Detailed Surface Research Articles

DebSDF: Delving Into the Details and Bias of Neural Indoor Scene Reconstruction.

In recent years, the neural implicit surface has emerged as a powerful representation for multi-view surface reconstruction due to its simplicity and State-of-the-Art performance. However, reconstructing smooth and detailed surfaces in indoor scenes from multi-view images presents unique challenges. Indoor scenes typically contain large texture-less regions, making the photometric loss unreliable for optimizing the implicit surface. Previous work utilizes monocular geometry priors to improve the reconstruction in indoor scenes. However, monocular priors often contain substantial errors in thin structure regions due to domain gaps and the inherent inconsistencies when derived independently from different views. This paper presents DebSDF to address these challenges, focusing on the utilization of uncertainty in monocular priors and the bias in SDF-based volume rendering. We propose an uncertainty modeling technique that associates larger uncertainties with larger errors in the monocular priors. High-uncertainty priors are then excluded from optimization to prevent bias. This uncertainty measure also informs an importance-guided ray sampling and adaptive smoothness regularization, enhancing the learning of fine structures. We further introduce a bias-aware signed distance function to density transformation that takes into account the curvature and the angle between the view direction and the SDF normals to reconstruct fine details better. Our approach has been validated through extensive experiments on several challenging datasets, demonstrating improved qualitative and quantitative results in reconstructing thin structures in indoor scenes, thereby outperforming previous work.

Experimental characterization and similarity scaling of smooth-body flow separation and reattachment on a two-dimensional ramp geometry

The results of an experimental investigation of smooth-body adverse pressure gradient (APG) turbulent boundary layer flow separation and reattachment over a two-dimensional ramp are presented. These results are part of a larger archival smooth-body flow separation data set acquired in partnership with NASA Langley Research Center and archived on the NASA Turbulence Modeling Resource website. The experimental geometry provides initial canonical turbulent boundary layer growth under nominally zero pressure gradient conditions prior to encountering a smooth, two-dimensional, backward facing ramp geometry onto which a streamwise APG that is fully adjustable is imposed. Detailed surface and off-surface flow field measurements are used to fully characterize the smooth-body APG turbulent boundary layer separation and reattachment at multiple spanwise locations over the ramp geometry. Unsteady aspects of the flow separation are characterized. It is shown that the first and second spatial derivatives of the streamwise static surface pressure profile are sufficient to determine key detachment and reattachment locations. The imposed streamwise APG gives rise to inflectional mean velocity profiles and the associated formation of an embedded shear layer, which is shown to play a dominant role in the subsequent flow development. Similarity scaling is developed for both the mean velocity and turbulent stresses that is found to provide self-similar collapse of profiles for different regions of the ramp flow. Despite the highly non-equilibrium flow environment, a new similarity scaling proved capable of providing self-similar turbulent stress profiles over the full streamwise extent of flow separation and downstream reattachment.

A new parameter unifying criterion for rectangular surface structure influences on vaporization nucleation: A molecular dynamics study

This study investigates the nucleation behavior on nanostructured surfaces and its impact on heat transfer efficiency. The significance of this research lies in the fact that accurate characterization of surface effects is crucial for optimizing heat transfer systems, which have profound implications for various industrial applications. Using molecular dynamics methods, we systematically analyze the influence of rectangular surface structures with varying heights, widths, and densities on nucleation behavior. Our findings reveal that traditional surface roughness metrics fail to capture critical surface details, limiting their ability to accurately describe the effects of different surfaces on nucleation. Furthermore, we observe that surface structures significantly influence nucleation by altering local liquid film thicknesses. To address these limitations, we propose a novel parameter that combines corrected effective liquid film thickness with surface roughness to provide a more comprehensive characterization of the nucleation process. Through computational modeling, we validate the effectiveness of this parameter in predicting heat flux density, heat transfer coefficient, and surface thermal resistance across different rectangular surface structures. The results of this study clarify the mechanisms through which surface structures affect nucleation, offering a more precise tool for characterizing these effects. This enhanced understanding not only advances the theoretical framework of nucleation science but also has practical implications for the design of more efficient heat transfer systems in various industrial settings. The novelty of this work lies in the introduction of a new parameter that surpasses previous efforts in the literature by providing a more accurate quantitative prediction of the impact of surface structures on nucleation and heat transfer efficiency.

