Multiscale Surface Research Articles

ResGEM: Multi-scale Graph Embedding Network for Residual Mesh Denoising.

Mesh denoising is a crucial technology that aims to recover a high-fidelity 3D mesh from a noise-corrupted one. Deep learning methods, particularly graph convolutional networks (GCNs) based mesh denoisers, have demonstrated their effectiveness in removing various complex real-world noises while preserving authentic geometry. However, it is still a quite challenging work to faithfully regress uncontaminated normals and vertices on meshes with irregular topology. In this paper, we propose a novel pipeline that incorporates two parallel normal-aware and vertex-aware branches to achieve a balance between smoothness and geometric details while maintaining the flexibility of surface topology. We introduce ResGEM, a new GCN, with multi-scale embedding modules and residual decoding structures to facilitate normal regression and vertex modification for mesh denoising. To effectively extract multi-scale surface features while avoiding the loss of topological information caused by graph pooling or coarsening operations, we encode the noisy normal and vertex graphs using four edge-conditioned embedding modules (EEMs) at different scales. This allows us to obtain favorable feature representations with multiple receptive field sizes. Formulating the denoising problem into a residual learning problem, the decoder incorporates residual blocks to accurately predict true normals and vertex offsets from the embedded feature space. Moreover, we propose novel regularization terms in the loss function that enhance the smoothing and generalization ability of our network by imposing constraints on normal consistency. Comprehensive experiments have been conducted to demonstrate the superiority of our method over the state-of-the-art on both synthetic and real-scanned datasets.

Multifunctional nanofiber membrane for high removal efficiency of biological/organic contaminations and oil-in-water emulsion under gravity-driven separation

Treating oily wastewater is a significant challenge, and developing cost-effective, durable, and efficient membranes is crucial for this purpose. This study introduces a novel and simple approach to transform a superhydrophobic/superoleophilic polystyrene Fe3O410%@ PS membrane, with a contact angle of 162°, into a multifunctional superhydrophilic-underwater superoleophobic (SUS) SiO2@Fe3O4@ PS membrane, with a 0°contact angle. Fabrication entails electrospinning highly porous Fe3O410%@ PS nanofibrous membranes coated with silica nanoparticles to achieve a unique surface structure with multiscale roughness and surface energy. The resulting membrane demonstrates excellent performance in gravity-driven separation, effectively removing oils, oil-in-water emulsion, dyes, and biological pollutants while maintaining high permeability (up to 1530 L.m−2.h−1). The SiO2@Fe3O4@ PS nanocomposite membrane exhibits outstanding separation efficiency for oil/water mixtures, emulsions, algae, parasites, and organic dyes like Methylene blue (MB) with a removal effectiveness of 99.9 %. Furthermore, this multifunctional membrane nanocomposite is durable and is utilized in various challenging environments, including alkaline, acidic, salty, and hot water. It can withstand at least 50 separation cycles without retraining, which is cost-effective.

Cascaded detection method for surface defects of lead frame based on high-resolution detection images

In the field of semiconductor production and manufacturing, the detection of defects on lead frame surfaces is a vital process. This process plays a key role in ensuring the quality of the final product. Using high-resolution detection images to detect multi-scale tiny surface defects is necessary, but this amplifies the impact of environmental noise. Therefore, suppressing both the false negative rate and false positive rate in practical detection scenarios is a challenge that needs to be overcome. Current research on lead frame surface defect detection is mostly concentrated on the downloaded standard original images, which limits its application in actual production lines. This paper presents a cascaded detection method for surface defects of lead frame based on high-resolution detection images. Firstly, this study presents the unit cell extraction module to convert the detection object from high-resolution image to hundreds of unit cells. The proposed module can handle real-time detection images in the production pipeline, especially addressing situations such as lighting imbalances and tilted detection images. Subsequently, this study proposes a lead frame surface defect detection network (LDD-net), which takes unit cells as inputs and can effectively detect multi-scale defects. Compared to other models, LDD-net can effectively capture the features of subtle defects. Additionally, this paper introduces the deviation in the central width direction into the CIoU localization loss, enhancing the accuracy of defect localization in LDD-net. The data set is constructed using the machine vision detection system and conducts training and testing. Specifically, experiments of LDD-net on the data set obtained 85.01% mean average precision (mAP) and 37 ms of inference time, respectively. The detection accuracy exceeds 95%, and the false negative rate can be controlled below 6%. This approach will assist manual monitoring personnel in evaluating product quality.

