Periodic Patterns Research Articles

Scale Differences and Gradient Effects of Local Climate Zone Spatial Pattern on Urban Heat Island Impact—A Case in Guangzhou’s Core Area

With the continuous development of cities, the surface urban heat island intensity (SUHII) is increasing, leading to the deterioration of the urban thermal environment, increasing energy consumption, and endangering the health of urban residents. Understanding the spatio-temporal scale difference and gradient effect of urban spatial patterns on the impact of SUHII is crucial for improving the climate resilience of cities and promoting sustainable urban development. This paper investigated the characteristics of SUHII changes at different time periods based on local climate zones (LCZs) and downscaled land surface temperature (LST) data. Meanwhile, landscape pattern indicators and the multiscale geographically weighted regression (MGWR) model were utilized to analyze the impacts of urban spatial patterns on SUHII at multiple spatial–temporal scales. The results indicated that the SUHII of each LCZ type exhibited diverse patterns in different time periods. High SUHII occurred in summer daytime and autumn nighttime. Compact and high-rise buildings (LCZ1/2/4) showed markedly higher SUHII during the daytime or nighttime, except for heavy industry. The extent of influence and the dominant factors of LCZ spatial patterns on SUHII exhibit obvious scale differences and gradient effects. At the regional scale, highly regular and compacted built-up areas tended to increase SUHII, while single and continuously distributed built-up areas had a greater impact on increasing SUHII. At the local scale, the impact of the PLAND (1/2/4/5/10) on SUHII exhibited a trend of diminishing from urban to suburban areas. In urban areas, the PLAND of LCZ 1, LCZ 2, and LCZ4 was the major factor affecting the increase in SUHII, whereas, in suburban areas, the PLAND of LCZ 2 and LCZ 10 was the major influencing factor on SUHII. The results can provide a scientific reference for mitigating urban heat island effects and constructing an ecologically ‘designed’ city.

Deret Fourier dengan Pendekatan Numerik Menggunakan Metode Trapezium

Fourier series has the ability to decompose a periodic function into a collection of sine and cosine wave sums. Another way to express a function into an infinite series is with the Maclaurin series or Taylor series but both of these series require that the function must have the first derivative, the second derivative and so on of the function is defined at its expansion point and there must be no discontinuity value at that point. However, the advantage of the Fourier series is that we can decompose the function into an infinite series containing the sum of sine and cosine terms even though the function cannot be differentiated and has discontinuity at some points. The Fourier series equation of an analytical function is obtained by first finding the values ​​of the Fourier coefficients using analytical integrals. In the case of functions formed from numerical data that are assumed to have a periodic pattern, the Fourier series formula that uses analytical integrals to find its Fourier coefficients can no longer be used so the numerical approach using the Trapezium method is an alternative that allows us to find the Fourier series equation for a periodic function expressed numerically. We use 12 points as the basis for the calculation in the Trapezium method to approximate the Fourier series numerically. This paper presents a numerical approach to solving the problem.

Advancing Process-Oriented Geographical Regionalization Model

Existing regionalization methods have largely overlooked the temporal dimension, leading to outcomes that predominantly reflect spatial differentiation of regional variables only at a singular temporal instance, rather than across their entire evolutionary process. In response, this research proposes a novel model for geographic process regionalization based on spatial time-series (STS) data. This model is designed to amalgamate adjacent entities exhibiting analogous trends in regional variable fluctuations, while segregating those with disparate trajectories. The study starts by establishing methodologies for the assessment of three distinct types of STS data similarities: value-based, shape-oriented, and periodic patterns. Following this, a tailored regionalization methodology specific to STS data is delineated. This methodology facilitates a quantitative analysis of time-series variables within each geographical region, leading to the development of a process-oriented geographic boundary model (GBM). The efficacy of this model is validated through empirical testing with synthetic data sets and further elucidated via case studies focusing on the dynamics of population and pollution levels, underscoring its substantial applicability in both human and physical geography. The proposed methodology is adaptable for any time-series data that incorporate locational information for regionalization.