Capturing Dichotomic Solvent Behavior in Solute-Solvent Reactions with Neural Network Potentials.

Simulations of chemical reactivity in condensed phase systems represent an ongoing challenge in computational chemistry, where traditional quantum chemical approaches typically struggle with both the size of the system and the potential complexity of the reaction. Here, we introduce a workflow aimed at efficiently training neural network potentials (NNPs) to explore energy barriers in solution at the hybrid density functional theory level. The computational burden associated with training at the PBE0-D3(BJ) level is bypassed through the use of active and transfer learning techniques, whereas extensive sampling of the transition state region is accelerated by well-tempered metadynamics simulations using multiple time step integration. These NNPs serve to explore a puzzling solute-solvent reactivity route involving the ring opening of N-enoxyphthalimide experimentally observed in methanol but not in 2,2,2-trifluoroethanol (TFE). This reaction represents a challenging example characterized by intricate hydrogen bonding networks and structurally ambiguous solvent-sensitive transition states. The methodology successfully delivers detailed free energy surfaces and relative energy barriers in agreement with experiment. These barriers are associated with an ensemble of transition states involving the direct participation of up to five solvent molecules. While this picture contrasts with the single transition state structure assumed by current static models, no drastic qualitative difference is observed between the formed hydrogen bonding networks and the number of participating solvent molecules in methanol or TFE. The dichotomy between the two solvents thus essentially arises from an electronic effect (i.e., distinct nucleophilicity) and from the larger conformational entropy contributions in methanol. This example underscores the critical role that dynamic simulations at the ab initio levels play in capturing the full complexity of solute-solvent interactions.

Comparative Evaluation of Ozone Water and Glutaraldehyde on Surface Detail Reproduction of Vinyl Polyether Silicone Impression Materials at Different Time Intervals

Comparative Evaluation of Ozone Water and Glutaraldehyde on Surface Detail Reproduction of Vinyl Polyether Silicone Impression Materials at Different Time Intervals

The effects of surface heterogeneity on erosion and sedimentation and their implications for of soil properties at postmining sites

Erosion is often mentioned as a serious problem namely in vegetation free areas such as post mining soils. Much less attention has been paid to the role of erosion and deposition on habitat formation. Study describes the effect of erosion and deposition on soil chemical and physical properties in sites with various topography using an array of artificial postmining catchments located near Sokolov (Czech Republic). All the study sites are formed by the deposition of postmining overburden consisting of clays impregnated with carbonates. Falcon was established in 2019 and allows a detailed assessment of the first stages of ecosystem development. It consists of four parallel catchments (0.25 ha each); two are rehabilitated, and two are not. The reclaimed areas are leveled and planted with alders, whereas the unreclaimed sites created longitudinal terrain waves to mimic the situation after heaping. Detailed surface change studies by means of erosion pins and 3D terrain models created with the help of drones revealed that the surface elevation rises in the flat catchments and decreased in the wave-like catchments. In contrast, sediment loss through surface runoff shows no difference between the flat and wave-like areas. Considerable heterogeneity in the erosion process is observed in wave-like areas; the upper part of the terrain waves is heavily eroded, while eroded material accumulates in the depression. The depressions contain more silt and clay material and have higher water retention rates and lower infiltration rates, whereas the opposite is true for elevated wave tops. Study show that erosion and deposition play key role in microhabitat formation, which can be crucial in future ecosystem development.

Depth Profiles of Crystallinity and Preferential Orientation and Surface Characteristics of Sputter‐Deposited CeO2 Thin Film Buffer Layers on Sapphire Substrates Evaluated Through Two‐Dimensional Grazing‐Incidence X‐Ray Diffraction

