Next-generation Applications Research Articles

Neuro-oncology application of next-generation, optically tracked robotic stereotaxis with intraoperative computed tomography: a pilot experience.

Innovations in robotics continue to reshape the landscape of neurosurgery. Here, the authors evaluated the safety and efficacy of the ExcelsiusGPS robot in the treatment of neuro-oncological, intracranial lesions. The authors conducted a retrospective analysis of 19 consecutive adult patients with a neuro-oncological diagnosis who underwent intracranial biopsy and/or laser interstitial thermal therapy (LITT) with the assistance of the ExcelsiusGPS robot and intraoperative CT. Demographic and clinical data were collected from the electronic medical record and the robot software. All 19 patients harbored lesions that were deep seated, involving the eloquent cortex, or subcentimeter. Definitive tissue diagnosis was achieved in all cases involving stereotactic biopsy (n = 16), with glioblastoma as the most common diagnosis. The mean ± SD time for setting up the robotic stereotaxis system was 57.4 ± 10.7 minutes. The mean procedural time after that was 71.6 ± 41.0 minutes for stereotactic needle biopsy and 188.4 ± 61.2 minutes for procedures involving LITT. The mean radial errors of the actual trajectory relative to the planned trajectory at the entry and target points were 0.625 ± 0.443 mm and 0.745 ± 0.472 mm, respectively. There were no procedural complications or new postoperative deficits, although routine postoperative CT showed new hyperdensity at the target site in 3/19 patients (15.7%). All patients who underwent elective procedures were discharged by postoperative day 3 (mean 1.38 ± 0.619 days). There were two 30-day readmissions (pulmonary embolus and general weakness), and neither was attributable to the surgical procedure. The authors' pilot experience with the ExcelsiusGPS robot in neuro-oncology procedures indicates a favorable efficacy and safety profile.

Omnidirectional reflector based on Ta2O5 cylinder-filled IZO mesh structure for flexible and efficient blue TADF top-emission OLEDs

Abstract Flexible top-emission organic light-emitting diodes (f-TEOLEDs) with a high aperture ratio can be used in next-generation wearable electronic applications. However, the advancement of f-TEOLEDs is being hindered by their low light extraction and poor mechanical stability. In this study, we introduce an omnidirectional reflector (ODR) consisting of an Ag/SiO2/Ta2O5 cylinder-embedded indium zinc oxide (IZO) mesh (c-mesh) structure that improves both the light extraction and mechanical flexibility of TEOLEDs using blue thermally activated delayed fluorescence emitters. The proposed ODR achieved a remarkable reflectance of over 96%, particularly in the transverse-electric mode. Furthermore, the Ta2O5 cylinders effectively compensated for the diverse void-induced depths in the IZO mesh, significantly reducing the leakage current between the electrode and the organic layers. In addition, the ODR electrodes exhibited outstanding mechanical stability. Moreover, even after being subjected to 2000 bending cycles over a 5 mm radius, the device luminance changed by less than 20%. Notably, the proposed f-TEOLEDs with Ag/SiO2/c-mesh electrodes demonstrated superior performance, achieving a low turn-on voltage (2.6 V), high current efficiency (33 cd·A−1), and power efficiency of 29.6 lm·W−1. Finally, the devices featured a narrow full width at half maximum of 27 nm under first-order microcavity effects.

2D Ti3C2Tx-MXene and its superstructures as dual-function electrodes for triboelectric nanogenerators for self-powered electronics

Selecting distinct materials to design a tribopositive and tribonegative electrode pair for a triboelectric nanogenerator (TENG) device is a challenging task, as the interfacial electric field causes surface charges to diffuse into the air or within the material itself, leads to charge deterioration. To address these challenges, there is a strong demand for engineered materials and electrode designs that enable effective charge accumulation and trapping to regulate their performance optimally. Herein, we introduce a unique electrode design for a contact-separation (CS) mode triboelectric nanogenerator TENG featuring multilayered Ti3C2Tx MXene/Kapton as tribonegative electrodes. For the tribopositive electrode, we developed an innovative synthesis strategy to create MXene-seeded layered TiO2 superstructures/PVA-based tribopositive electrodes. After optimization of both triboelectric layers, the optimized TENG showed an open-circuit voltage (Voc) ∼ 120 V, short-circuit current (Isc) ∼ 25 μA, and power density of 5.66 W·m−2. The surface terminations groups in highly conductive black MXene nanosheets and the oxygen vacancies in TiO2-layered superstructures provide abundant charge accumulation and trapping sites due to organized multilayered structures at both ends. Moreover, the induced polarization in the tribopositive layer due to the hybrid film could further hinder the free electrons’ drift to the bottom electrode, preventing charge recombination. The optimized TENG was tested as a pressure sensor to monitor different sensitive physiological movements of the human body. Further application of the designed TENG has been revealed by humidity sensing characteristics, powering LEDs, stopwatches, pedometers, and swiftly charging micro-capacitors by utilizing direct output power. By employing a controlled synthesis strategy, our approach leverages the unique properties of MXenes to function as both tribo-positive and tribonegative electrodes within the same device. This dual-function capability simplifies material selection and enhances overall device performance, presenting a transformative solution for next-generation TENG applications.