Effect of Flow Interference between Cylinders Subjected to a Cross Flow over a Cluster of Three Equally Spaced Cylinders

In this paper, flow over a cluster of three equally spaced circular cylinders was studied by numerical simulation based on the turbulence model k-kl-ω for two incidence angles β = 0° and 60°, at different Reynolds numbers, and flow interference pattern characteristics between cylinders, characteristics of force parameters, and Strouhal number of each cylinder with different spacing ratios ranging from 1.5 to 4 at Re 8 × 104, 2 × 105 and 2 × 106 were obtained. Analyzing the flow field around three cylinders, the following results have been obtained: (1) at incidence angle β = 0° and 60°, the wake was nearly symmetrical if S/D ≥ 2.0; (2) at β = 60°, S/D = 1.5, and Re = 2 × 105, an asymmetric periodic flow pattern occurred in the wake region which produced a significant effect on the surface mechanical parameters and Strouhal number, and this was observed for the first time. The periodic flow regime of the wake region also occurred at S/D = 1.35 and 1.5, without the same phenomenon at S/D = 1.7 and 2.0; this phenomenon is described for the first time in this paper; (3) the phenomenon of periodic flow regime in the wake region was intrinsic and related to Reynolds number and space ratio. In addition, the characteristics of force parameters of three cylinders were mainly affected by the interference between cylinders, at 1.5 < S/D < 4, which indicated that the drag coefficient of three cylinders reduces with different Reynolds numbers and increases with enlargement of the spacing ratio for upstream cylinders at incidence angle 0°. At incidence angle 60° and S/D = 1.5~4, the Strouhal number decreases with the enlargement of spacing ratio for the upstream cylinders, but the Strouhal number increases for the downstream cylinder, which is another prominent flow interference influence. The results indicated the effect of flow interference between cylinders subjected to a cross flow over a cluster of three equally spaced cylinders, considering the flow pattern, surface mechanical parameters and Strouhal number, which should be considered in the establishment of standard design codes in fields such as offshore wind turbine engineering for flow interference around groups of cylinders.

Theory of Turing pattern formation and its experimental realization in the CIMA reaction system in the presence of materials lowering the diffusivity of activators

In 1952, Alan Turing accomplished a pioneering theoretical study to show that the coupling of nonlinear chemical reactions and diffusion leads to the instability of spatially homogeneous states. The activator and inhibitor are synthesized as intermediates of the reaction system in the Turing model. Turing found that spatially periodic stationary concentration patterns are spontaneously generated when the diffusion coefficient of the activator is lower than that of the inhibitor. The first experimental realization of the Turing pattern was achieved in 1990 in a chlorite–iodide–malonic acid (CIMA) reaction system. Iodide and chlorite anions act as the activator and inhibitor of this reaction system, respectively. Although there is no significant difference in the diffusion coefficient of iodide and chlorite anions, the Turing pattern was generated because starch was added to the gel reactor to enhance the color tone. This formed a complex with iodide to inhibit its diffusion to satisfy the condition for the Turing instability. Several examples were found after this finding. We focused on the high affinity of quaternary alkyl ammonium cations to iodide. The CIMA reaction was performed in an open gel reactor by adding a quaternary alkyl ammonium cationic surfactant. In addition, the polymer gel consists of the quaternary alkyl ammonium group as the side chain was utilized for the open gel reactor. The micelles of the surfactants and the polymer gels trapped iodide in their vicinity as a counter anion to lower the effective diffusivity to satisfy the condition for the Turing instability.

Molecular Evolutionary Dynamics of Coxsackievirus A6 Causing Hand, Foot, and Mouth Disease From 2021 to 2023 in China: Genomic Epidemiology Study.