ABSTRACTTwo‐dimensional grazing‐incidence wide‐angle X‐ray diffraction data were acquired from CeO2 thin films serving as buffer layers on r‐cut sapphire substrates to investigate the depth profiles of crystallinity and preferential orientation as well as surface conditions. The thin film samples were sputter‐deposited under different conditions. Data were obtained at an X‐ray wavelength of 0.100 nm at 0.02° intervals from 0.06° to 0.30° at the grazing‐incidence angle. A 60‐nm‐thick CeO2 thin film sputter‐deposited under Ar (Ar‐60) and a 100‐nm‐thick CeO2 thin film sputter deposited using 10 vol.% O2 in Ar (O+Ar‐100) were found to have partially oriented three‐dimensional bulk crystal structures with preferential orientation in the (100) direction near the surface. The lattice constant for the Ar‐60 film was found to be approximately 0.30% smaller than that for a CeO2 standard, whereas the lattice constant for the O+Ar‐100 film was approximately 0.50% larger. Lattice mismatch between the CeO and CeO2 on the substrate surface is evidently not an issue based on the lattice constants near the surface. Hence, both films could be considered to represent optimal buffer layers for YBa2Cu3O6+δ thin film growth. However, even in the case of a CeO2 thin film suitable for use as a buffer layer, the difference in lattice constants between the interior and surface of an Ar‐60 film was found to be relatively small, whereas that for an O+Ar‐100 film was relatively large. These results show that it is important to analyze the film surface in detail by reducing the incidence angle to less than 0.1°.

Design of superparamagnetic Fe3O4@SiO2@3,4-DABP nanocatalysts, fabrication by co-precipitation and sol-gel methods, characterization of detailed surface texture properties and investigation of solar cell performance

This research focuses on the synthesis, characterization, and evaluation of Fe3O4, Fe3O4@SiO2, and Fe3O4@SiO2@3,4-DABP magnetic nanocatalysts (MNCs) for their potential use as sensors within the intricate architectures of solar cell devices. Fourier transform infrared (FTIR) spectroscopy, scanning electron microscopy with energy dispersive X-ray spectroscopy (SEM-EDS), transmission electron microscopy (TEM), vibrating sample magnetometry (VSM), X-ray diffraction (XRD), thermogravimetric analysis (TGA) and Brunauer-Emmett-Teller (BET) surface area measurements were carried out to characterize the structural, morphological and magnetic properties of the MNCs. The MNCs exhibit an average particle size of approximately 10 nm. Fe3O4, Fe3O4@SiO2, and Fe3O4@SiO2@3,4-DABP MNCs have saturation magnetization values ​​of 61.64 emu/g, 37.31 emu/g, and 20.13 emu/g, respectively. Thermal analysis reveals mass change losses of 6.5%, 12% and 28.1%, respectively, indicating different thermal stability profiles. It confirms that their crystal structure is face-centered cubic spinel, with type IV hysteresis loops and H3 loops indicating a mesoporous structure according to the IUPAC classification. Efficiency tests of Fe3O4, Fe3O4@SiO2 and Fe3O4@SiO2@3,4-DABP MNCs in solar cell devices show efficiencies of 1.49%, 1.77% and 2.15%, respectively. As the hierarchical modification of the MNCs increases, the efficiency of the solar cell devices increases. These results highlight the potential of Fe3O4, Fe3O4@SiO2 and Fe3O4@SiO2@3,4-DABP as promising sensitizers in solar cell technology. Fe3O4@SiO2@3,4-DABP MNCs have high catalytic activity, chemical stability, electronic conductivity and low cost. This study also marks the first demonstration of the effectiveness of environmentally friendly Fe3O4@SiO2@3,4-DABP MNCs in enhancing solar cell performance, prepared via a cost-effective, simple and eco-friendly approach.

Unravelling the Complexity of Amyloid Peptide Core Interfaces.

Amyloids, large intermolecular sandwiched β-sheet structures, underlie several protein misfolding diseases but have also been shown to have functional roles and can be a basis for designing smart and responsive nanomaterials. Short segments of proteins, called aggregation-prone regions (APRs), have been identified that nucleate amyloid formation. Here we present the database of 173 APR crystal structures currently available in the PDB, and a tool, ACW, for analyzing their topologies and the 267 inter-β-sheet interfaces of zipper regions assigned in these structures. We defined a new descriptor of zipper interfaces, the surface detail index (SDi), which quantifies the intertwining between the side chains of both β-sheets of the zipper, an important factor for the molecular recognition and self-assembly of these mesostructures. This allowed a comparative analysis of the zipper interfaces and identification of 6 clusters with different intertwining, steric fit, and size characteristics using three complementary descriptors, SDi, shape complementarity, and buried surface area. 60% of the APR structures are formed by parallel β-sheets, of which 52% belong to the topological class 1. This could be explained by the better fit and a deeper entanglement of the zipper regions of the parallel structures than of the antiparallel structures, as the analysis showed that both their shape complementarity (0.79 vs 0.70) and SDi (1.53 vs 1.32) were higher. The higher abundance of certain residues (Asn and Gln in parallel and Leu and Ala in antiparallel β-sheets) can be explained by their ability to form different ladder-like secondary interaction patterns within β-sheets. Analogous to the hierarchy of protein structure, we interpreted the primary, secondary, tertiary, and quaternary structure levels of APRs revealing different characteristics of the zipper regions for both parallel and antiparallel β-sheet structures, which may provide clues to the structural conditions of amyloid core formation and the rational design of amyloid polymorphs.

