Feature Size Research Articles

Drop Dynamics during Condensation on Superhydrophobic Surfaces in Vapor Shear Flow.

Accurate models for predicting drop dynamics, such as maximum drop departure sizes, are crucial for estimating heat transfer rates during condensation on superhydrophobic (SH) surfaces. Previous studies have focused on examining the heat transfer rates for SH surfaces under the influence of gravity or vapor flowing over the surface. This study investigates the impact of surface solid fraction and texture scale on drop mobility in a condensing environment with a humid air flow. Experiments recorded condensation with varying surface feature sizes from micro- to nano scale under different flow rates. Video analysis detected the drop-size distribution and maximum drop departure sizes. Particle image velocimetry (PIV) provided accurate shear force representations. Results showed that the maximum drop departure sizes decreased with lower surface solid fractions and higher flow rates. A force balance analysis revealed that coalescence-induced drop jumping aids drop departure. Different drop behaviors due to coalescence were linked to surface characteristics. The study quantified drop jump distances under shear force during condensation on SH surfaces, and found that jump distances increased when coalescing drop sizes were similar. Based on these findings, a method for tuning SH surfaces to control drop size at coalescence and departure was suggested. Drop mobility was measured in terms of Bond and Capillary numbers, showing a dependence on surface solid fraction and pitch size. By combining these into surface slip length, it was shown that drop mobility increases with increasing slip length.

Influence of Process Parameter and Build Rate Variations on Defect Formation in Laser Powder Bed Fusion SS316L.

Laser powder bed fusion (LPBF) is an additive manufacturing process that has gained interest for its material fabrication due to multiple advantages, such as the ability to print parts with small feature sizes, good mechanical properties, reduced material waste, etc. However, variations in the key process parameters in LPBF may result in the instantiation of porosity defects and variation in build rate. Particularly, volumetric energy density (VED) is a variable that encapsulates a number of those parameters and represents the amount of energy input from the laser source to the feedstock. VED has been traditionally used to inform the quality of the printed part but different values of VED are presented as optimal values for certain material systems. An optimal VED value can be maintained by changing the key process parameters so that various combinations yield a constant value. In this study, an optimal constant VED value is maintained while printing SS316L with variable key processing parameters. Porosity analysis is performed using optical microscopy, as well as X-ray computed tomography, to reveal the volume density and distribution of those pores. Two primary defect categories are identified, namely lack of fusion and porosity induced by balling defects. The findings indicate that, even at optimal VED, variations in process parameters can significantly influence defect type, underscoring the sensitivity of defect formation to the variation of these parameters. Furthermore, a minor change in the build rate, driven by adjustments in process parameters, was found to influence defect categories. These findings emphasize that fine tuning the process parameters and build rate is essential to minimize defects. Finally, fiducial marks have been identified as a source of unintentional porosity defects. These results enable the refinement of process parameters, ultimately optimizing LPBF to achieve enhanced material density and expedite the printing.

Effect of Feature Size on Defects, Microstructure, and Mechanical Properties of Selective Laser Melted AlSi10Mg Lattice Structure

Selective laser melting lightweight lattice structures have broad application prospects in the aerospace field. Understanding the dependence of mechanical performance on feature size is crucial for structure design. This work optimized the process parameters based on large-size metal blocks (20 mm) and then fabricated submillimeter features with a size of 0.4~1.0 mm. The influence of feature size on the defects, microstructures, and mechanical properties was investigated. The results showed that the dimensional errors for all size features were above 15%. When matched with appropriate border offset, these features could be printed precisely. The densification of submillimeter features was more than 99%, demonstrating the applicability of the optimized process parameters for the fine features. The porosity and relative roughness decreased and tended to stabilize with increasing feature size. Due to having less defects, the thicker features exhibited better mechanical properties in terms of ultimate strength and elongation. After being processed with polishing treatment, the roughness was reduced below 1 μm and the tensile strength increased above 320 MPa. The elastic modulus, yield strength, and elongation were also significantly improved.