Direct Numerical Simulation of Transitional and Turbulent Flows Over Multi-Scale Surface Roughness—Part II: The Effect of Roughness on the Performance of a High-Pressure Turbine Blade

Abstract Turbine blades generally present surface roughness introduced in the manufacturing process or caused by in-service degradation, which can have a significant impact on aero-thermal performance. A better understanding of the fundamental physical mechanisms arising from the interaction between the roughness and the turbine flow at engine-relevant conditions can provide insights for the design of blades with improved efficiency and longer operational life. To this end, a high-fidelity numerical framework combining a well-validated solver for direct numerical simulation and a second-order accurate immersed boundary method is employed to predict roughness-induced aero-thermal effects on an LS89 high-pressure turbine (HPT) blade at engine-relevant conditions. Different amplitudes and distributions of surface roughness are investigated and a reference smooth-blade simulation under the same flow conditions is conducted for comparison. Roughness of increasing amplitude progressively shifts the blade suction side boundary layer transition upstream, producing larger values of the turbulent kinetic energy and higher total wake losses. The on-surface data-capturing capabilities of the numerical framework provide direct measurements of the heat flux and the skin friction coefficient, hence offering quantitative information between the surface topology and engineering-relevant performance parameters. This work may provide a benchmark for future numerical studies of turbomachinery flows with roughness.

Direct Numerical Simulation of Transitional and Turbulent Flows Over Multi-Scale Surface Roughness—Part I: Methodology and Challenges

Abstract High-fidelity simulation of transitional and turbulent flows over multi-scale surface roughness presents several challenges. For instance, the complex and irregular geometrical nature of surface roughness makes it impractical to employ conforming structured grids, commonly adopted in large-scale numerical simulations due to their high computational efficiency. One possible solution to overcome this problem is offered by immersed boundary methods, which allow wall boundary conditions to be enforced on grids that do not conform to the geometry of the solid boundary. To this end, a three-dimensional, second-order accurate boundary data immersion method (BDIM) is adopted. A novel mapping algorithm that can be applied to general three-dimensional surfaces is presented, together with a newly developed data-capturing methodology to extract and analyze on-surface flow quantities of interest. A rigorous procedure to compute gradient quantities such as the wall shear stress and the heat flux on complex non-conforming geometries is also introduced. The new framework is validated by performing a direct numerical simulation (DNS) of fully developed turbulent channel flow over sinusoidal egg-carton roughness in a minimal-span domain. For this canonical case, the averaged streamwise velocity profiles are compared against results from the literature obtained with a body-fitted grid. General guidelines on the BDIM resolution requirements for multi-scale roughness simulation are given. Momentum and energy balance methods are used to validate the calculation of the overall skin friction and heat transfer at the wall. The BDIM is then employed to investigate the effect of irregular homogeneous surface roughness on the performance of an LS-89 high-pressure turbine blade at engine-relevant conditions using DNS. This is the first application of the BDIM to realize multi-scale roughness for transitional flow in transonic conditions in the context of high-pressure turbines. The methodology adopted to generate the desired roughness distribution and to apply it to the reference blade geometry is introduced. The results are compared to the case of an equivalent smooth blade.

Critical pressure versus mechanical criteria for the analytical modeling of wetting on multiscale surfaces

HypothesisIn order to better understand the outstanding wetting behaviors of many vegetal surfaces, it is necessary to properly carry out an analytical modeling of mixed-state wetting on real and multiscale surfaces. The energetical approach is limited with regards to its ability to describe the dynamics of the impregnation of surface asperities and therefore, a mechanical or a critical pressure approach can be more suitable. Nevertheless, as yet they have not been applied on natural surfaces with a sufficiently accurate description of the latter and have only yielded qualitative results. ExperimentsThe theoretical basis underlying both the mechanical and the critical pressure approaches to capillary phenomena is thoroughly derived and discussed. Critical pressure criteria that are applicable to real multiscale surfaces are introduced and applied to the case of the sacred Lotus, accounting for its nanoscale topography. FindingsEffective anchorage depths, solid-liquid contact areas and triple line lengths are calculated. Results quantitatively show that the presence of a nanoscale topography leads to a more superficial mixed-state wetting and increases the maximal overpressure sustainable by the menisci in a composite-wetting state. It is also demonstrated that the surface asperities of Nelumbo nucifera leaves have evolved to withstand severe raindrop impacts without being over-sized. Eventually, we show that the mechanical approach is flawed. Therefore, we strongly advocate using critical pressure criteria when designing multiscale water-repellent surfaces, for which we suggest design guidelines.