Hand, foot, and mouth disease (HFMD) is a global public health concern, notably within the Asia-Pacific region. Recently, the primary pathogen causing HFMD outbreaks across numerous countries, including China, is coxsackievirus (CV) A6, one of the most prevalent enteroviruses in the world. It is a new variant that has undergone genetic recombination and evolution, which might not only induce modifications in the clinical manifestations of HFMD but also heighten its pathogenicity because of nucleotide mutation accumulation. The study assessed the epidemiological characteristics of HFMD in China and characterized the molecular epidemiology of the major pathogen (CV-A6) causing HFMD. We attempted to establish the association between disease progression and viral genetic evolution through a molecular epidemiological study. Surveillance data from the Chinese Center for Disease Control and Prevention from 2021 to 2023 were used to analyze the epidemiological seasons and peaks of HFMD in Henan, China, and capture the results of HFMD pathogen typing. We analyzed the evolutionary characteristics of all full-length CV-A6 sequences in the NCBI database and the isolated sequences in Henan. To characterize the molecular evolution of CV-A6, time-scaled tree and historical population dynamics regarding CV-A6 sequences were estimated. Additionally, we analyzed the isolated strains for mutated or missing amino acid sites compared to the prototype CV-A6 strain. The 2021-2023 epidemic seasons for HFMD in Henan usually lasted from June to August, with peaks around June and July. The monthly case reporting rate during the peak period ranged from 20.7% (4854/23,440) to 35% (12,135/34,706) of the total annual number of cases. Analysis of the pathogen composition of 2850 laboratory-confirmed cases identified 8 enterovirus serotypes, among which CV-A6 accounted for the highest proportion (652/2850, 22.88%). CV-A6 emerged as the major pathogen for HFMD in 2022 (203/732, 27.73%) and 2023 (262/708, 37.01%). We analyzed all CV-A6 full-length sequences in the NCBI database and the evolutionary features of viruses isolated in Henan. In China, the D3 subtype gradually appeared from 2011, and by 2019, all CV-A6 virus strains belonged to the D3 subtype. The VP1 sequences analyzed in Henan showed that its subtypes were consistent with the national subtypes. Furthermore, we analyzed the molecular evolutionary features of CV-A6 using Bayesian phylogeny and found that the most recent common ancestor of CV-A6 D3 dates back to 2006 in China, earlier than the 2011 HFMD outbreak. Moreover, the strains isolated in 2023 had mutations at several amino acid sites compared to the original strain. The CV-A6 virus may have been introduced and circulating covertly within China prior to the large-scale HFMD outbreak. Our laboratory testing data confirmed the fluctuation and periodic patterns of CV-A6 prevalence. Our study provides valuable insights into understanding the evolutionary dynamics of CV-A6.

Heat transfer and visualization of flow boiling on nanowire surfaces in the microchannel

Enhancing heat transfer efficiency is crucial in heat exchange equipment. Although previous studies have focused on developing micro/nano-structured surfaces, further exploration into how surface structure can improve heat transfer efficiency (H) by altering bubble dynamics is still needed. This study aimed to innovatively analyze the impact of titanium carbide (TiC) nanowire heights—specifically 4 µm and 12 µm—on boiling heat transfer performance. Conducted under varying heat flux (Q) (50–200 W/m2) and mass flux (G) (200–300 kg/m2·s), our experiments assessed H, pressure drop (P), and local boiling curves. Using a high-speed camera, we observed complex periodic flow patterns on the 4 µm nanowire surface, including elongated bubble formation, expansion, local dryout, and subsequent fluid rewetting. Results showed that the 12 µm nanowire surface increased H by up to 19.84 % in single-phase conditions, while the 4 µm nanowire surface increased H by up to 27.9 % in two-phase conditions. These findings highlight the significant role of nanowire length and arrangement in optimizing boiling heat transfer performance. This work lays a foundation for further investigations into diverse nanowire materials and configurations.

