Patterning Technique Research Articles

Versatile Patterning of Liquid Metal via Multiphase 3D Printing.

This paper presents a scalable and straightforward technique for the immediate patterning of liquid metal/polymer composites via multiphase 3D printing. Capitalizing on the polymer's capacity to confine liquid metal (LM) into diverse patterns. The interplay between distinctive fluidic properties of liquid metal and its self-passivating oxide layer within an oxidative environment ensures a resilient interface with the polymer matrix. This study introduces an inventive approach for achieving versatile patterns in eutectic gallium indium (EGaIn), a gallium alloy. The efficacy of pattern formation hinges on nozzle's design and internal geometry, which govern multiphase interaction. The interplay between EGaIn and polymer within the nozzle channels, regulated by variables such as traverse speed and material flow pressure, leads to periodic patterns. These patterns, when encapsulated within a dielectric polymer polyvinyl alcohol (PVA), exhibit an augmented inherent capacitance in capacitor assemblies. This discovery not only unveils the potential for cost-effective and highly sensitive capacitive pressure sensors but also underscores prospective applications of these novel patterns in precise motion detection, including heart rate monitoring, and comprehensive analysis of gait profiles. The amalgamation of advanced materials and intricate patterning techniques presents a transformative prospect in the domains of wearable sensing and comprehensive human motion analysis.

Implementation of high‐performance, freestanding flexible film masks through photosensitive polyimide for arbitrary surface micropatterns creation

AbstractGiven the widespread presence of intricate surfaces, the development of electronics has generated a significant demand for surface patterning techniques capable of creating refined or novel patterns. Nevertheless, present surface patterning techniques suffer from complex processes, limited resolution, stringent conditions, and high manufacturing costs. Herein, we present a novel approach for arbitrary surface micropatterning using photosensitive polyimide (PSPI), enabling the in situ fabrication of electrodes without the need for a pattern‐transferring process. On this basis, we have implemented a high‐performance, freestanding flexible thin‐film mask with high optical transparency that facilitates precise alignment of microelectrode patterns with the target material. It also exhibits exceptional mechanical properties suitable for long‐term use and high‐temperature applications, with a notable glass transition temperature of up to 300°C. The fabricated masks with thicknesses of 5–20 μm are well‐suited for high‐resolution applications, including those requiring sub‐5 μm resolution. Furthermore, the creation of microelectrodes on a variety of surfaces utilizing the fabricated PSPI masks was successfully demonstrated. Our facile method provides a solid foundation for achieving efficient micropatterning for the fabrication of high‐performance flexible electronics on complex surfaces.

Moving Meniscus-Assisted Template-Free Optothermofluidic Nanoparticle Patterning and Its Application in Optothermoconvective Particle Trapping.

Moving meniscus-assisted vertical lifting is a commonly employed particle assembly technique to realize large-area particle patterning for the easy fabrication of colloidal photonic crystals and sensors. Though great success has been achieved for large-area patterning, inscribing desired patterns over the target substrate with precise control over the morphology remains a challenge. The target substrates need to be functionalized (physically or chemically) to realize desired patterns, which increases the complexity and limits their applicability to specific particle-liquid combinations. We demonstrate a new approach for the precise patterning of gold nanoparticles (Au NPs, diameter ∼60 nm) over solid substrates by the synergy of light-induced Marangoni flow and vertical lifting process (moving meniscus), without the requirement of photomasks or templates. The core idea relies on the particle accumulation due to light-induced Marangoni flow near the liquid meniscus in contact with a solid surface (due to plasmonic absorption of the particles) and the controlled lifting of the substrate. We present both the simulation and experimental results of the developed patterning technique. Various patterns such as continuous lines, intermittent lines with varying lengths, patterns with continuously varying widths, cross patterns, etc. are successfully inscribed. Dynamic control over the three-dimensional morphology of the deposited patterns is achieved by varying the lifting velocity, laser irradiation time, and lifting direction during the inscription process. Finally, we show the applicability of the developed plasmonically active surface for the large-area parallel manipulation of nonabsorbing microparticles based on optothermoconvective flow. The major advantage of the developed method compared to the existing light-controlled patterning techniques is its ability to inscribe patterns over large distances (up to several centimeters). We expect that the results presented in this paper will benefit different applications requiring precise particle patterning, such as optical elements, sensors, plasmonic substrates, microfluidic master templates, and electronic circuits.

