and nanotextures, combined with a chemically grafted, robust ultrathin (≈4.0 nm) poly-1H,1H,2H,2H-perfluorodecyl acrylate (pPFDA) coating, which is 1 order of magnitude thinner than the current state of the art, allows the sustenance of long-term coalescence-induced condensate jumping drop condensation for at least 72 h. This yields unprecedented, up to an order of magnitude higher heat transfer coefficients compared to filmwise condensation under the same conditions and significantly outperforms the current state of the art in terms of both durability and performance establishing a new milestone.

Out-of-Plane Biphilic Surface Structuring for Enhanced Capillary-Driven Dropwise Condensation.

Rapid and sustained condensate droplet departure from a surface is key toward achieving high heat-transfer rates in condensation, a physical process critical to a broad range of industrial and societal applications. Despite the progress in enhancing condensation heat transfer through inducing its dropwise mode with hydrophobic materials, sophisticated surface engineering methods that can lead to further enhancement of heat transfer are still highly desirable. Here, by employing a three-dimensional, multiphase computational approach, we present an effective out-of-plane biphilic surface topography, which reveals an unexplored capillarity-driven departure mechanism of condensate droplets. This texture consists of biphilic diverging microcavities wherein a matrix of small hydrophilic spots is placed at their bottom, that is, among the pyramid-shaped, superhydrophobic microtextures forming the cavities. We show that an optimal combination of the hydrophilic spots and the angles of the pyramidal structures can achieve high deformational stretching of the droplets, eventually realizing an impressive "slingshot-like" droplet ejection process from the texture. Such a droplet departure mechanism has the potential to reduce the droplet ejection volume and thus enhance the overall condensation efficiency, compared to coalescence-initiated droplet jumping from other state-of-the-art surfaces. Simulations have shown that optimal pyramid-shaped biphilic microstructures can provoke droplet self-ejection at low volumes, up to 56% lower than superhydrophobic straight pillars, revealing a promising new surface microtexture design strategy toward enhancing the condensation heat-transfer efficiency and water harvesting capabilities.

Ultrathin Durable Organic Hydrophobic Coatings Enhancing Dropwise Condensation Heat Transfer.

Organic hydrophobic layers targeting sustained dropwise condensation are highly desirable but suffer from poor chemical and mechanical stability, combined with low thermal conductivity. The requirement of such layers to remain ultrathin to minimize their inherent thermal resistance competes against durability considerations. Here, we investigate the long-term durability and enhanced heat-transfer performance of perfluorodecanethiol (PFDT) coatings compared to alternative organic coatings, namely, perfluorodecyltriethoxysilane (PFDTS) and perfluorodecyl acrylate (PFDA), the latter fabricated with initiated chemical vapor deposition (iCVD), in condensation heat transfer and under the challenging operating conditions of intense flow (up to 9 m s-1) of superheated steam (111 °C) at high pressures (1.42 bar). We find that the thiol coating clearly outperforms the silane coating in terms of both heat transfer and durability. In addition, despite being only a monolayer, it clearly also outperforms the iCVD-fabricated PFDA coating in terms of durability. Remarkably, the thiol layer exhibited dropwise condensation for at least 63 h (>2× times more than the PFDA coating, which survived for 30 h), without any visible deterioration, showcasing its hydrolytic stability. The cost of thiol functionalization per area was also the lowest as compared to all of the other surface hydrophobic treatments used in this study, thus making it the most efficient option for practical applications on copper substrates.

Exploring Artificial Neural Networks Efficiency in Tiny Wearable Devices for Human Activity Recognition.

The increasing diffusion of tiny wearable devices and, at the same time, the advent of machine learning techniques that can perform sophisticated inference, represent a valuable opportunity for the development of pervasive computing applications. Moreover, pushing inference on edge devices can in principle improve application responsiveness, reduce energy consumption and mitigate privacy and security issues. However, devices with small size and low-power consumption and factor form, like those dedicated to wearable platforms, pose strict computational, memory, and energy requirements which result in challenging issues to be addressed by designers. The main purpose of this study is to empirically explore this trade-off through the characterization of memory usage, energy consumption, and execution time needed by different types of neural networks (namely multilayer and convolutional neural networks) trained for human activity recognition on board of a typical low-power wearable device.Through extensive experimental results, obtained on a public human activity recognition dataset, we derive Pareto curves that demonstrate the possibility of achieving a 4× reduction in memory usage and a 36× reduction in energy consumption, at fixed accuracy levels, for a multilayer Perceptron network with respect to more sophisticated convolution network models.