Bubble Engineering on Micro-/Nanostructured Electrodes for Water Splitting.

Bubble behaviors play crucial roles in mass transfer and energy efficiency in gas evolution reactions. Combining multiscale structures and surface chemical compositions, micro-/nanostructured electrodes have drawn increasing attention. With the aim to identify the exciting opportunities and rationalize the electrode designs, in this review, we present our current comprehension of bubble engineering on micro-/nanostructured electrodes, focusing on water splitting. We first provide a brief introduction of gas wettability on micro-/nanostructured electrodes. Then we discuss the advantages of micro-/nanostructured electrodes for mass transfer (detailing the lowered overpotential, promoted supply of electrolyte, and faster bubble growth kinetics), localized electric field intensity, and electrode stability. Following that, we outline strategies for promoting bubble detachment and directional transportation. Finally, we offer our perspectives on this emerging field for future research directions.

Enhancing mechanical properties and thermal conductivity in polymer bonded explosives by multi-scale surface modification of carbon fibers

Poor interfacial interaction and strength largely restrict the overall performance and practical application of carbon fibers (CFs) reinforced composites. The favorable interfacial properties were the key to realize superior mechanical properties in composites. Herein, we reported a novel multi-scale surface modification strategy of CFs to strengthen interfacial properties. Based on chemical oxidation treatment, the surface of CFs was further in situ grafted by a crosslinked high-strength polymer network consisting of aromatic diisocyanate, graphene oxide (GO) and polyethylenen glycol (PEG), which significantly improved the interfacial bonding and mechanical strength of interface layer itself. Benefitting from this multi-scale surface treatment, a high-efficiency mechanical enhancement of polymer bonded explosives (PBX) was achieved. With only 0.3 wt% fiber content, the maximum tensile and compressive strength PBX composites were both significantly improved, which were 63 % and 39 % higher than those of pure PBX, respectively. Meanwhile, the thermal conductivity was also enhanced, yielding a significant synergistic enhancement effect. The interface failure mechanism of the composite under stress was clarified by the fracture morphology characterization. This study sheds a light for exploring novel surface modification and has the potential application in in high performance polymer composites.

3D Printed Bioinspired Hierarchical Surface Structure with Tunable Wettability

Abstract Nature has examples of impressive surfaces and interfaces with diverse wettability stemming from superhydrophilicity to superhydrophobicity. The multiscale surface structures found in biological systems generally have high geometric complexity, which makes it challenging to replicate their characteristics, especially using traditional fabrication techniques. It is even more challenging to fabricate such complex microstructures with tunable wettability. In this paper, we propose a method to tune the wettability of a micro-scale surface by changing the geometrical parameters of embedded micro-structures in the surface. By taking inspiration from an insect (springtails), we designed micropillar arrays with different roughness by adjusting geometric parameters such as reentrant angle, pitch distance, and the number of spikes and pillars. This study shows that, by changing geometrical parameters in microscale, the apparent contact angle, and hence the surface wettability can be calibrated. The microscale pillars were fabricated using a precise micro direct light processing (uDLP) three-dimensional (3D) printer. Different printing parameters were studied to optimize the geometric parameters to fabricate 3D hierarchical structures with high accuracy and resolution. By controlling the size of different features of the pillar, pillar number, and layout of the mushroom-shaped micropillars, the wettability of the surface is possible to be tuned from a highly nonwetting liquid/material combination to highly wetting material. Such wettability tuning capability expands the design space for many biomedical and thermofluidic applications.