Automated asthma detection in a 1326-subject cohort using a one-dimensional attractive-and-repulsive center-symmetric local binary pattern technique with cough sounds

Asthma is a common disease. The clinical diagnosis is usually confirmed on a pulmonary function test, which is not always readily accessible. We aimed to develop a computationally lightweight handcrafted machine learning model for asthma detection based on cough sounds recorded using mobile phones. Toward this aim, we proposed a novel feature extractor based on a one-dimensional version of the published attractive-and-repulsive center-symmetric local binary pattern (1D-ARCSLBP), which we tested on a new cough sound dataset. We prospectively recorded cough sounds from 511 asthmatics and 815 non-asthmatic subjects (comprising mostly healthy volunteers), which yielded 1875 one-second cough sound segments for analysis. Our model comprised four steps: (i) preprocessing, in which speech signals and stop times (silent zones between coughs) were removed, leaving behind analyzable cough sound segments; (ii) feature extraction, in which tunable q-factor wavelet transformation was used to perform multilevel signal decomposition into wavelet subbands, allowing 1D-ARCSLBP to extract local low- and high-level features; (iii) feature selection, in which neighborhood component analysis was used to select the most discriminative features; and (iv) classification, in which a standard shallow cubic support vector machine was deployed to calculate binary classification results (asthma versus non-asthma) using tenfold and leave-one-subject-out cross-validations. Our model attained 98.24% and 96.91% accuracy rates with tenfold and leave-one-subject-out cross-validation strategies, respectively, and obtained a low-time complexity. The excellent results confirmed the feature extraction capability of 1D-ARCSLBP and the feasibility of the model being developed into a real-world application for asthma screening.

P‐208: Ultra High‐resolution patterning technology with Fine silicon mask

Side by side RGB OLEDs‐on‐Silicon backplane holds great promise for high brightness, high resolution and low cost Micro OLED displays. The main challenge of vacuum thermal evaporation (VTE) patterning technique is to get a submricon‐size shadow distance. Herein, we have explored the relation of shadow distance with mask‐substrate gap, as well as the relationship of shadow distance with evaporation angle. The achievements verify the possibility of manufacturing the ultra‐high resolution side by side RGB AMOLEDs with >4000ppi. The study of ultra‐high resolution evaporation patterning need to be expanded for the OLED microdisplays futures.

Management of Meniscus Pathology with Concomitant Anterior Cruciate Ligament Injury.

The purpose of this review is to summarize current clinical knowledge on the prevalence and types of meniscus pathology seen with concomitant anterior cruciate ligament (ACL) injury, as well as surgical techniques, clinical outcomes, and rehabilitation following operative management of these pathologies. Meniscus pathology with concomitant ACL injury is relatively common, with reports of meniscus pathology identified in 21-64% of operative ACL injuries. These concomitant injuries have been associated with increased age and body mass index. Lateral meniscus pathology is more common in acute ACL injury, while medial meniscus pathology is more typical in chronic ACL deficiency. Meniscus tear patterns associated with concomitant ACL injury include meniscus root tears, lateral meniscus oblique radial tears of the posterior horn (14%), and ramp lesions of the medial meniscus (8-24%). These meniscal pathologies with concomitant ACL injury are associated with increased rotational laxity and meniscal extrusion. There is a paucity of comparative studies to determine the optimal meniscus repair technique, as well as rehabilitation protocol, depending on specific tear pattern, location, and ACL reconstruction technique. There has been a substantial increase in recent publications demonstrating the importance of meniscus repair at the time of ACL repair or reconstruction to restore knee biomechanics and reduce the risk of progressive osteoarthritic degeneration. Through these studies, there has been a growing understanding of the meniscus tear patterns commonly identified or nearly missed during ACL reconstruction. Surgical management of meniscal pathology with concomitant ACL injury implements the same principles as utilized in the setting of isolated meniscus repair alone: anatomic reduction, biologic preparation and augmentation, and circumferential compression. Advances in repair techniques have demonstrated promising clinical outcomes, and the ability to restore and preserve the meniscus in pathologies previously deemed irreparable. Further research to determine the optimal surgical technique for specific tear patterns, as well as rehabilitation protocols for meniscus pathology with concomitant ACL injury, is warranted.