Exploring machine learning for untargeted metabolomics using molecular fingerprints.

BACKGROUND: Metabolomics, the study of substrates and products of cellular metabolism, offers valuable insights into an organism's state under specific conditions and has the potential to revolutionise preventive healthcare and pharmaceutical research. However, analysing large metabolomics datasets remains challenging, with available methods relying on limited and incompletely annotated metabolic pathways. METHODS: This study, inspired by well-established methods in drug discovery, employs machine learning on metabolite fingerprints to explore the relationship of their structure with responses in experimental conditions beyond known pathways, shedding light on metabolic processes. It evaluates fingerprinting effectiveness in representing metabolites, addressing challenges like class imbalance, data sparsity, high dimensionality, duplicate structural encoding, and interpretable features. Feature importance analysis is then applied to reveal key chemical configurations affecting classification, identifying related metabolite groups. RESULTS: The approach is tested on two datasets: one on Ataxia Telangiectasia and another on endothelial cells under low oxygen. Machine learning on molecular fingerprints predicts metabolite responses effectively, and feature importance analysis aligns with known metabolic pathways, unveiling new affected metabolite groups for further study. CONCLUSION: In conclusion, the presented approach leverages the strengths of drug discovery to address critical issues in metabolomics research and aims to bridge the gap between these two disciplines. This work lays the foundation for future research in this direction, possibly exploring alternative structural encodings and machine learning models.

Durable, Ultrathin, and Antifouling Polymer Brush Coating for Efficient Condensation Heat Transfer.

Heat exchangers are made of metals because of their high heat conductivity and mechanical stability. Metal surfaces are inherently hydrophilic, leading to inefficient filmwise condensation. It is still a challenge to coat these metal surfaces with a durable, robust, and thin hydrophobic layer, which is required for efficient dropwise condensation. Here, we report the nonstructured and ultrathin (∼6 nm) polydimethylsiloxane (PDMS) brushes on copper that sustain high-performing dropwise condensation in high supersaturation. Due to the flexible hydrophobic siloxane polymer chains, the coating has low resistance to drop sliding and excellent chemical stability. The PDMS brushes can sustain dropwise condensation for up to ∼8 h during exposure to 111 °C saturated steam flowing at 3 m·s-1, with a 5-7 times higher heat transfer coefficient compared to filmwise condensation. The surface is self-cleaning and can reduce the level of bacterial attachment by 99%. This low-cost, facile, fluorine-free, and scalable method is suitable for a great variety of heat transfer applications.

Ultrathin Lubricant-Infused Vertical Graphene Nanoscaffolds for High-Performance Dropwise Condensation.

Lubricant-infused surfaces (LIS) are highly efficient in repelling water and constitute a very promising family of materials for condensation processes occurring in a broad range of energy applications. However, the performance of LIS in such processes is limited by the inherent thermal resistance imposed by the thickness of the lubricant and supporting surface structure, as well as by the gradual depletion of the lubricant over time. Here, we present an ultrathin (∼70 nm) and conductive LIS architecture, obtained by infusing lubricant into a vertically grown graphene nanoscaffold on copper. The ultrathin nature of the scaffold, combined with the high in-plane thermal conductivity of graphene, drastically minimize earlier limitations, effectively doubling the heat transfer performance compared to a state-of-the-art CuO LIS surface. We show that the effect of the thermal resistance to the heat transfer performance of a LIS surface, although often overlooked, can be so detrimental that a simple nanostructured CuO surface can outperform a CuO LIS surface, despite filmwise condensation on the former. The present vertical graphene LIS is also found to be resistant to lubricant depletion, maintaining stable dropwise condensation for at least 24 h with no significant change of advancing contact angle and contact angle hysteresis. The lubricant consumed by the vertical graphene LIS is 52.6% less than that of the existing state-of-the-art CuO LIS, also making the fabrication process more economical.

Droplet Self-Propulsion on Superhydrophobic Microtracks.