Stable Air Plastron Prolongs Biofluid Repellency of Submerged Superhydrophobic Surfaces.

Superhydrophobic surfaces find applications in numerous biomedical scenarios, requiring the repellence of biofluids and biomolecules. Plastron, the trapped air between a superhydrophobic surface and a wetting liquid, plays a pivotal role in biofluid repellency. A key challenge, however, is the often short-lived plastron stability in biofluids and the lack of knowledge surrounding it. Plastron stability refers to the duration for which a surface remains in the Cassie state before transitioning to the fully wetting Wenzel state. Here, a submersion test with real-time optical monitoring is used to determine the plastron lifetime of different superhydrophobic surfaces upon immersion in various biofluids. We find that biofluids of all types exhibit shorter plastron lifetimes compared to pure water, which is attributed to their lower surface tension and biomolecular adsorption through hydrophobic-hydrophobic interactions. Proteins and glucose are identified as the major contributors to plastron dissipation in fetal bovine serum-based biofluids. Plastron minimizes the solid-liquid interface, reducing biomolecular adsorption, making its stability crucial for biofluid repellence. Thus, the effects of surface texture, feature size, Cassie solid fraction, Wenzel dimensionless roughness, and surface chemistry on plastron stability are investigated. Our key findings indicate that prolonged plastron stability and thus enhanced biofluid repellency are achieved through a combination of larger plastron volumes, increased Wenzel roughness degrees, greater Cassie solid fractions, and smaller feature sizes. We demonstrate that with optimized parameters, our surface design can maintain plastron stability and sustain a consistent solid-liquid area fraction for over 120 h in complex biofluids containing high levels of protein and glucose, underscoring a robust design for long-term use in biomedical and antifouling applications. This research is essential for advancing the design of superhydrophobic surfaces that effectively resist biofouling in diverse medical and engineering settings.

Rheological Behavior of Gassy Marine Clay: Coupling Effects of Bubbles and Salinity

Understanding the rheological behavior of marine clay is crucial to analyzing submarine landslides and their impact on marine resource exploitation. Dispersed bubbles in marine clay (gassy clay) and electrolytes in seawater (e.g., NaCl concentration of 0.47 M) significantly impacts rheological properties. Under low ionic strength and low pore water pressure conditions, dispersed bubbles have a strengthening effect on the yield stress and the viscosity of clays. This effect turns into a weakening effect when the pore water pressure reaches 300 kPa or the ionic strength exceeds 0.18 M. It was proposed that the effect of bubbles, whether strengthening or weakening, was determined by the size of bubbles with respect to the characteristic size of the particle structure formed by clay particles. A theoretical model was developed, which reasonably captures rheological behaviors of gassy clays.

Ring-Opening Polymerization of Surface Ligands Enables Versatile Optical Patterning and Form Factor Flexibility in Quantum Dot Assemblies.

The evolution of display technologies is rapidly transitioning from traditional screens to advanced augmented reality (AR)/virtual reality (VR) and wearable devices, where quantum dots (QDs) serve as crucial pure-color emitters. While solution processing efficiently forms QD solids, challenges emerge in subsequent stages, such as layer deposition, etching, and solvent immersion. These issues become especially pronounced when developing diverse form factors, necessitating innovative patterning methods that are both reversible and sustainable. Herein, a novel approach utilizing lipoic acid (LA) as a ligand is presented, featuring a carboxylic acid group for QD surface attachment and a reversible disulfide ring structure. Upon i-line UV exposure, the LA ligand initiates ring-opening polymerization (ROP), crosslinking the QDs and enhances their solvent resistance. This method enables precise full-color QD patterns with feature sizes as small as 3µm and pixel densities exceeding 3788 ppi. Additionally, it supports the fabrication of stretchable QD composites using LA-derived monomers. The reversible ROP process allows for flexibility, self-healing, and QD recovery, promoting sustainability and expanding QD applications for ultra-fine patterning and on-silicon displays.