Wave trapping by porous breakwater near a rigid wall under the influence of ocean current

The present work investigates the water wave interaction with bottom-standing thick porous trapezoidal-shaped structures and shore-side vertical rigid wall in the presence of uniform ocean currents. This study has been done to understand the impact of different physical parameters like friction, porosity, and ocean currents along with different structural parameters (width and height) on different phenomena like wave energy reflection, wave forces, wave energy dissipation, etc. The quadratic boundary element method-based numerical technique has been used to solve the boundary value problem. The structural porosity is modeled using Sollitt and Cross’s model of water wave interaction with thick porous structures. Several results associated with the wave energy reflection and energy dissipation have been analyzed. Also, the wave force exerted by the incoming waves has been investigated to check the stability and sustainability of the right vertical rigid wall and porous structure. The Doppler-shift effect is observed in wave transformation characteristics due to the presence of ocean currents. The impact of following and opposing ocean currents can be seen in the graph of wave energy reflection, dissipation, and wave forces. The periodic patterns can be observed clearly in wave characteristics like wave energy reflection, dissipation, and wave forces when plotted against the non-dimensional separation gap between the porous breakwater and shore-side rigid seawall.

Learning periodic skills for robotic manipulation: Insights on orientation and impedance

Many daily tasks exhibit a periodic nature, necessitating that robots possess the ability to execute them either alone or in collaboration with humans. A widely used approach to encode and learn such periodic patterns from human demonstrations is through periodic Dynamic Movement Primitives (DMPs). Periodic DMPs encode cyclic data independently across multiple dimensions of multi-degree of freedom systems. This method is effective for simple data, like Cartesian or joint position trajectories. However, it cannot account for various geometric constraints imposed by more complex data, such as orientation and stiffness. To bridge this gap, we propose a novel periodic DMP formulation that enables the encoding of periodic orientation trajectories and varying stiffness matrices while considering their geometric constraints. Our geometry-aware approach exploits the properties of the Riemannian manifold and Lie group to directly encode such periodic data while respecting its inherent geometric constraints. We initially employed simulation to validate the technical aspects of the proposed method thoroughly. Subsequently, we conducted experiments with two different real-world robots performing daily tasks involving periodic changes in orientation and/or stiffness, i.e., operating a drilling machine using a rotary handle and facilitating collaborative human–robot sawing.

An ankyrin G–binding motif mediates TRAAK periodic localization at axon initial segments of hippocampal pyramidal neurons

The axon initial segment (AIS) is a critical compartment in neurons. It converts postsynaptic input into action potentials that subsequently trigger information transfer to target neurons. This process relies on the presence of several voltage-gated sodium (NaV) and potassium (KV) channels that accumulate in high densities at the AIS. TRAAK is a mechanosensitive leak potassium channel that was recently localized to the nodes of Ranvier. Here, we uncover that TRAAK is also present in AISs of hippocampal and cortical neurons in the adult rat brain as well as in AISs of cultured rat hippocampal neurons. We show that the AIS localization is driven by a C-terminal ankyrin G-binding sequence that organizes TRAAK in a 190 nm spaced periodic pattern that codistributes with periodically organized ankyrin G. We furthermore uncover that while the identified ankyrin G-binding motif is analogous to known ankyrin G-binding motifs in NaV1 and KV7.2/KV7.3 channels, it was acquired by convergent evolution. Our findings identify TRAAK as an AIS ion channel that convergently acquired an ankyrin G-binding motif and expand the role of ankyrin G to include the nanoscale organization of ion channels at the AIS.

Stable top-k periodic high-utility patterns mining over multi-sequence

Stable top-k periodic high-utility patterns mining over multi-sequence

Intrinsically Patterned Two-Dimensional Transition Metal Halides.