Liquid transport (continuous or segmented) in microfluidic platforms typically requires pumping devices or external fields working collaboratively with special fluid properties to enable fluid motion. Natural liquid adhesion on surfaces deters motion and promotes the possibility of liquid or surface contamination. Despite progress, significant advancements are needed before devices for passive liquid propulsion, without the input of external energy and unwanted contamination, become a reality in applications. Here we present an unexplored and facile approach based on the Laplace pressure imbalance, manifesting itself through targeted track texturing, driving passively droplet motion, while maintaining the limited contact of the Cassie-Baxter state on superhydrophobic surfaces. The track topography resembles out-of-plane, backgammon-board, slowly converging microridges decorated with nanotexturing. This design naturally deforms asymmetrically the menisci formed at the bottom of a droplet contacting such tracks and causes a Laplace pressure imbalance that drives droplet motion. We investigate this effect over a range of opening track angles and develop a model to explain and quantify the underlying mechanism of droplet self-propulsion. We further implement the developed topography for applications relevant to microfluidic platform functionalities. We demonstrate control of the rebound angle of vertically impacting droplets, achieve horizontal self-transport to distances up to 65 times the droplet diameter, show significant uphill motion against gravity, and illustrate a self-driven droplet-merging process.

PCDD/Fs, DL-PCBs, and NDL-PCBs in Dairy Cows: Carryover in Milk from a Controlled Feeding Study.

A feeding study was carried out to investigate the kinetics in cow milk of the 17 polychlorinated dibenzo-p-dioxins and dibenzofurans (PCDD/Fs), the 12 dioxin-like polychlorinated biphenyls (DL-PCBs), and the 6 non-dioxin-like PCBs (NDL-PCBs) regulated by the European (EU) legislation. A fortified ration (ΣPCDD/Fs and DL-PCBs: 24.68 ng TEQ/day/cow; ΣNDL-PCBs: 163.99 µg/day/cow) was given to the animals for 49 days, followed by 42 days on clean feed. EU maximum limit for TEQPCDD/F+DL-PCB was exceeded in milk after 1 week of exposure, while for ΣNDL-PCBs, after 5 weeks. Milk compliance was restored after 1 week on clean feed, but to return to the basal TEQPCDD/F+DL-PCB it took 42 days. At the end of the study, ΣNDL-PCBs had not yet reached the basal level. The carryover rate of ΣNDL-PCBs was 25.4%, while the carryover rate of TEQPCDD/F+DL-PCB was 36.9%. The latter was mainly affected by the 12 congeners contributing most to the toxic equivalent (TEQ) level, explaining the fast overcome of the maximum limit in milk.

Water-Based Scalable Methods for Self-Cleaning Antibacterial ZnO-Nanostructured Surfaces.

Bacterial colonization poses significant health risks, such as infestation of surfaces in biomedical applications and clean water unavailability. If maintaining the surrounding water clean is a target, developing surfaces with strong bactericidal action, which is facilitated by bacterial access to the surface and mixing, can be a solution. On the other hand, if sustenance of a surface free of bacteria is the goal, developing surfaces with ultralow bacterial adhesion often suffices. Here we report a facile, scalable, and environmentally benign strategy that delivers customized surfaces for these challenges. For bactericidal action, nanostructures of inherently antibacterial ZnO, through simple immersion of zinc in hot water, are fabricated. The resulting nanostructured surface exhibits extreme bactericidal effectiveness (9250 cells cm-2 h-1) that eliminates bacteria in direct contact and also remotely through the action of reactive oxygen species. Remarkably, the remote bactericidal action is achieved without the need for any illumination, otherwise required in conventional approaches. As a result, ZnO nanostructures yield outstanding water disinfection of >99.98%, in the dark, by inactivating the bacteria within 3 h. Moreover, Zn2+ released to the aqueous medium from the nanostructured ZnO surface have a concentration of 0.73 ± 0.15 ppm, markedly below the legal limit for safe drinking water (5-6 ppm). The same nanostructures, when hydrophobized (through a water-based or fluorine-free spray process), exhibit strong bacterial repulsion, thus substantially reducing bacterial adhesion. Such environmentally benign and scalable methods showcase pathways toward inhibiting surface bacterial colonization.