Joint Optimization-Based Texture and Geometry Enhancement Method for Single-Image-Based 3D Content Creation

Recent studies have explored the generation of three-dimensional (3D) meshes from single images. A key challenge in this area is the difficulty of improving both the generalization and detail simultaneously in 3D mesh generation. To address this issue, existing methods utilize fixed-resolution mesh features to train networks for generalization. This approach is capable of generating the overall 3D shape without limitations on object categories. However, the generated shape often exhibits a blurred surface and suffers from suboptimal texture resolution due to the fixed-resolution mesh features. In this study, we propose a joint optimization method that enhances geometry and texture by integrating generalized 3D mesh generation with adjustable mesh resolution. Specifically, we apply an inverse-rendering-based remeshing technique that enables the estimation of complex-shaped mesh estimations without relying on fixed-resolution structures. After remeshing, we enhance the texture to improve the detailed quality of the remeshed mesh via a texture enhancement diffusion model. By separating the tasks of generalization, detailed geometry estimation, and texture enhancement and adapting different target features for each specific network, the proposed joint optimization method effectively addresses the characteristics of individual objects, resulting in increased surface detail and the generation of high-quality textures. Experimental results on the Google Scanned Objects and ShapeNet datasets demonstrate that the proposed method significantly improves the accuracy of 3D geometry and texture estimation, as evaluated by the PSNR, SSIM, LPIPS, and CD metrics.

The Cibarco anticline of the northernmost San Jacinto fold belt: Possible influence of mud diapirism on the Neogene to the recent evolution of the structure where the first oil and gas wells were drilled in Colombia

The Cibarco anticline, located in the northwestern South American convergent margin, is a long (approximately 40 km) and narrow (2.5 km wide), south-southwest–north-northeast-trending structure that separates two much wider depocenters, the Tubará-Juan de Acosta depocenter (approximately 20 km wide) to the west, and the Sabanalarga syncline (approximately 11 km wide) to the east. Detailed geologic surface mapping and subsurface seismic interpretation suggest that the formation and evolution of the Cibarco anticline have been strongly influenced by mud diapirism, which has been focused along a west-southwest–east-northeast-trending, inverted pre-Oligocene extensional fault. Seismic interpretation and outcrop analyses also confirmed that the source of mobile shales is the Upper Oligocene marlstones of the Lower Ciénaga de Oro Formation. We suggest that the main mechanism that caused the overpressures that triggered and controlled shale mobilization was disequilibrium compaction related to the sedimentary loading of impermeable seals and the shortening and inversion of pre-Oligocene extensional faults. Growth strata indicate that the Upper Oligocene mud started moving in Early Miocene times during the deposition of the thick Porquero mudstones, and it stopped migrating in Latest Miocene times. After an uplift and erosion event, mud diapirism remained inactive in the Early Miocene, and it was then reactivated in Late Pliocene times. Slight mud migration focused on the inverted fault continued throughout the Pleistocene and to the present. Today, the slight mud migration to the surface is observed in volcanoes located in the northern strand of the Cibarco anticline, although the rest of the anticline has become welded. Our new interpretations, carried out more than a century after the first hydrocarbon exploration activities began in Colombia, will hopefully allow us to increase our understanding of shale tectonics, petroleum systems, and plays in this portion of the convergent margin.