Superconducting flip-chip devices using indium microspheres on Au-passivated Nb or NbN as under-bump metallization layer

Superconducting flip-chip interconnects are crucial for the three-dimensional integration of superconducting circuits in sensing and quantum technology applications. We demonstrate a simplified approach for a superconducting flip-chip device using commercially available indium microspheres and an in-house-built transfer stage for bonding two chips patterned with superconducting thin films. We use a gold-passivated niobium or niobium nitride layer as an under-bump metallization (UBM) layer between an aluminum-based superconducting wiring layer and the indium interconnect. At millikelvin temperatures, our flip-chip assembly can transport a supercurrent with tens of milliamperes, limited by the smallest geometric feature size and critical current density of the UBM layer and not by the indium interconnect. We show that the pressed indium interconnect itself can carry a supercurrent exceeding 1 A due to its large size of about 500 μm diameter. Our flip-chip assembly does require neither electroplating nor patterning of indium. The assembly process does not need a flip-chip bonder and can be realized with a transfer stage using a top chip with transparency or through-vias for alignment. These flip-chip devices can be utilized in applications that require few superconducting interconnects carrying large currents at millikelvin temperatures.

Preparing to mass produce photonic devices

AbstractOver more than a half century, the pace of innovation in electronic communication and computing has consistently increased, giving rise to progressively smaller silicon microchips with enhanced processing power. This achievement is attributed to the exponential growth in the density of integrated circuit (IC) transistors, a development predicted by Intel co‐founder Gordon Moore in 1965 and commonly called Moore's Law. However, there are inherent limits to reducing the physical feature size of silicon structures before quantum effects start influencing their functionality.

Application of the quantum effects of single-electron transistors in low-power

As the feature size of transistors approaches the 2nm limit, quantum effects become more pronounced as a result of devices that cannot work properly. Conventional MOSFET architectures are difficult to solve the problems. Therefore, new transistor architectures need to be developed. As a new transistor architecture, the single-electron transistor (SET) working based on quantum effects and has a very low power consumption. This paper analyzed working principle of single-electron transistor (SET) and compared the advantages of its extremely low power consumption in quantum computing with MOSFET. Although single-electron transistors (SET) have many advantages, there still are some challenges. Such as manufacturing technology, stability and reliability issues. As technology advances, single-electron transistor (SET) is expected to play an important role in the future of electronics.

Functionalization of Polymer Surfaces for Organic Photoresist Materials.

Photoresists are thin film materials designed to transform an optimal image into a mechanical mask. Diverse exposure techniques such as photolithography induce modifications in the exposed areas that result in solubility changes that can then be selectively removed with appropriate agents (developers). Photoresist materials need to keep pace with the increasingly demand for feature size reduction. Typically, photoresist materials are organic polymers that can present small cross sections to the incoming photons, resulting in poor trade-off between resolution, sensitivity, or line-edge roughness. Photoresists also require a high etch-resistance relative to the substrate material, in order to preserve patterned features after the mechanical mask has been created. The main strategy to improve polymer performance during such processes is functionalization with different elements that can deliver higher efficiencies while keeping the chemical reactivity of the original polymer design. Here we consider a photoresist polymer structure model with general characteristics and investigate the functionalization of the polymer surface using density-functional theory (DFT), with a focus on reactive halogen adsorption. Physical and chemical surface reactions and the corresponding byproducts are identified, obtaining self-limitation thresholds for each specific functionalizing agent. Moreover, spectral signals of the modified polymer surfaces are analyzed in detail, to allow experimental validation of the proposed surface modifications. Finally, using ab initio molecular dynamics (AIMD) and real time time-dependent DFT (rt-TDDFT), we study the interaction of energetic ions and electrons with the modified polymer surfaces, to validate the obtained functionalizations as effectively enhanced etch-resistance strategies. The computational results provide valuable insights on the complex physical, chemical, and dynamic ion and electron interactions with functionalized polymer photoresists, with the immediate consequence of allowing the extraction of definite strategies for improving photoresist polymer performance.