Analysis of SAR mitigation implementing millimeter wave antenna with FSS-based absorber for 5 G application

In this article, an FSS-based absorber has been developed and utilized to mitigate the specific absorption rate of a millimeter-wave antenna loaded with an inter-digital capacitor (IDC) for 5 G applications. SAR mitigation of millimeter-wave antennas is one of the aspects to be considered for next-generation applications. The addition of a triple-band absorber operating at 28, 60, and 73 GHz reduces the SAR value by blocking radiation on the antenna's backside. This approach lowered the value of the SAR by 75 % and more through the targeted operating bands, diminished significantly the SAR distribution, and increased the antenna gain. In accordance with the Federal Communications Commission and International Commission on Non-Ionizing Radiation Protection (ICNIRP) regulations, a decrease in SAR values signify a decrement in radiation absorption by human tissue, thereby lowering the likelihood of health risks from electromagnetic radiation. Rogers RT-Duroid 5880 serves as the antenna's substrate, while epoxy resin fiber (FR-4) is used for the absorber. The investigation's findings in the constructed antenna sample with the incorporated absorber demonstrate that the modeled and observed results correspond well, demonstrating the viability and effectiveness of the unique design in SAR mitigation in millimeter wave bands.

A Novel 3D 2TnC FeRAM Architecture and Operation Scheme with Improved Disturbance for High-Bit-Density Dynamic Random-Access Memory

In this paper, we proposed the development of stackable 3D ferroelectric random-access memory (FeRAM), with two select transistors and n capacitors (2TnC), to address scaling limitations for bit density growth and the complicated manufacturing of 3D dynamic random-access memory (DRAM). The proposed 3D FeRAM has a 3D NAND-like architecture, with stacked metal–ferroelectric–metal (MFM) capacitors serving as memory cells in a unit string. A similar manufacturing process is used to achieve a cost-effective process and high bit density for next-generation DRAM applications. The two access transistors, string–select–line (SSL) and ground–select–line (GSL), are perfect string selections. We confirmed that the grounded back gate (GBG) of the proposed architecture can significantly improve the worst disturbance case compared to a floating back gate (FBG) like the 1TnC structure. Also, we confirmed the feasibility of performing the random-access operation during the read operation regardless of the data pattern of the selected string.

Programmable orientation of blue phase soft photonic crystal

Understanding the structure formation and its underlying physical mechanism is a fundamental topic in condensed matter systems, with both academic and practical implications. Soft matter is playing a remarkable role in current era of information explosion, demonstrating enormous potential in integrated functional photonics. As unique soft photonic crystals with cubic symmetries, not only liquid crystalline blue phases (BPs) have circularly polarized selective reflection and ultra-fast electro-optical response, but also their three-dimensional photonic structures increase degrees-of-freedom for multiplexed optical modulation. In the thriving field of soft-matter-based photonics, precise and programmable engineering of BP crystal orientation is of vital importance for planar optical elements, which remains a challenging task due to the complexity of the nucleation process as well as the interaction between the BP building blocks and the boundary conditions. Aiming to gain a comprehensive understanding of how to tailor the orientation of BP crystals for the photonic applications of next generation, here we discuss the solutions for uniformity improvement and orientation control of BP crystals, about which a few of examples in combination with the underlying mechanisms are explained. In addition, the remaining challenges and the efforts that are expected are also reviewed. We expect this work provides a deeper understanding of phase transitions and resulting structures in soft crystals, which may open encouraging perspectives for their applications in photonics, biosensing, interfacial, and chemical engineering.