Innovative 8-hydroxyquinoline derivatives as corrosion inhibitors for mild steel in hydrochloric acid: a quantum chemical and experimental study

ABSTRACT This study explores the inhibitory action of two novel organic compounds, namely diethyl 1-((8hydroxyquinolin-5-yl)methyl)−2,6-dimethyl-4-(p-tolyl)−1,4-dihydropyridine-3,5-dicarboxylate (PR1) and diethyl 1-((8-hydroxyquinoline-5-yl) methyl)−2,6-dimethyl-4-(4-nitrophenyl)−1,4dihydropyridine-3,5-dicarboxylate (PR2), on mild steel (MS) corrosion in 1M HCl. Using a combination of quantum chemical and experimental methodologies, the study evaluates the performance of these inhibitors through electrochemical techniques, including potentiodynamic polarisation (PDP) and electrochemical impedance spectroscopy (EIS), complemented by surface analysis via scanning electron microscopy (SEM) and X-ray electron microscopy (EDX). Key findings reveal that both PR1 and PR2 exhibit a significant rise in protection performance with concentration, reaching an impressive peak of 95.3% at 10−3 M for PR2. The inhibitors demonstrate mixed-type inhibition properties and adhere to the Langmuir adsorption isotherm. Detailed surface analysis confirms the creation of a defensive film on the metal surface, correlating with the observed electrochemical results.

Assessing the Corrosive Effects of Unmelted Particles in Additively Manufactured Ti6Al4V: A Study in Simulated Body Fluid

This study investigates the corrosion behavior of Grade 23 Ti6Al4V alloys produced through laser powder bed fusion (L-PBF) when exposed to simulated body fluid at room temperature, focusing on the role of unmelted particles. This research aims to understand how these microstructural features, resulting from the additive manufacturing process, influence the corrosion resistance of the alloys. It was observed that unmelted particles serve as critical sites for initiating localized corrosion, including pitting, which significantly compromises the material’s overall durability. Electrochemical testing and detailed surface analysis revealed that these particles, alongside other defects such as voids, exacerbate the susceptibility to corrosion in biomedical environments where high material reliability is paramount. Weight loss measurements conducted over exposure periods of 48 h, 96 h, and 144 h demonstrated a progressive increase in corrosion, correlating with the presence of unmelted particles. These findings underscore the importance of optimizing L-PBF processing parameters to minimize the formation of unmelted particles, thereby enhancing corrosion resistance and extending the operational lifespan of Ti6Al4V implants in biomedical applications.

Effects of Strontium Modification on Corrosion Resistance of Al-Si Alloys in Various Corrosive Environments.

This study investigates the impact of strontium (Sr) additions on the corrosion resistance of an LM6 (A413) aluminium alloy. By incorporating varying concentrations of Sr (0.01 wt.% and 0.05 wt.%), the morphological and corrosion behaviours of the alloy were analysed under different corrosive environments, including sulphuric acid, sodium hydroxide, and sodium chloride solutions. The results demonstrate that Sr modifications significantly enhance the alloy's corrosion resistance, with the most substantial improvement observed at 0.05 wt.% Sr. The analysis revealed that the weight loss of the alloy in sulphuric acid decreased by 2.5% with 0.05 wt.% Sr after 10 days of immersion, due to the formation of a stable passive oxide layer. In sodium hydroxide, however, the weight loss was reduced by 5% with 0.05 wt.% Sr after 10 days, indicating aggressive uniform corrosion. In the 3.5% sodium chloride solution, the corrosion rates remain relatively low, and the 0.05 wt.% Sr alloy showed a decrease in corrosion product formation over time, suggesting enhanced resistance. Detailed surface analyses, including 3D profiling and morphology assessments, revealed that Sr additions refine the eutectic silicon phase, transforming it from a coarse to a more desirable fibrous or lamellar structure, thus improving the alloy's overall performance. The innovative findings underscore the potential of Sr as an effective microstructural modifier for enhancing the durability and longevity of Al-Si alloys in corrosive environments.