Blindly separated spontaneous network-level oscillations predict corticospinal excitability

Objective. The corticospinal responses of the motor network to transcranial magnetic stimulation (TMS) are highly variable. While often regarded as noise, this variability provides a way of probing dynamic brain states related to excitability. We aimed to uncover spontaneously occurring cortical states that alter corticospinal excitability. Approach. Electroencephalography (EEG) recorded during TMS registers fast neural dynamics—unfortunately, at the cost of anatomical precision. We employed analytic Common Spatial Patterns technique to derive excitability-related cortical activity from pre-TMS EEG signals while overcoming spatial specificity issues. Main results. High corticospinal excitability was predicted by alpha-band activity, localized adjacent to the stimulated left motor cortex, and suggesting a travelling wave-like phenomenon towards frontal regions. Low excitability was predicted by alpha-band activity localized in the medial parietal–occipital and frontal cortical regions. Significance. We established a data-driven approach for uncovering network-level neural activity that modulates TMS effects. It requires no prior anatomical assumptions, while being physiologically interpretable, and can be employed in both exploratory investigation and brain state-dependent stimulation.

Effective prediction of human skin cancer using stacking based ensemble deep learning algorithm

ABSTRACT Automated diagnosis of cancer from skin lesion data has been the focus of numerous research. Despite that it can be challenging to interpret these images because of features like colour illumination changes, variation in the sizes and forms of the lesions. To tackle these problems, the proposed model develops an ensemble of deep learning techniques for skin cancer diagnosis. Initially, skin imaging data are collected and preprocessed using resizing and anisotropic diffusion to enhance the quality of the image. Preprocessed images are fed into the Fuzzy-C-Means clustering technique to segment the region of diseases. Stacking-based ensemble deep learning approach is used for classification and the LSTM acts as a meta-classifier. Deep Neural Network (DNN) and Convolutional Neural Network (CNN) are used as input for LSTM. This segmented images are utilized to be input into the CNN, and the local binary pattern (LBP) technique is employed to extract DNN features from the segments of the image. The output from these two classifiers will be fed into the LSTM Meta classifier. This LSTM classifies the input data and predicts the skin cancer disease. The proposed approach had a greater accuracy of 97%. Hence, the developed model accurately predicts skin cancer disease.

Opportunity of Patterning in Chemistry.

Patterning offers an efficient way to quantitatively enhance and enlarge material properties and functionalities, offering unprecedented opportunities for innovation in various scientific domains. By precisely controlling the spatial arrangement of materials at the micro- and nanoscale, patterning enables the exploitation of inherent material properties in novel ways. In addition, it generates new properties, leading to the development of advanced devices and applications. This article highlights the significant contributions of spatially controlled patterning in chemistry, particularly in generating new functional properties and devices, discussing some representative articles. Examples include the use of unconventional patterning techniques for surface functionalization, as well as the application of spatial confinement in improving material properties and controlling crystallization processes. Furthermore, the discussion extends to creating new devices, such as optical storage media and sensors, through spatial organization of materials.

Dual-Mode Patterns Enabled by Photofluidization of an Azobenzene-Containing Linear Liquid Crystal Copolymer.

Creating dual-mode patterns in the same area of the material is an advanced method to increase the dimension of information storage, improve the level of encryption security, and promote the development of encoding technology. However, in situ, different patterns may lead to serious mutual interference in the process of manufacturing and usage. New materials and patterning techniques are essential for the advancement of noninterfering dual-mode patterns. Herein, noninterfering dual-mode patterns are demonstrated by combining the structural color and chromatic polarization, which is designed with an azobenzene-containing linear liquid crystal copolymer featuring a photofluidization effect. On the one hand, structural color patterns are imprinted via silicon templates with periodic microstructures after a UV-light-induced local transition of the polymer surface from a glassy to rubbery state. On the other hand, different polarization patterns based on the local photoinduced orientation of mesogens are created within the photofluidized region by the Weigert effect. Especially, the secondary imprinting is used to eliminate the partial damage to the structural color patterns during writing of the polarization patterns, thus obtaining dual-mode patterns without interference. This study provides a blueprint for the creation of advanced materials and sophisticated photopatterning techniques with potential cross-industry applications.