Patterning and defect engineering are key methods for tuning the properties and enabling distinctive functionalities in two-dimensional (2D) materials. However, generating 2D periodic patterns of point defects in 2D materials, such as vacancy lattices that can serve as antidot lattices, has been elusive until now. Herein, we report on 2D transition metal dihalides epitaxially grown on metal surfaces featuring periodically assembled halogen vacancies that result in alternating coordination of the transition metal atom. Using low-temperature scanning probe microscopy and low-energy electron diffraction, we identified the structural properties of intrinsically patterned FeBr2 and CoBr2 monolayers grown epitaxially on Au(111). Density functional theory reveals that Br vacancies are facilitated by low formation energies, and the formation of a vacancy lattice results in a substantial decrease in the lattice mismatch with the underlying Au(111). We demonstrate that interfacial strain engineering presents a versatile strategy for controlled patterning in two dimensions with atomic precision over several hundred nanometers to solve a long-standing challenge of growing atomically precise antidot lattices. In particular, patterning of 2D materials containing transition metals provides a versatile method to achieve unconventional spin textures with noncollinear spin.

Mining Weighted Patterns from Time Series Databases Based on Sliding Window

Data mining has traditionally relied heavily on sliding window-based challenges, which has sparked a variety of studies. For each new window in time series mining, current literature mandates the rebuilding of the underlying structure, Suffix Tree - A trie-based structure representing all the suffixes of a string. However, reconstruction struggles when the window is wide or when sliding happens frequently. As a result, we provide a new technique Dynamic Tree-Based Approach to handle Sliding Windows (DTSW) in time series in this study that dynamically changes the representative suffix tree structure rather than reconstructing it after every alteration or sliding. In addition, we also put forth a different approach to the issue of extracting weighted periodic patterns from time series. To prevent testing pointless patterns, existing studies mostly rely on the weight of the database's highest-weighted item. However, these methods continue to examine numerous patterns. These methods still examine numerous patterns to see whether they can be candidates. Our proposed measure Maximum Possible Weighted Support (MPWS) accelerates the candidate generation process by removing numerous unnecessary patterns in advance. The novelty of MPWS is it considers the maximum weighted average over the maximum weighted item extension by enforcing more constraints. The usefulness of our two techniques in handling sliding windows and trimming redundant candidate patterns is demonstrated by experimental results using a variety of real-world datasets. Our experiments state that our dynamic handling technique significantly improves runtime than the reconstruction in a dynamic sliding window-based environment with simultaneous insertion and deletion actions and MPWS reduces the number of tested patterns resulting in lesser mining time in weighted time series pattern mining. DUJASE Vol. 8 (1) 13-25, 2023 (January)

Ultrafast laser-induced topochemistry on metallic glass surfaces

Manufacturing multifunctional nanocomposite materials and engineered surface nanopatterns involves a strategic blend of topography, crystal structures, and chemistry. Here, we report the controllable formation of crystalline nanoparticles and intermetallic compounds on thin films of metallic glasses (Zr50Cu50, Ti50Cu50, and Zr67Ag33) irradiated by ultrafast laser beams. Mapping the structural modification of the photoexcited and subsequently heated alloys reveals previously neglected chemical reactions with air, offering a direct solution for incorporating nanoparticles into an amorphous oxide matrix and broadening the range of laser-induced surface self-organization features. Our findings are attributed to the occurrence and enrichment of oxygen surface contamination that reacts with selected elements of the metallic glasses. Additionally, the growth of the crystalline phase from undercooled liquid may originate from the dissolution of oxides. Finally, our results establish that the combination of crystalline nanoparticles on amorphous periodic patterns can be universally obtained in a wide range of binary systems of irradiated metallic glasses.

Nanostructure engineering in organic semiconductor devices toward interface matching

The performance of organic semiconductor devices with heterojunctions between the organic semiconductors and electrodes can be improved by reducing the contact resistance. In this study, we have developed nanopatterned electrodes that gradually change the impedance at the interface between the metal and organic semiconductor in organic devices, which were fabricated in periodic patterns using nanoimprint lithography. The imprint pattern spacing was changed to control the interface between the metal and organic semiconductor to ensure smooth carrier injection. We analyzed the carrier injection based on the pattern spacing of the electrode interface using electrical current–voltage and capacitance–frequency measurements in the diode. Subsequently, we analyzed the improved current mechanism through numerical simulation. Therefore, this study suggests the possibility of designing the interface of an organic device using the nanostructure between the organic semiconductor and carrier injection electrode.