A practical approach to the spatial-domain calculation of nonprewhitening model observers in computed tomography.

Modern reconstruction algorithms for computed tomography (CT) can exhibit nonlinear properties, including non-stationarity of noise and contrast dependence of both noise and spatial resolution. Model observers have been recommended as a tool for the task-based assessment of image quality (Samei E etal., Med Phys. 2019; 46(11): e735-e756), but the common Fourier domain approach to their calculation assumes quasi-stationarity. A practical spatial-domain approach is proposed for the calculation of the nonprewhitening (NPW) family of model observers in CT, avoiding the disadvantages of the Fourier domain. The methodology avoids explicit estimation of a noise covariance matrix. A formula is also provided for the uncertainty on estimates of detectability index, for a given number of slices and repeat scans. The purpose of this work is to demonstrate the method and provide comparisons to the conventional Fourier approach for both iterative reconstruction (IR) and a deep Learning-based reconstruction (DLR) algorithm. Acquisitions were made on a Revolution CT scanner (GE Healthcare, Waukesha, Wisconsin, USA) and reconstructed using the vendor's IR and DLR algorithms (ASiR-V and TrueFidelity). Several reconstruction kernels were investigated (Standard, Lung, and Bone for IR and Standard for DLR). An in-house developed phantom with two flat contrast levels (2 and 8 mgI/mL) and varying feature size (1-10mm diameter) was used. Two single-energy protocols (80 and 120kV) were investigated with two dose levels (CTDIvol=5 and 13 mGy). The spatial domain calculations relied on repeated scanning, region-of-interest placement and simple operations with image matrices. No more repeat scans were utilized than required for Fourier domain estimations. Fourier domain calculations were made using techniques described in a previous publication (Thor D etal., Med Phys. 2023;50(5):2775-2786). Differences between the calculations in the two domains were assessed using the normalized root-mean-square discrepancy (NMRSD). Fourier domain calculations agreed closely with those in the spatial domain for all zero-strength IR reconstructions, which most closely resemble traditional filtered backprojection. The Fourier-based calculations, however, displayed higher detectability compared to those in the spatial domain for IR with strong iterative strength and for the DLR algorithm. The NRMSD remained within 10% for the NPW model observer without eye filter, but reached larger values when an eye filter was included. The formula for the uncertainty on the detectability index was validated by bootstrap estimates. A practical methodology was demonstrated for calculating NPW observers in the spatial domain. In addition to being a valuable tool for verifying the applicability of typical Fourier-based methodologies, it lends itself to routine calculations for features embedded in a phantom. Higher estimates of detectability were observed when adopting the Fourier domain methodology for IR and for a DLR algorithm, demonstrating that use of the Fourier domain can indicate greater benefit to noise suppression than suggested by spatial domain calculations. This is consistent with the results of previous authors for the Fourier domain, who have compared to human and other model observers, but not, as in this study, to the NPW model observer calculated in the spatial domain.

BIMSSA: enhancing cancer prediction with salp swarm optimization and ensemble machine learning approaches.