Optimization of InP-based traveling-wave Mach-Zehnder modulator design using artificial neural network and heuristic algorithms

In this study, we report an innovative multi-parameter artificial neural network (ANN) based optimization approach for designing InP-based capacitive loading traveling wave Mach-Zehnder modulators (CL-MZMs). Our ANN-based heuristic algorithm optimization method surpasses traditional manual optical device design in efficiently searching for the optimal solution, based on user-defined figure of merit (FOM) in a large multi-parameter design space, while also providing statistical data-based insight into the underlying complex device physics involved. We achieved an optimized 1 mm InP MZM design, with an anticipated 112 GHz 3-dB electro-optic bandwidth and 5.8 V half-wave voltage, making it a promising candidate for next-generation data center high-speed optical link applications at 400 Gb/s and beyond.

Low-energy photoredox catalysis.

With the advent of photoredox catalysis, new synthetic paradigms have been established with many novel transformations being achieved. Nevertheless, modern photoredox chemistry has several drawbacks, namely, deficiencies in reaction efficiency and scalability. Furthermore, wavelengths of light in excess of the energy required for a chemical reaction are often used. In this Review, we document recent developments of low-energy light-absorbing catalysts and their cognate photochemical methods, advantageously mitigating off-cycle photochemical reactivity of excited-state species in the reaction mixture and improving batch scalability of photochemical reactions. Finally, developments in red-light photoredox catalysis are leading the next-generation applications to polymer science and biochemistry-chemical biology, enabling catalytic reactions within media composites - including mammalian tissue - that are historically recalcitrant with blue-light photoredox catalysis.

Practical hardware demonstration of a multi-sensor goal-oriented semantic signal processing and communications network

Recent advancements in machine learning, particularly real-time extraction of rich semantic information, reshape signal processing techniques and related hardware architectures. To address the highly challenging requirements of next-generation signal processing applications in networked platforms, we investigate low-power hardware implementation alternatives for a multi-sensor, goal-oriented semantic communications network. Specifically, we focus on cost-effective Raspberry Pis in a multi-sensor semantic video communication application, showcasing adaptability from traditional CPU/GPU configurations. Additionally, we provide a preliminary investigation on implementing semantic extraction tasks through in-memory computation using memristor arrays to further emphasize the potential future of low-power low-cost semantic signal processing. Hardware demonstrations using Raspberry Pi 4Bs and simulations with in-memory computation architectures offer promising hardware architectures with cost-effective and low-power sensor alternatives to the next-generation semantic signal processing applications and semantic communication systems.

Wurtzite CuIn(SxSe1-x)2 Nanocrystals: Colloidal Synthesis and Band-Gap Engineering.

CuIn(SxSe1-x)2 nanocrystals as an emerging class of functional materials present huge potential for industrial applications; however, the synthesis of CuIn(SxSe1-x)2 nanocrystals remains a formidable challenge in achieving both tunable band gap and phase. Here, we reported a facile hot-injection method for synthesizing a family of wurtzite CuIn(SxSe1-x)2 nanocrystals, enabling manipulation of the S and Se contents across the entire compositional range (0 ≤ x ≤ 1). The obtained nanocrystals exhibit band gaps ranging from 1.21 to 1.58 eV, which vary depending on the S/Se ratios in the products. This approach can be readily extended to other scenarios involving chalcogenide nanomaterials, thereby facilitating the advancement of next-generation functional materials and applications.

Comparative Analysis of Next-Generation Space Computing Applications on AMD-Xilinx Versal Architecture

Space computing has unique considerations that limit the achievable performance capabilities of onboard processing. Due to these limiting factors, current state-of-the-art devices for space computing are unable to meet the performance and energy-efficiency requirements for next-generation communication, navigation, and artificial-intelligence applications planned for future science and exploration missions. Space designers are transitioning to domain-specific architectures with specialized acceleration hardware, such as the AMD-Xilinx Versal Adaptive System-on-Chip, to address these issues. This heterogeneous platform provides developers with several processing subsystems that allow for mission-specific customization, including scalar processing units, programmable logic, and AI engine technology enabled by vector processing units. In this paper, performance, power-consumption, energy-efficiency, and resource-utilization tradeoffs for each Versal processing subsystem are evaluated by benchmarking a suite of representative next-generation artificial-intelligence and communication applications for space. This evaluation yields a design tradespace that mission designers can use to determine the Pareto-optimal solution for an artificial-intelligence or communication application based on performance requirements and power constraints. Additionally, this evaluation demonstrates that the performance and energy-efficiency benefits of the AI engines for space computing are significant; however, they are curtailed by considerably more power consumption than the scalar processors and programmable logic.