Characterization of the in-situ degradation process of P3HT:PCBM based on hyperspectral and neural networks

In situ online observation of surface morphology during degradation processes is of paramount importance for exploring the stability of organic photovoltaic materials. In this study, we designed an in situ online characterization system based on hyperspectral and neural network technologies, and observed the degradation processes of P3HT:PCBM thin film materials. The system is capable of collecting hyperspectral image data from 101 channels within the 400–700 nm wavelength range for characterizing detailed surface features of materials. Additionally, to automate the processing of hyperspectral image data, we designed a spectral image segmentation algorithm based on neural networks and proposed a foreground attention mechanism to improve the segmentation accuracy of the algorithm. The experimental results indicate that the system can achieve high spectral characterization of P3HT:PCBM thin film materials and automate image data processing through artificial intelligence algorithms, with an image segmentation accuracy of 99.62 %. Furthermore, owing to the higher spectral resolution of this system and its computer-assisted analysis capabilities for material image data, not only are the in-situ variations in size, density, and formation rate of aggregates formed during the thermal degradation process of P3HT:PCBM thin film materials experimentally analyzed, but also the fluorescence changes at the edges of aggregates during the photodegradation process are revealed. The reliable code can be found at the following link: https://github.com/HyperSystemAndImageProc/IONFMDP-UHHNN.

Antimicrobial Efficiency of Hypochlorous Acid Incorporation and its Effect on Surface Properties of Irreversible Hydrocolloid Materials

Objective: Conventional approaches for disinfection, including spraying and immersion, resulted in only surface disinfection of impressions. As a result, self-disinfecting impression materials incorporated with antimicrobial compounds require more extensive studies. The incorporation of a disinfectant into irreversible hydrocolloid impression materials could eliminate the need for the disinfection step by conventional approaches, including spraying and immersion which only result in surface disinfection of impressions. The study was aimed to investigate the effect of incorporation of hypochlorous acid in irreversible hydrocolloid materials on antimicrobial efficiency, detail reproduction, and dimensional stability. Materials and Methods: Hypochlorous acid (HOCl) was used in two concentrations, 100 ppm and 200 ppm, and mixed with alginate powder to compare with the control group (distilled water mixed with alginate). Candida albicans, Staphylococcus aureus, and Pseudomonas aeruginosawere chosen for assessing the antimicrobial activity with the disk diffusion test. In addition, the dimensional stability and reproduction of details were tested. Results: The results revealed that both HOCl concentrations of 100 ppm and 200 ppm imposed significant antimicrobial activity against all three tested microorganisms. There was no significant difference regarding reproduction of details, but the addition of the antimicrobial had a significant adverse effect on the alginate’s dimensional stability. Conclusion: It may be concluded that the incorporation of HOCL into irreversible hydrocolloid impression material resulted in an impression with antimicrobial activity. In addition, there was no effect on the impression materials ability to reproduce surface details, but the antimicrobial addition may affect its dimensional stability.

The role of 3D cinematic rendering in the evaluation of upper extremity trauma.

Traumatic upper extremity injuries are a common cause of emergency department visits, comprising between 10-30% of traumatic injury visits. Timely and accurate evaluation is important to prevent severe complications such as permanent deformities, ischemia, or even death. Computed tomography (CT) and CT angiography (CTA) are the favored non-invasive imaging techniques for assessing upper extremity trauma, playing a crucial role in both the treatment planning and decision-making processes for such injuries. In CT postprocessing, a novel 3D rendering method, cinematic rendering (CR), employs sophisticated lighting models that simulate the interaction of multiple photons with the volumetric dataset. This technique produces images with realistic shadows and improved surface detail, surpassing the capabilities of volume rendering (VR) or maximal intensity projection (MIP). Considering the benefits of CR, we demonstrate its use and ability to achieve photorealistic anatomic visualization in a series of 11 cases where patients presented with traumatic upper extremity injuries, including bone, vascular, and skin/soft tissue injuries, adding to diagnostic confidence and intervention planning.

Numerical simulation of particles distribution of environmental tobacco smoke (ETS) and its concentration in a closed environment