The Use of Dixon Magnetic Resonance Imaging Methods for the Quantification of Rotator Cuff Fatty Infiltration: A Systematic Review

Fatty infiltration of the rotator cuff muscles is very common following rotator cuff tears and is one of the most important factors in determining treatment. Current clinical practice relies on subjective evaluation of fatty infiltration through categorical scoring based on the Goutallier classification system. The Dixon magnetic resonance imaging (MRI) sequence provides flexibility in selecting echo times for water–fat separation. The Dixon method, therefore, has the potential to provide robust and high-quality fat quantification that allows for more accurate calculation of fat fraction (%Fat) of the rotator cuff muscles than the Goutallier classification system. However, significant variance exists in sequencing and post-processing methodology within the recent application of Dixon sequences to quantify rotator cuff fatty infiltration. In this paper, we conducted a systematic review to synthesize the relevant literature utilizing Dixon sequencing for the quantification of rotator cuff fatty infiltration. The literature search was extracted from 1094 articles, with 12 studies included in the final review. Regardless of the varying sequencing pattern and post-processing techniques among studies, the findings suggest the Dixon method is reliable for quantitatively calculating the fat fraction of the rotator cuff muscles, even at very low levels of fatty infiltration. In addition, a quantitative difference in fat fraction was observed between participants with different degrees of tear vs. those without any shoulder pathologies. Multi-point Dixon imaging has the potential to be utilized clinically to objectively quantify fatty infiltration and may lead to improved clinical decision making for patients with rotator cuff tears.

Cellular Patterning Alone Using Bioprinting Regenerates Articular Cartilage Through Native-Like Cartilagenesis.

Few studies have proved that bioprinting itself helps recapitulate native tissue functions mainly because the bioprinted macro shape can rarely, if ever, influence cell function. This can be more problematic in bioprinting cartilage, generally considered more challenging to engineer. Here a new method is shown to micro-pattern chondrocytes within bioprinted sub-millimeter micro tissues, denoted as patterned micro-articular-cartilages tissues (PA-MCTs). Under the sole influence of bioprinted cellular patterns. A pattern scoring system is developed after over 600 bioprinted cellular patterns are analyzed. The top-scored pattern mimics that of the isogenous group in native articular cartilage. Under the sole influence of this pattern during PA-MCTs bio-assembling into macro-cartilage and repairing cartilage defects, chondrogenic cell phenotype is preserved, and cartilagenesis is initiated and maintained. Neocartilage tissues from individual and assembled PA-MCTs are comparable to native articular cartilage and superior to cartilage bioprinted with homogeneously distributed cells in morphology, biochemical components, cartilage-specific protein and gene expression, mechanical properties, integration with host tissues, zonation forming and stem cell chondrogenesis. PA-MCTs can also be used as osteoarthritic and healthy cartilage models for therapeutic drug screening and cartilage development studies. This cellular patterning technique can pave a new way for bioprinting to recapitulate native tissue functions via tissue genesis.

Controlled Anisotropic Wetting by Plasma Treatment for Directed Self-Assembly of High-χ Block Copolymers.

The directed self-assembly (DSA) of block copolymers (BCPs) is a promising next-generation lithography technique for high-resolution patterning. However, achieving lithographically applicable BCP organization such as out-of-plane lamellae requires proper tuning of interfacial energies between the BCP domains and the substrate, which remains difficult to address effectively and efficiently with high-χ BCPs. Here, we present the successful generation of anisotropic wetting by plasma treatment on patterned spin-on-carbon (SOC) substrates and its application to the DSA of a high-χ Si-containing material, poly(1,1-dimethylsilacyclobutane)-block-polystyrene (PDMSB-b-PS), with a 9 nm half pitch. Exposing the SOC substrate to different plasma chemistries promotes the vertical alignment of the PDMSB-b-PS lamellae within the trenches. In particular, a patterned substrate treated with HBr/O2 plasma gives both a neutral wetting at the bottom interface and a strong PS-affine wetting at the sidewalls of the SOC trenches to efficiently guide the vertical BCP lamellae. Furthermore, prolonged exposure to HBr/O2 plasma enables an adjustment of the trench width and an increased density of BCP lines on the substrate. Experimental observations are in agreement with a free energy configurational model developed to describe the system. These advances, which could be easily implemented in industry, could contribute to the wider adoption of self-assembly techniques in microelectronics, and beyond to applications such as metasurfaces, surface-enhanced Raman spectroscopy, and sensing technologies.