Cancer rates are rising rapidly, causing global mortality. According to the World Health Organization (WHO), 9.9 million people died from cancer in 2020. Machine learning (ML) helps identify cancer early, reducing deaths. An ML-based cancer diagnostic model can use the patient's genetic information, such as microarray data. Microarray data are high dimensional, which can degrade the performance of the ML-based models. For this, feature selection becomes essential. Swarm Optimization Algorithm (SSA), Improved Maximum Relevance and Minimum Redundancy (IMRMR), and Boruta form the basis of this work's ML-based model BIMSSA. The BIMSSA model implements a pipelined feature selection method to effectively handle high-dimensional microarray data. Initially, Boruta and IMRMR were applied to extract relevant gene expression aspects. Then, SSA was implemented to optimize feature size. To optimize feature space, five separate machine learning classifiers, Support Vector Machine (SVM), Random Forest (RF), Extreme Learning Machine (ELM), AdaBoost, and XGBoost, were applied as the base learners. Then, majority voting was used to build an ensemble of the top three algorithms. The ensemble ML-based model BIMSSA was evaluated using microarray data from four different cancer types: Adult acute lymphoblastic leukemia and Acute myelogenous leukemia (ALL-AML), Lymphoma, Mixed-lineage leukemia (MLL), and Small round blue cell tumors (SRBCT). In terms of accuracy, the proposed BIMSSA (Boruta + IMRMR + SSA) achieved 96.7% for ALL-AML, 96.2% for Lymphoma, 95.1% for MLL, and 97.1% for the SRBCT cancer datasets, according to the empirical evaluations. The results show that the proposed approach can accurately predict different forms of cancer, which is useful for both physicians and researchers.

Plastic pollution and marine mussels: Unravelling disparities in research efforts, biological effects and influences of global warming.

The ever-growing contamination of the environment by plastics is a major scientific and societal concern. Specifically, the study of microplastics (1μm to 5mm), nanoplastics (< 1μm), and their leachates is a critical research area as they have the potential to cause detrimental effects, especially when they impact key ecological species. Marine mussels, as ecosystem engineers and filter feeders, are particularly vulnerable to this type of pollution. In this study, we reviewed the 106 articles that focus on the impacts of plastic pollution on marine mussels. First, we examined the research efforts in terms of plastic characteristics (size, polymer, shape, and leachates) and exposure conditions (concentration, duration, species, life stages, and internal factors), their disparities, and their environmental relevance. Then, we provided an overview of the effects of plastics on mussels at each organisational levels, from the smaller scales (molecular, cellular, tissue and organ impacts) to the organism level (functional, physiological, and behavioural impacts) as well as larger-scale implications (associated community impacts). We finally discussed the limited research available on multi-stressor studies involving plastics, particularly in relation to temperature stress. We identified temperature as an underestimated factor that could shape the impacts of plastics, and proposed a roadmap for future research to address their combined effects. This review also highlights the impact of plastic pollution on mussels at multiple levels and emphasises the strong disparities in research effort and the need for more holistic research, notably through the consideration of multiple stressors, with a specific focus on temperature which is likely to become an increasingly relevant forcing factor in an era of global warming. By identifying critical gaps in current knowledge, we advocate for more coordinated interdisciplinary and international collaborations and raise awareness of the need for environmental coherence in the choice and implementation of experimental protocols.

Experimental study on the impact load characteristics of a single-row pile dam against debris flow

A single-row pile dam is a type of rigid debris flow barrier structure. In order to reveal the mutual interaction effects between debris flow and a single-row pile dam and the distribution law of debris flow impact forces, an analysis of the correlation between debris flow density, characteristic particle size, and relative pile spacing (the ratio of pile gap size to debris flow characteristic particle diameter) and debris flow impact forces was conducted to obtain dimensionless parameters related to debris flow impact force and climbing height. These parameters were verified through flume model experiments to provide theoretical support for the engineering design of single-row pile dams. The results show that the impact force of the debris flow on the pile dam is mainly affected by the flow velocity, debris flow density, and relative pile spacing. The dimensionless parameters are introduced, and the semi-empirical expression of the impact load coefficient is established. The impact pressure coefficient of debris flow is classified into two categories, and the calculation model of impact load of a single-row pile dam is constructed. The research results can provide a theoretical basis and technical parameters for designing a single-row pile dam.