A Sustainable Superhydrophobic and Photothermal Coatings for Anti-Icing Application on Concrete with a Simple Method for CNTs/SiO2 Modification

Ice formation on concrete surfaces significantly challenges productivity, economic growth, and safety in diverse industrial sectors. Superhydrophobic coatings represent an effective solution to delay ice formation, although their functionality deteriorates under repeated freeze–thaw cycles. To address this issue, carbon nanotubes (CNTs) are frequently employed due to their exceptional photothermal conversion and mechanical properties, which contribute to extending the sustainability of the superhydrophobic coatings. However, the chemical inertness of CNTs often necessitates complex reactions to modify their functionalization. In this study, we have invented a simple method involving the sequential growth of silica on the surface of CNTs and the hydrophobic modification of the silica surface to enhance CNT functionality. These CNTs/SiO2 functionalized nanoparticles were then incorporated into an epoxy resin using a simple spray technique, resulting in a superhydrophobic and photothermal coating on concrete. To fine-tune the coating’s properties, we explored the effects of varying the doping levels of the nanoparticles on the surface morphology, roughness, and wettability of the CNT/SiO2-EP coatings. The optimal level of hydrophobicity was achieved by doping the coatings with 300 mg of functionalized nanoparticles, yielding an impressive contact angle of 159.6°. The integration of functionalized nanoparticles into the epoxy matrix not only enhances hydrophobicity but also improves mechanical robustness and abrasion resistance by creating multiscale surface roughness. Additionally, the coating exhibits outstanding chemical stability even under extreme conditions. One of the most significant advantages of these coatings is their ability to extend the ice nucleation time significantly. This effect is primarily attributed to the superior superhydrophobicity of the nanoparticles and the remarkable photothermal conversion capability of the CNTs. Upon exposure to Xenon lamp radiation, the ice droplets rapidly melt, underscoring the impressive performance of these coatings in preventing ice formation.

High-resolution art recognition of modern packaging design and images based on texture image classification

Abstract First, this paper proposes the EPNN algorithm to recognize the high-resolution art of texture images. Then, a multi-scale multivariate image analysis method for color-textured surface classification is established. Regarding probabilistic neural networks, a differential evolutionary algorithm is proposed to optimize the smoothing parameters of basic probabilistic neural networks. Texture feature extraction involves extracting energy and statistical features of texture images using tree-structured wavelet packet decomposition and statistical-based methods. The image classification is performed by estimating similarity distances and classifying typical feature clusters. The results show that in terms of recognition ability, the proposed EPNN in this paper achieves an average improvement of 0.216 in CCP compared to the LBP method. In terms of classification ability, the proposed multi-scale multivariate image analysis color texture surface classification method in this paper generally achieves more than 10% higher classification ability than LGP in CUReT and Outex texture image libraries.

Short-Term (7 Day) Beaufort Sea Ice Extent Forecasting with Deep Learning

Abstract Ships inside the Arctic basin require high-resolution (1–5 km), near-term (days to semimonthly) forecasts for guidance on scales of interest to their operations where forecast model predictions are insufficient due to their coarse spatial and temporal resolutions. Deep learning techniques offer the capability of rapid assimilation and analysis of multiple sources of information for improved forecasting. Data from the National Oceanographic and Atmospheric Administration’s Global Forecast System, Multi-scale Ultra-high Resolution Sea Surface Temperature (MEaSUREs), and the National Snow and Ice Data Center’s Multisensor Analyzed Sea ice Extent (MASIE) were used to develop the sea ice extent deep learning forecast model, over the freeze-up periods of 2016, 2018, 2019, and 2020 in the Beaufort Sea. Sea ice extent forecasts were produced for 1–7 days in the future. The approach was novel for sea ice extent forecasting in using forecast data as model input to aid in the prediction of sea ice extent. Model accuracy was assessed against a persistence model. While the average accuracy of the persistence model dropped from 97% to 90% for forecast days 1–7, the deep learning model accuracy dropped only to 93%. A k-fold (fourfold) cross-validation study found that on all except the first day, the deep learning model, which includes a U-Net architecture with an 18-layer residual neural network (Resnet-18) backbone, does better than the persistence model. Skill scores improve the farther out in time to 0.27. The model demonstrated success in predicting changes in ice extent of significance for navigation in the Amundsen Gulf. Extensions to other Arctic seas, seasons, and sea ice parameters are under development. Significance Statement Ships traversing the Arctic require timely, accurate sea ice location information to successfully complete their transits. After testing several potential candidates, we have developed a short-term (7 day) forecast process using existing observations of ice extent and sea surface temperature, with operational forecasts of weather and oceanographic variables, and appropriate machine learning models. The process included using forecasts of atmospheric and oceanographic conditions, as a human forecaster/analyst would. The models were trained for the Beaufort Sea north of Alaska using data and forecasts from 2016 combined with 2018–20. The results showed improvement in short-term forecasts of ice locations over current methods and also demonstrated correctly predicted changes in the sea ice that are important for navigation.