Effects of nonlinearities and geometric imperfections on multistability and deformation localization in wrinkling films on planar substrates

Compressed elastic films on soft substrates release part of their strain energy by wrinkling, which represents a loss of symmetry, characterized by a pitchfork bifurcation. Its development is well understood at the onset of supercritical bifurcation, but not beyond, or in the case of subcritical bifurcation. This is mainly due to nonlinearities and the extreme imperfection sensitivity. In both types of bifurcations, the energy–displacement diagrams that can characterize an energy landscape are non-convex, which is notoriously difficult to determine numerically or experimentally, let alone analytically. To gain an elementary understanding of such potential energy landscapes, we take a thin beam theory suitable for analyzing large displacements under small strains and significantly reduce its complexity by reformulating it in terms of the tangent rotation angle. This enables a comprehensive analytical and numerical analysis of wrinkling elastic films on planar substrates, which are effective stiffening and/or softening due to either geometric or material nonlinearities. We also validate our findings experimentally. We explicitly show how effective stiffening nonlinear behavior (e.g., due to substrate or membrane deformations) leads to a supercritical post-bifurcation response, makes the energy landscape non-convex through energy barriers causing multistability, which is extremely problematic for numerical computation. Moreover, this type of nonlinearity promotes uni-modal, uniformly distributed, periodic deformation patterns. In contrast, nonlinear effective softening behavior leads to subcritical post-bifurcation behavior, similarly divides the energy landscape by energy barriers and conversely promotes localization of deformations. With our theoretical model we can thus explain an experimentally observed phenomenon that in structures with effective softening followed by an effective stiffening behavior, the symmetry is initially broken by localizing the deformation and later restored by forming periodic, distributed deformation patterns as the load is increased. Finally, we show that initial imperfections can significantly alter the local or global energy-minimizing deformation pattern and completely remove some energy barriers. We envision that this knowledge can be extrapolated and exploited to convexify extremely divergent energy landscapes of more sophisticated systems, such as wrinkling compressed films on curved substrates (e.g., on cylinders and spheres) and that it will enable elementary analysis and the development of specialized numerical tools.

Periodic Energy Optimization Using IOT and ML

The rapid expansion of Internet of Things (IoT) applications across various sectors generates an enormous volume of continuous time-series data. However, transmitting this massive amount of sensor data from energy constrained IoT nodes poses a significant challenge. The continuous transmission of such data consumes vast amounts of energy.In this work, we present a solution to this problem by predicting the periodic behavior of sensor data through a higher-level view of continuous transmission data from nodes in IoT at server side. Our system is composed of an IoT sensor network and a data processing unit. The local sensor network: temperature and humidity data is collected using 4 different nodes, as well, which afterward this info is transferred into a data processing unit built on the Raspberry Pi device. We use the machine learning model Autoregressive Integrated Moving Average (ARIMA) on the processing unit. This model is then applied individually to the data from each of the four nodes, predicting processed sensor values in the future accurately. In short, after getting highly accurate prediction, then we settle down proper energy saving pattern which reduces the data transmission requirements hence results in energy saving pattern.By utilizing the predictive capabilities of the ARIMA model, we minimize the need for constant transmission of raw sensor data. Instead, we transmit only essential updates or deviations from the predicted values. This approach substantially reduces energy consumption by eliminating the transmission of redundant information. In summary, our project aims to overcome the energy limitations of IoT sensor nodes by leveraging predictive modelling techniques, specifically the ARIMA model. By accurately predicting periodic patterns in sensor data, we can optimize energy usage by transmitting only the necessary information, while still ensuring effective monitoring of temperature and humidity in the IoT network.