Humans are frequently exposed to various chemical hazards, with smoking being a significant source of indoor air pollution. This pollution is particularly pronounced in environments such as smoking rooms, conference halls, cafes, and coffee houses. This study focuses on analyzing the concentration, distribution, and behavior of emitted particles from both mainstream and sidestream smoke in a controlled smoking room environment. A critical aspect of the research is the consideration of the thermal plume generated by the smoker's body, particularly around the breathing zone, which plays a crucial role in the dispersion and inhalation of pollutants. To conduct this analysis, a computational fluid dynamics approach was employed, incorporating a displacement ventilation (DV) system integrated with a detailed surface mesh model of a standing manikin. A Lagrangian approach was utilized to track the particle paths, considering factors such as particle size, density, and initial velocity. This approach was complemented by an Eulerian method to simulate the complex airflow patterns within the smoking room, accounting for turbulent flow and temperature variations. The integration of these methods provides a comprehensive understanding of both the macroscopic and microscopic behaviors of smoke particles. The motion equations governing the particles incorporated several forces, including inertial drag force, viscous drag force, buoyancy force, and gravitational force. Additionally, the study examined the interaction between thermal plumes and smoke particles, particularly how temperature gradients around the body affect particle dispersion and concentration. The study meticulously evaluated the distribution and volume average of respirable suspended particles (RSPs) and nicotine by analyzing a plane opposite to the breathing zone of both heated and unheated manikins. It was observed that the thermal plume generated by the temperature gradient around the body could significantly alter the flow field of both sidestream and mainstream smoke between puff periods. The simulation results demonstrated that the thermal plume increased the concentration of RSPs in the breathing zone, highlighting a critical exposure pathway for smokers and nonsmokers alike. A significant increase of approximately 70% in the nicotine concentration is due to the thermal plume. The concentration of nicotine peaks at approximately 0.003 mg/m3 by the third second in smoker#1's (6 smokers' position) breathing zone. Specific configurations of the DV system showed varying efficiencies in reducing pollutant concentrations, emphasizing the need for targeted ventilation strategies. In a chamber with six smokers, researchers found that the time required for suspended particles to reach a cleaning ratio, considering the dynamics of particle movement due to smoking activity, was approximately 184 s per puff.

Enhancing 3D Models with Spectral Imaging for Surface Reflectivity.

The increasing demand for accurate and detailed 3D modeling in fields such as cultural heritage preservation, industrial inspection, and scientific research necessitates advanced techniques to enhance model quality. This paper addresses this necessity by incorporating spectral imaging data to improve the surface detail and reflectivity of 3D models. The methodology integrates spectral imaging with traditional 3D modeling processes, offering a novel approach to capturing fine textures and subtle surface variations. The experimental results of this paper underscore the advantages of incorporating spectral imaging data in the creation of 3D models, particularly in terms of enhancing surface detail and reflectivity. The achieved experimental results demonstrate that 3D models generated with spectral imaging data exhibit significant improvements in surface detail and accuracy, particularly for objects with intricate surface patterns. These findings highlight the potential of spectral imaging in enhancing 3D model quality. This approach offers significant advancements in 3D modeling, contributing to more precise and reliable representations of complex surfaces.

The role of crystallographic orientation on surface properties and ion transport in lithium germanate (Li2GeO3) anodes: A computational approach

The performance of lithium-ion batteries (LIBs) hinges on the surface properties of their anodes. Compared to the bulk material, the anode surface is more susceptible to environmental changes during lithium (Li) intake and release, directly impacting factors like capacity, cycling stability, and charge/discharge rates. Lithium germanate (Li2GeO3, LGO) has emerged as a promising anode material due to its fast Li-ion conduction. While numerous studies have explored performance improvements through various methods, including defect engineering. However, there is currently a lack of atomistic-level understanding of the surface structure. Consequently, despite the importance of precisely understanding the surface to manipulate its different properties, specific surface details of LGO remain unclear. This knowledge gap hinders precise manipulation of surface properties for optimal performance.This study addresses this critical need by employing theoretical calculations to predict the structural, electrochemical characteristics, and Li-ion transport behavior in LGO surfaces. Our results indicate that polar surfaces exhibit lower formation energies compared to non-polar surfaces. Further investigation revealed that Li-terminated surfaces possess the lowest surface energy among various surface terminations. Interestingly, the work function calculations displayed an opposite trend to surface formation energy, with polar surfaces exhibiting the lowest work function values.To explore Li-ion transport, we employed ab initio molecular dynamics simulations. Notably, the (003) surface displayed the highest Li-ion diffusion rate among all considered surfaces.Further analysis of the (001) surface, which exhibited similar diffusion pathways to the (003) surface, revealed a lower diffusion rate.To understand this disparity, nudged elastic band (NEB) simulations were used to estimate the energy barriers for Li-ion migration along each pathway in both structures. Despite sharing similar pathways, the energy barriers in the (003) surface were significantly lower than those in the (001) surface. This finding suggests that the intrinsic energy landscape of the surface plays a crucial role in dictating Li-ion transport behavior.