Rapid multiscale surface texture manufacturing process using hybrid laser belt machining

Rapid multiscale surface texture manufacturing process using hybrid laser belt machining

Tailoring microbial response to stainless steel surfaces via electrolyte jet machining and electrodeposition

Electrolyte jet machining (EJM) and electrodeposition present unexploited opportunities for the manufacturing of multiscale structured surfaces. The present work assesses the morphology and antibacterial properties of textured and multiscale stainless steel surfaces produced by EJM with a new 50 mm × 0.3 mm slit nozzle, as well as multiscale surfaces subject to electrodeposition with copper. Characterisation of the surface morphology obtained via EJM with various combinations of current density, machining time and groove spacing is performed to facilitate the selection of representative surfaces for bacterial retention tests. Higher current density with constant applied charge leads to lower surface roughness at constant scanning speed and greater groove depth and width under static conditions. Groove depth is proportional to machining time at constant current density, while a groove pitch of 1.05 mm is found to provide the optimum compromise between maximum groove depth and complete surface coverage. Microscale surface features are found to reduce Escherichia coli and Pseudomonas aeruginosa retention by up to 97% compared to untextured control surfaces, while larger grooves play a role in inhibiting bacterial attachment by inducing turbulence. The presence of copper agglomerates completely eliminates bacterial retention under the tested conditions. The developed process provides a flexible approach to tailoring surface properties for specific applications requiring antimicrobial properties.

Multiscale enhancement of refrigerant falling film boiling by combining commercially enhanced tubes with nanostructures

Multiscale surface structures offer the opportunity to combine the heat transfer enhancement provided by microscale structures with the dryout benefits provided by some nanostructures, which is particularly attractive for falling film evaporators, who have to prevent dryout to ensure high heat transfer coefficients can be maintained.In this study commercially produced plain, low-finned and 3D enhanced commercially produced tubes were tested uncoated as well as coated with fibrous nanostructures that induce wicking on the surface to investigate the opportunity that multiscale enhancements might offer falling film boiling evaporators. Single tubes were heated internally by water and tested in the horizontal position with refrigerant R-134a drizzled over the outside of the tube surface at saturation temperatures of 5 and 25 °C, heat fluxes from 20 to 100 kW/m2 and film Reynolds numbers of up to 2000. The tubes tested were a roughened plain tube, a low-finned Gewa-KS tube and two different 3D enhanced tubes, an Gewa-B5 and an Turbo EHPII, that have networks of re-entrant cavities.The uncoated 3D enhanced tubes achieved the highest heat transfer coefficients, outperforming a roughened plain tube by up to 500 %. The uncoated low-finned tube had heat transfer coefficients that were up to 140 % higher than the roughened plain tube, despite having only an 80 % larger surface area, thought to be due to bubbles sliding down the channels between the fins, increasing the surface area of the bubbles in contact with the surface and thus allowing for increased microlayer evaporation.The application of the copper oxide coating benefitted the low-finned tube, with heat transfer enhancement of up to 60 %, possibly due to liquid wicked underneath the sliding bubbles and onto the fin tips. However, the nanocoating largely decreased the heat transfer of the roughened plain tube and the 3D enhanced tubes, thought to be due flooding of nucleation sites on the roughened tube and interference with the hydraulics of the re-entrant cavity network on the surface of the 3D enhanced surfaces.The low-finned tube also benefitted the most from the falling film boiling heat transfer mode compared to previous pool boiling results, with heat transfer increased by up to 80 and 460 % for the uncoated and coated low-finned tube respectively, again likely due to the bubbles sliding in the fin channels under falling film conditions.The Gewa-B5 tubes had the best dryout performance, followed by the Turbo EHPII tubes and then the low-finned Gewa-KS tube. The open pore structure of the Gewa-B5 likely aided it in dryout prevention, while the fins of the Gewa-KS are thought to have prevented lateral fluid distribution and thus worsen dryout.The dryout performance of the tubes we not markedly improved by the nanocoating, highlighting the difficulty in improving wettability on high wetting low surface tensions fluids such as refrigerants. Microscale enhancements were thus shown to have significant influence on dryout performance, while nanoscale enhancements had little effect in this study.