On-Wire Design of Axial Periodic Halide Perovskite Superlattices for High-Performance Photodetection.

Precise synthesis of all-inorganic lead halide perovskite nanowire heterostructures and superlattices with designable modulation of chemical compositions is essential for tailoring their optoelectronic properties. Nevertheless, controllable synthesis of perovskite nanostructure heterostructures remains challenging and underexplored to date. Here, we report a rational strategy for wafer-scale synthesis of one-dimensional periodic CsPbCl3/CsPbI3 superlattices. We show that the highly parallel array of halide perovskite nanowires can be prepared roughly as horizontally guided growth on an M-plane sapphire. A periodic patterning of the sapphire substrate enables position-selective ion exchange to obtain highly periodic CsPbCl3/CsPbI3 nanowire superlattices. This patterning is further confirmed by micro-photoluminescence investigations, which show that two separate band-edge emission peaks appear at the interface of a CsPbCl3/CsPbI3 heterojunction. Additionally, compared with the pure CsPbCl3 nanowires, photodetectors fabricated using these periodic heterostructure nanowires exhibit superior photoelectric performance, namely, high ION/IOFF ratio (104), higher responsivity (49 A/W), and higher detectivity (1.51 × 1013 Jones). Moreover, a spatially resolved visible image sensor based on periodic nanowire superlattices is demonstrated with good imaging capability, suggesting promising application prospects in future photoelectronic imaging systems. All these results based on the periodic CsPbCl3/CsPbI3 nanowire superlattices provides an attractive material platform for integrated perovskite devices and circuits.

Nonlinear dynamics of a laser diode subjected to both optical injection and optical feedback

We analyse theoretically the nonlinear dynamics of a single-mode laser diode subjected to both optical injection and optical feedback. Detailed mappings of the laser dynamics reveal that, due to optical feedback (OF), the locking boundaries resulting from optical injection (OI) shift towards larger negative detunings and higher injection rates and display a periodic pattern of the injection locking boundaries. We demonstrate how feedback induces a cascade of quasiperiodic bifurcations associated with abrupt dynamic changes, hence altering the route to locking. A close inspection of the laser optical spectra for increasing feedback rate reveals the complex interplay between undamped relaxation oscillations and external cavity frequencies.

Chemical heterogeneity size effects at nanoscale on interface thermal resistance of solid–liquid polymer interface via molecular dynamics simulations

The shrinking size of integrated chips poses thermal management challenges. Understanding the size effect of chemical heterogeneity on solid–liquid interfacial thermal transfer is essential for heterogeneous chip design, yet the underlying mechanisms remain lacking. The present work used the liquid n-alkanes as the thermal interface material between solid platinum substrates. To characterize chemical heterogeneity, periodic solid surface patterns composed of patches with alternating solid–liquid affinities were constructed. By using non-equilibrium molecular dynamics simulations, we investigated the size effect of chemically heterogeneous patterns on interfacial thermal resistance (ITR) at the nanoscale. At larger heterogeneity sizes, i.e., larger patch sizes, most alkane molecules directly in contact weak interaction patches cannot interact with strong interaction patches due to long atomic distances. In the case of alkanes in contact a cold substrate, alkanes in contact weak interaction patches transferred thermal energy to the substrate at a lower rate than those in contact strong interaction patches. The different rates resulted in the higher temperature of alkanes in contact weak interaction patches than those in contact strong interaction patches and, therefore, a larger disparity between temperature jump at the strong interaction areas and that at the weak interaction areas. The non-uniformity of temperature jump distribution increased ITR when compared to the heterogeneous surface system characterized by a smaller patch size with a more uniform temperature distribution in the plane perpendicular to the heat flux direction. In addition, the classical parallel thermal resistance model predicted ITR accurately for the heterogeneous surface systems with small size patches but overestimated overall thermal resistance.