AI-Augmented Chemical Engineering Models for Predictive Battery Maintenance in IoT Applications

This research presents a cutting-edge approach that combines chemical engineering principles with artificial intelligence (AI) to enhance battery maintenance in Internet of Things (IoT) applications. The study develops predictive maintenance models capable of accurately forecasting battery degradation, thereby optimizing usage patterns and extending battery life. By integrating electrochemical data with AI-driven predictive analytics, the project enables real-time monitoring of battery health, achieving over 90% accuracy in anticipating maintenance needs, which reduces unplanned downtime and lowers operational costs. Employing machine learning frameworks, data analysis tools, and chemical engineering simulation software, the focus is on lithium-ion and solid-state batteries, commonly used in IoT networks across sectors such as smart cities, agriculture, and healthcare. Key results include a 20−30% increase in battery lifespan and a 15% improvement in energy efficiency, significantly reducing electronic waste and environmental footprint. This interdisciplinary approach provides a foundation for sustainable battery technology in IoT technology, paving the way for future advancements in AI-augmented predictive maintenance and chemical engineering models for next-generation applications. The integration of AI with chemical engineering principles allows for the development of models that adapt to the unique electrochemical behavior of different battery types, such as lithium-ion and solid-state batteries. By analyzing lifecycle data and device usage patterns, predictive models can account for variables that influence battery performance over time, including temperature fluctuations and charging cycles. This project utilizes neural networks and reinforcement learning algorithms to train models capable of learning from historical data, enhancing their predictive capabilities. A significant aspect of this research involves the preprocessing of battery lifecycle data to ensure high data quality, which is critical for accurate model training and validation.

Mozart: A Mobile ToF System for Sensing in the Dark Through Phase Manipulation

Sensing in low-light and dark environments has a wide range of applications. However, existing sensing technologies suffer several major challenges, such as excessive noise and low resolution. In this work, we propose Mozart - a new mobile sensing system that leverages off-the-shelf Time-of-Flight (ToF) depth cameras to generate high-resolution and rich-in-texture maps for applications in dark scenarios. The design of Mozart is based on our key observation that the phase components of ToF measurements can be manipulated to expose texture information. Through in-depth analysis of the physical reflection model, we show that the textures can be exposed and enhanced using highly compute-efficient phase manipulation functions. Moreover, by exploiting the physics texture models, we propose an autoencoderbased unsupervised learning approach that can automatically learn efficient representations from phase components to generate high-resolution maps. We implemented Mozart on several Android smartphone models and an edge testbed with standalone ToF camera platforms for various applications in the dark. The results show that Mozart can work in real time and delivers significant improvement over existing sensing technologies. The demo video at https:// www.youtube.com/watch?v=L_sxyTZxIdU shows that Mozart offers a low-cost, highperformance sensing technology for nextgeneration applications in the dark.

Comparative performance analysis of recuperative helium and supercritical CO₂ Brayton cycles for high-temperature energy systems

This study presents a comprehensive comparative analysis of recuperative Brayton cycles utilizing helium (He) and supercritical carbon dioxide (sCO2) as working fluids for high-temperature power generation, focusing on applications in next-generation concentrated solar power (CSP) systems. Thermodynamic cycle simulations were conducted across a temperature range of 1000 K–2000 K, exploring the effects of low-side pressure (1 bar–100 bar) and expansion ratio (1–5) on key performance indicators. These indicators included thermal efficiency, net specific work output, mass flow rate, volumetric flow rate, and system irreversibilities. Results demonstrate that helium-based Brayton cycles exhibit superior thermal efficiency at high temperatures, reaching up to 65 %, compared to sCO2 cycles, which achieve efficiencies up to 55 %. This advantage stems from helium's higher specific heat capacity and lower pressure drops within the cycle. Conversely, sCO2 cycles demonstrate higher net specific work output due to the fluid's higher density, leading to more compact turbomachinery. The study further investigates the impact of irreversibilities, including pressure losses, leakage losses, and heat transfer losses, on cycle performance. A detailed analysis of multi-stage Brayton cycles incorporating intercoolers and reheaters reveals the potential for further efficiency enhancements in both helium and sCO2 systems. This research provides valuable insights for the design and optimization of high-temperature power generation systems, particularly for next-generation CSP plants. The findings highlight the trade-offs between working fluid properties and cycle configurations, guiding the selection of optimal solutions based on specific application requirements and operating conditions.