Defined Transfer of Colloidal Particles by Electrochemical Microcontact Printing

AbstractSoft lithography, in particular microcontact printing (µCP), represents a well‐established and widespread class of lithographic patterning techniques. It is based on a directed deposition of molecules or colloidal particles by a transfer process with a micro‐structured stamp. A critical aspect of µCP is the adhesion cascade that enables the directed transfer of the objects. Here, the interfacial properties of a µCP‐stamp are tuned electrochemically to modify the adhesion cascade. During the printing process, the µCP‐stamp is submerged in an electrolyte solution and acted as a working electrode whose surface properties depended on the externally applied potential, thus enabling in situ rapid switching of its adhesion properties. As a proof of principle, defined particle patterns are selectively removed from a monolayer of colloidal particles. The adhesion at the particle/solid interface and the transfer mechanisms are determined by using the colloidal probe technique based on atomic force microscopy (AFM). In this case, a single particle is brought into contact with an electrode with the same surface chemistry as the µCP‐stamp. Hence, it became possible to determine the electrochemical potential ranges suitable to establish an adhesion cascade.

DNA Steganography: Embedding the Secret Messages Using Glass Stack Method and Detecting Errors Made by Different Attacks

Cyber threats pose a significant challenge to protecting cloud-based health data, including DNA sequences and patient information. Steganography is a well-known solution to this problem. Recent studies have limitations, such as not reconstructing the cover or enlarging the original sequence, increasing personal data security risks. In this study, we provide a novel data-hiding technique that uses a secret key, bit padding, and exclusive OR (XOR) operation to insert secrets into a pre-processed DNA sequence. The message encryption and pre-processing of the cover are performed using a new glass stack pattern technique. The message encryption and pre-processing processes using the new glass stack pattern method increase the security and robustness of the system. The method embeds 3 bits per nucleotide which is greater than the existing schemes. It has a relatively small DNA expansion which is 50% and has a low risk of breaking security. The method also preserves the biological properties of DNA sequences. Additionally, a parity check approach further improves security by thwarting attacks like man-in-the-middle, chosen stego, and modification attacks that alter the stego DNA. These advances improve the system's overall security capabilities. The experimental findings demonstrate that the suggested method reconstructs the cover, healthcare data, and DNA sequence properly and error-free, and outperforms competing algorithms on all performance metric dimensions. Highlights The scheme implants a maximum of 3 bits per nucleotide with a small expansion. Its cover pre-processing and message encryption using a glass stack pattern makes strong security. The scheme can detect and fix changed bits caused by attacks. A very low cracking probability shows strong security and robustness. The biological properties of the stego DNA sequence are preserved by this scheme.

Advancing Engineered Plant Living Materials through Tobacco BY-2 Cell Growth and Transfection within Tailored Granular Hydrogel Scaffolds.

In this study, an innovative approach is presented in the field of engineered plant living materials (EPLMs), leveraging a sophisticated interplay between synthetic biology and engineering. We detail a 3D bioprinting technique for the precise spatial patterning and genetic transformation of the tobacco BY-2 cell line within custom-engineered granular hydrogel scaffolds. Our methodology involves the integration of biocompatible hydrogel microparticles (HMPs) primed for 3D bioprinting with Agrobacterium tumefaciens capable of plant cell transfection, serving as the backbone for the simultaneous growth and transformation of tobacco BY-2 cells. This system facilitates the concurrent growth and genetic modification of tobacco BY-2 cells within our specially designed scaffolds. These scaffolds enable the cells to develop into predefined patterns while remaining conducive to the uptake of exogenous DNA. We showcase the versatility of this technology by fabricating EPLMs with unique structural and functional properties, exemplified by EPLMs exhibiting distinct pigmentation patterns. These patterns are achieved through the integration of the betalain biosynthetic pathway into tobacco BY-2 cells. Overall, our study represents a groundbreaking shift in the convergence of materials science and plant synthetic biology, offering promising avenues for the evolution of sustainable, adaptive, and responsive living material systems.

Prediction and modeling of tie-dye polyester fabric's texture and color feature in supercritical carbon dioxide

To mitigate the water-intensive challenges of tie-dye clothing production and address the absence of objective evaluation for finished products, we have explored the correlation between tie-dye techniques in supercritical carbon dioxide (SC-CO2) and image patterns. Employing digital image processing, we extracted the average HSV three-component value of the valid tie-dye area, the proportion of incompletely dyed area in the HSV color space, and the initial three texture features of the digital tie-dye image using the Tamura method. We scrutinized five factors in the supercritical CO2 dyeing process: rotational speed, dyeing temperature, dyeing pressure, dye concentration, and dyeing time. The single-factor climbing test revealed that temperature and pressure are the predominant factors influencing tie-dyeing performance. We propose a correlation between the texture parameters of tie-dyeing and the swelling effect of polyester fabric in a supercritical CO2 environment. Subsequently, we conducted two-factor tests on temperature and pressure to investigate their specific effects on the parameters of supercritical polyester tie-dyeing. The results indicated significant changes in regularity, saturation, and the proportion of undyed area with variations in dyeing temperature and pressure. Building upon these findings, we developed an artificial neural network for predicting tie-dye dyeing temperature and pressure in supercritical CO2, utilizing features regularity, saturation, and proportion of undyed area as inputs. In the 105st round, experimental results demonstrated an accuracy of 0.99445, substantiating the efficacy of tie-dye pattern features in supercritical CO2 for technique forecasting.