Unlocking multiscale metallic metamaterials via lithography additive manufacturing

ABSTRACT Metamaterials possess properties not found in nature and are expected to revolutionise the design of structural components. However large-scale production of metallic metamaterials remains locked due to the compromise between print size and resolution in existing metal 3D printing methods. We unlock the possibility of 3D printing of stainless steel metamaterials across scales using lithography metal manufacturing, a vat photopolymerisation technology that uses digital light processing (DLP) on metal-filled resin to 3D print a green body for further debinding and sintering in a furnace. Here in, were explore the effects of energy dose on overpolymerisation, minimal feature size, and print resolution as well as the effects of sintering temperature on microstructure, shape stability, and mechanical properties of 3D printed metamaterials. It has become possible to 3D print steel metamaterials with a twist and auxetic metamaterials with micro-scaled structures on a decimetre scale. Our benchmarking experiments demonstrate that lithography metal manufacturing competes with laser powder bed fusion regarding print accuracy, surface roughness, and design freedom and provides a viable solution for translating metallic metamaterials from laboratories to markets.

A novel design strategy to enhance buckling resistance of thin-walled single-cell lattice structures via topology optimisation

ABSTRACT Lattice structures with thin walls or members are susceptible to instability due to their limited buckling strength. We propose a design methodology that utilises topology optimisation techniques to create lattice structure members with improved buckling resistance, demonstrated here on a single cell of a thin-walled square honeycomb lattice. We utilise the buckling mode of the single cell as a reference to inform the design of modified lattice structure members via optimisation, while ensuring the weight and stiffness of the structure remain unchanged. We apply a 2D topology optimisation method with the objective of maximising buckling load factors, while simultaneously enforcing constraints on stiffness and volume fraction within the optimisation domain. A family of designs is created via a parametric study, considering optimisation parameters like the thickness of the buckling member and the active domain. Designing and fabricating these structures also consider additive manufacturing constraints, such as minimal feature size and overhang. Experimental analysis of fabricated structures reveals a remarkable 70% increase in buckling strength without altering stiffness or weight. Numerical simulations corroborate these findings, with a discrepancy of less than 10%. This methodology shows the potential to enhance buckling resistance of thin-walled lattices in various lattice types while maintaining base cell stiffness.

A Comparative Study between Thiol-Ene and Acrylate Photocrosslinkable Hyaluronic Acid Hydrogel Inks for Digital Light Processing.

Photocrosslinkable formulations based on the radical thiol-ene reaction are considered better alternatives than methacrylated counterparts for light-based fabrication processes. This study quantifies differences between thiol-ene and methacrylated crosslinked hydrogels in terms of precursors stability, the control of the crosslinking process, and the resolution of printed features particularized for hyaluronic acid (HA) inks at concentrations relevant for bioprinting. First, the synthesis of HA functionalized with norbornene, allyl ether, or methacrylate groups with the same molecular weight and comparable degrees of functionalization is presented. The thiol-ene hydrogel precursors show storage stability over 15 months, 3.8 times higher than the methacrylated derivative. Photorheology experiments demonstrate up to 4.7-times faster photocrosslinking. Network formation in photoinitiated thiol-ene HA crosslinking allows higher temporal control than in methacrylated HA, which shows long post-illumination hardening. Using digital light processing, 4% w/v HA hydrogels crosslinked with a dithiol allowed printing of 13.5 × 4 × 1mm3 layers with holes of 100µm resolution within 2 s. This is the smallest feature size demonstrated in DLP printing with HA-based thiol-ene hydrogels. The results are important to estimate the extent to which the synthetic effort of introducing -ene functions can pay off in the printing step.