3D hot laser shock peening without coating based on Leidenfrost effect enables self-armored hydrophobic surface with enhanced fatigue properties

Hydrophobic metal surfaces have widespread applications in self-cleaning, ice removal, corrosion resistance, and drag reduction. The fine structure of these surfaces is crucial for their hydrophobic properties. Currently, pulse laser direct surface texturing is commonly employed to create hydrophobic surfaces, but this method primarily operates at room temperature. However, the rapid cooling rate and significant temperature gradient of the thin melted layer generated at room temperature often result in high stress concentration and cracks within the fine structure, compromising the overall integrity of the hydrophobic surface when subjected to complex external loads. To assess structural robustness by evaluating crack propagation, fatigue performance serves as a vital indicator. The thermal effect helps mitigate thermal cracking caused by direct laser irradiation and introduces an additional strengthening mechanism through dynamic strain aging. By utilizing the Leidenfrost effect, water can act as a confinement layer to achieve high temperature laser shock, which is expected to enhance the fatigue performance of hydrophobic structures. In this study, we propose a technique called 3D hot laser shock peening without coating (3HLSPwoC) as a cost-effective and efficient method to fabricate multi-scale self-armored hydrophobic surfaces with enhanced fatigue properties. We systematically discuss and analyze the wettability and mechanical properties of samples manufactured using different approaches. Furthermore, we reveal the underlying mechanism responsible for the fatigue performance improvement achieved through the 3HLSPwoC process, supported by molecular dynamics calculations and finite element simulations. Compared with the samples manufactured at room temperature, the surface of 3HLSPwoC samples exhibit greater plastic deformation, with no visible cracks on the surface. Additionally, these samples possess higher compressive residual stress and hardness. Notably, 3HLSPwoC samples demonstrate superior fatigue performance and enhanced durability of the hydrophobic properties. 3HLSPwoC has proved to be a novel process for manufacturing advanced hydrophobic surfaces with comprehensive mechanical properties.

A controllable interface design and manufacturing strategy for embossed glass hierarchical nano-lens

Constructing nanostructured surfaces on the macro/micro-scale optical lens is of great importance in the improvement of light transmittance, reflection, and surface functionality of optical devices. In this paper, a flexible and novel material interface design and manufacturing method for hierarchical macro/nano optical glass lenses is proposed. To verify the feasibility of the presented manufacturing strategy, the deformation history and flow driving mechanism of multiscale structured surfaces during second hot embossing were studied by simulations and experiments. The results showed that solid-like glass preform exhibits a higher nanoscale deformation resistance and elastic recovery, which can reduce the distortion levels of the nanostructured surface. At small scales, molecular confinement greatly reduces the tectonic deformation of nanostructured surface at relatively low applied heat and pressure. However, beyond a specific thermal energy threshold, nanostructured convex surfaces are easily compressed into a smooth flattened surface. By collaborative control of embossing conditions, the nano-surface integrity of multiscale structured surface can be ensured during second hot embossing. Compared to the smooth macroscale optical lens, the hierarchical macro/nano glass lens exhibited better optical transmittance and antireflection properties. The proposed design and manufacturing strategy provides a facile and flexible solution for building the hierarchical macro/micro/nanostructure functional optics.

Freezing Characteristics of a Water Droplet on a Multiscale Superhydrophobic Surface.