Dynamic Polarization Patterning Technique for High-Quality Liquid Crystal Planar Optics

The Pancharatnam–Berry (PB)-phase liquid crystal (LC) planar optical elements, featuring large apertures and a light weight, are emerging as the new generation optics. The primary method for fabricating large-aperture LC planar optical elements is through photo-alignment, utilizing polarization laser direct writing. However, conventional polarization direct writing suffers from an inertia-induced stopping step during splicing, leading to suboptimal optical effects. Here, we propose a novel highly efficient method for arbitrary polarization patterning, significantly reducing interface splicing errors in these optical elements. (We call it dynamic polarization patterning technology). This process involves simultaneous mobile splicing and real-time generation of different polarization patterns for exposure, eliminating the inertia-related splicing interruption. As a demonstration, we fabricated a lens with an aperture of approximately 1 cm within 30 min at 633 nm. Furthermore, we developed a 100% fill-factor lens array (3 × 3) with an element lens diameter of approximately 7 mm within 1.5 h at 532 nm. Their focal lengths were uniformly set at 30 cm, demonstrating superior convergence capabilities within their designated working wavelengths, alongside commendable performance in converging light across various other wavelengths. Our measurements confirmed the good focusing performance of these samples. The convergence spot size of the lens deviated by approximately 40% from the theoretical diffraction limit, whereas the lens array exhibited a deviation of around 30%. The dynamic polarization direct writing during uniform platform movement reduced splicing errors to a mere 100–200 nm. The enhancement in imaging quality can be primarily attributed to the innovative use of mobile polarization splicing exposure technology, coupled with the inherent self-smoothing properties of LC molecules. This synergy significantly mitigates the impact of seam diffraction interference.

Real-time Hazard Detection in Autonomous Vehicles: Deep Learning Approaches for Enhancing Road Safety

This examination explores ongoing risk location in independent vehicles through the use of profound learning philosophies, expecting to upgrade street security. Utilizing Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), the review centers around distinguishing and grouping dangers like people on foot, vehicles, obstructions, and street abnormalities in assorted driving situations. By examining information from locally available sensors including cameras, LiDAR, and radar, the profound learning models learn perplexing examples and portrayals, empowering them to settle on informed choices in powerful conditions. The trial results exhibit the viability of the proposed approaches, with CNN-based and RNN-based models accomplishing high exactness, accuracy, review, and F1-score upsides of 0.95, 0.93, 0.96, and 0.94 individually. These outcomes outperform those of conventional AI calculations and pattern techniques, displaying the predominance of profound learning in danger identification undertakings. Also, examinations with related work highlight the headways accomplished, featuring the capability of profoundly figuring out how to upgrade the well-being abilities of independent vehicles. In general, this examination adds to the continuous endeavors in creating solid and effective danger recognition frameworks, making ready for more secure and more dependable independent driving innovation.

Prolonged pattern coarsening in ion irradiated swinging Si substrates

Low energy ion beams are regarded as an effective means to spatio-temporally pattern a wide variety of surfaces that exhibit intriguing motifs. In this work, an unconventional technique of surface patterning has been demonstrated by studying the topographical evolution of a Si(100) surface in the presence of azimuthal sample swinging (Δϕ=20∘) under 500 eV Ar+irradiation with a polar incidence angle of 67°. A prolonged wavelength coarsening is also noticed for the swinging sample. Ripple anticoarsening is observed for static sample at high fluences. The linear and nonlinear growth regimes are found to bear a direct consequence of this unconventional geometry. Furthermore, two-dimensional slope distributions of the surface morphologies clearly indicate reduced asymmetries in the surface structures. The present geometry brings in an additional factor of differential exposure and redeposition effects in addition to the near-surface mass redistribution, the former being inconceivable in the static configuration. This study establishes the role of this unconventional yet simple method to precisely modify and control surface asymmetries and influence regime shifts.