Conductive and convective combustion modes of granular mixtures of Ti–C–NiCr

The combustion modes of powder and granular mixtures (100 – X)(Ti + C) + XNiCr (X = 0–30%) containing Ti powders of different dispersion with different amounts of impurity gases in them were investigated. The experimental setup provided filtration of impurity gases released during combustion in the cocurrent direction or through the side surface of the sample. The difference between the experimental burning velocities of powder mixtures with titanium of different fineness is explained using a convective-conductive combustion model. For granular mixtures based on Ti powder with a characteristic size of 120 μm, it was shown that combustion occurs in the conductive mode. Comparison of the combustion velocities of granular mixtures containing Ti powder with particles of a characteristic size of 60 μm in the absence and presence of gas filtration through the sample indicates the transition of combustion to the convective regime. The necessary and sufficient conditions for the transition from conductive to convective combustion are formulated, which makes it possible to determine the composition of the mixture whose combustion occurs in the boundary region. In mixtures based on Ti with a particle size of 60 μm, the conductive combustion regime is observed during the combustion of granules 0.6 mm in size and a mixture with X = 30% of granules 1.7 mm in size. For mixtures with X = 0–20% with granules 1.7 mm in size, burning in the convective regime, the interfacial heat transfer coefficients were evaluated using experimental data. Their values are more than an order of magnitude higher than the theoretical ones. The XPA results of the combustion products showed that in order to obtain synthesis products without side phases of intermetallic compounds, it is necessary to use finely dispersed titanium powder.

Deep Learning for Melanoma Detection: A Deep Learning Approach to Differentiating Malignant Melanoma from Benign Melanocytic Nevi

Background/Objectives: Melanoma, an aggressive form of skin cancer, accounts for a significant proportion of skin-cancer-related deaths worldwide. Early and accurate differentiation between melanoma and benign melanocytic nevi is critical for improving survival rates but remains challenging because of diagnostic variability. Convolutional neural networks (CNNs) have shown promise in automating melanoma detection with accuracy comparable to expert dermatologists. This study evaluates and compares the performance of four CNN architectures—DenseNet121, ResNet50V2, NASNetMobile, and MobileNetV2—for the binary classification of dermoscopic images. Methods: A dataset of 8825 dermoscopic images from DermNet was standardized and divided into training (80%), validation (10%), and testing (10%) subsets. Image augmentation techniques were applied to enhance model generalizability. The CNN architectures were pre-trained on ImageNet and customized for binary classification. Models were trained using the Adam optimizer and evaluated based on accuracy, area under the receiver operating characteristic curve (AUC-ROC), inference time, and model size. The statistical significance of the differences was assessed using McNemar’s test. Results: DenseNet121 achieved the highest accuracy (92.30%) and an AUC of 0.951, while ResNet50V2 recorded the highest AUC (0.957). MobileNetV2 combined efficiency with competitive performance, achieving a 92.19% accuracy, the smallest model size (9.89 MB), and the fastest inference time (23.46 ms). NASNetMobile, despite its compact size, had a slower inference time (108.67 ms), and slightly lower accuracy (90.94%). Performance differences among the models were statistically significant (p &lt; 0.0001). Conclusions: DenseNet121 demonstrated a superior diagnostic performance, while MobileNetV2 provided the most efficient solution for deployment in resource-constrained settings. The CNNs show substantial potential for improving melanoma detection in clinical and mobile applications.

Unconventional Photo‐Control of Structural Features Using Elliptically Polarized Light

AbstractThe holographic interference of either same‐handed or orthogonal polarized beams is commonly employed in the fabrication of optical elements as it enables the development of an ideal sinusoidal surface modulation by inducing the mass migration of photochromic polymer. However, this approach encounters challenges in producing multiplexed optical elements with a complex surface topography, primarily due to only sinusoidal surface topography which limits control over the feature's shape and size. Here, a versatile approach is demonstrated for fabricating spatially multiplexed optical elements using ellipticity‐controlled orthogonally elliptically polarized (OEP) beams that offer distorted square‐like and rhombus‐like pillars on the azopolymer layer by asymmetric azopolymer migration driven by spatially varying ellipticity and azimuth angle of interference beam. These multiplexed distorted shapes enable selective energy transfer during light diffraction and also induce a phase‐shift to polarization states. Ellipticity control of both writing and interference beams provides an additional degree of freedom for structuring the surface topography of optical elements. Furthermore, a quantitative analysis of the multiplexed OEP surface relief is performed to achieve distinct diffraction properties, thereby rendering its suitability for fabricating complex optical elements.