Superhydrophobic surfaces have the potential to retard ice formation owing to their super water-repellant nature arising from high static contact angle and low contact angle hysteresis. Most of the previous studies have focused on patterned surfaces with mono-scaled prismatic structures. In contrast, the freezing behavior on multiscaled rough superhydrophobic surfaces that are of practical significance is relatively little studied. This article presents, for the first time, the freezing dynamics of a water droplet interacting with multiscale fractal superhydrophobic surfaces which validates well with experimental measurements. It is shown that the dual effects of increased contact angle and poor interfacial conduction due to trapped air cavities within the roughness features of the superhydrophobic surface lead to increasing freezing time with increasing surface hydrophobicity, determined as a function of the fractal surface parameters. A comparison of the freezing dynamics of sessile droplets of identical contact angle on a smooth versus a rough superhydrophobic surface shows that interfacial asperity thermal resistance contributes to over 14% increase in the freeze time. It is further shown that by tailoring the multiscale characteristics, the freeze time may be increased by up to 7-fold compared to freezing on a smooth surface. The application of the numerical model to studying ice formation on several practical superhydrophobic surfaces of a range of metallic materials and fabrication methods is also discussed, which offers guidelines for the design of anti-icing surfaces in practice.

Assessing the Quality of Oxygen Plasma Focused Ion Beam (O-PFIB) Etching on Polypropylene Surfaces Using Secondary Electron Hyperspectral Imaging.

The development of Focused Ion Beam-Scanning Electron Microscopy (FIB-SEM) systems has provided significant advances in the processing and characterization of polymers. A fundamental understanding of ion-sample interactions is still missing despite FIB-SEM being routinely applied in microstructural analyses of polymers. This study applies Secondary Electron Hyperspectral Imaging to reveal oxygen and xenon plasma FIB interactions on the surface of a polymer (in this instance, polypropylene). Secondary Electron Hyperspectral Imaging (SEHI) is a technique housed within the SEM chamber that exhibits multiscale surface sensitivity with a high spatial resolution and the ability to identify carbon bonding present using low beam energies without requiring an Ultra High Vacuum (UHV). SEHI is made possible through the use of through-the-lens detectors (TLDs) to provide a low-pass SE collection of low primary electron beam energies and currents. SE images acquired over the same region of interest from different energy ranges are plotted to produce an SE spectrum. The data provided in this study provide evidence of SEHI's ability to be a valuable tool in the characterization of polymer surfaces post-PFIB etching, allowing for insights into both tailoring polymer processing FIB parameters and SEHI's ability to be used to monitor serial FIB polymer surfaces in situ.

Defining multi-scale surface roughness of a coral reef using a high-resolution LiDAR digital elevation model

A decline in coral reef surface roughness may indicate that a coral reef ecosystem is approaching functional collapse. This is because surface roughness underpins many critical ecosystem metrics, such as, live coral cover and high fish biomass. Yet, we understand very little about how surface roughness changes at ecosystem scales due to the limited spatial extents of coral reef ecological surveys. Here, we investigate the surface roughness of a coral reef across multiple spatial scales on a high-resolution Light Detection and Ranging (LiDAR) derived digital elevation model (DEM, 0.25 m cells). We found that complex features dominated regions with high roughness (e.g., spur and groove systems on the reef slope) and increased the average roughness of geomorphic zones that are otherwise flat and featureless (e.g., lagoonal patch reefs in the sandy deep lagoon). Each geomorphic zone had a unique signature with high values of roughness usually expressed over finer spatial scales and, conversely, lower values of roughness observed over broader spatial scales. We allocated each geomorphic zone into one of four roughness equivalent habitats (REHs 1–4) that share similar surface roughness properties but are not necessarily geographically contiguous regions. These results identify fine scale features and geomorphic zones that are important for providing roughness to coral reef systems, such as spur and groove systems and the fore-reef slope respectively. They also suggest that morphodynamic processes operating at broader spatial scales influence the physical structure of coral reef ecosystems. The quantification of coral reef surface roughness is becoming increasingly important due to the wider availability of high (<1 m) and hyper (<0.1 m) resolution DEMs. Our approach and findings here can be used to provide greater surface textural information for common coral reef geomorphic zones and aid future management and research efforts, such as, monitoring of coral reef ecosystem response to environmental change and reef restoration and adaptation programs.