Radiation Hardened Read-Stability and Speed Enhanced SRAM for Space Applications

With the advancement of CMOS technology, the susceptibility of SRAM to single node upset (SNU), double node upset (DNU), and multiple node upset (MNU) induced by radiation has increased. To address this issue, various cutting-edge solutions, such as radiation hardened sextuple cross coupled (RHSCC)-16T and DNU-completely-tolerant memory (DNUCTM) cells, have been proposed. While the RHSCC-16T cell is robust against SNU, it may be vulnerable to DNU. The DNUCTM cell is resistant to both SNU and DNU, but it remains susceptible to MNU. In this paper, we propose a radiation hardened read-stability and speed enhanced (RHRSE)-20T SRAM, which is immune to all potential cases of SNU, DNU, and MNU. Additionally, the proposed design demonstrates improvements in read and write delays compared to conventional SRAM designs. Experimental results confirm that the RHRSE-20T SRAM maintains stability under various charge levels for SEU, DNU, and MNU. The proposed integrated circuit is implemented in a 90-nm CMOS process and operates on a 1 V supply voltage, offering significant advantages for next-generation radiation-hardened memory applications.

Security with Wireless Sensor Networks in Smart Grids: A Review

Smart Grids are an area where next-generation technologies, applications, architectures, and approaches are utilized. These grids involve equipping and managing electrical systems with information and communication technologies. Equipping and managing electrical systems with information and communication technologies, developing data-driven solutions, and integrating them with Internet of Things (IoT) applications are among the significant applications of Smart Grids. As dynamic systems, Smart Grids embody symmetrical principles in their utilization of next-generation technologies and approaches. The symmetrical integration of Wireless Sensor Networks (WSNs) and energy harvesting techniques not only enhances the resilience and reliability of Smart Grids but also ensures a balanced and harmonized energy management system. WSNs carry the potential to enhance various aspects of Smart Grids by offering energy efficiency, reliability, and cost-effective solutions. These networks find applications in various domains including power generation, distribution, monitoring, control management, measurement, demand response, pricing, fault detection, and power automation. Smart Grids hold a position among critical infrastructures, and without ensuring their cybersecurity, they can result in national security vulnerabilities, disruption of public order, loss of life, or significant economic damage. Therefore, developing security approaches against cyberattacks in Smart Grids is of paramount importance. This study examines the literature on “Cybersecurity with WSN in Smart Grids,” presenting a systematic review of applications, challenges, and standards. Our goal is to demonstrate how we can enhance cybersecurity in Smart Grids with research collected from various sources. In line with this goal, recommendations for future research in this field are provided, taking into account symmetrical principles.

NaLiFe(C2O4)2: A polyanionic Li/Na-ion battery cathode exhibiting cationic and anionic redox

Recently, polyanionic compounds have received great interest as alternative cathode materials to conventional oxides due to their different advantages in cost, safety, structural stability, as well as being environmentally friendly. However, the vast majority of polyanionic materials reported so far rely exclusively upon the redox reaction of the transition metal for lithium/sodium transfer.The development of multielectron redox-active cathode materials is a top priority for achieving high energy density with long cycle life in the next-generation secondary battery applications. Triggering anion redox activity is a promising strategy to enhance the energy density of polyanionic cathode materials for Li/Na-ion batteries. In addition to transition metal redox activity, the oxalate group also shows redox behavior enabling reversible charge/discharge and high capacity without gas evolution.Herein, we report NaLiFe(C2O4)2 as a new positive electrode and use different characterization techniques such as Raman spectroscopy and Mössbauer analyses to characterise this dual-ion redox process experimentally. First-principles calculations also help to understand the interactions between the transition metal and the oxalate group as the main factor that modulates the cationic and polyanionic redox couples in these materials.

An Enhanced Particle Swarm Optimization-Based Node Deployment and Coverage in Sensor Networks.

Positioning, coverage, and connectivity play important roles in next-generation wireless network applications. The coverage in a wireless sensor network (WSN) is a measure of how effectively a region of interest (ROI) is monitored and targets are detected by the sensor nodes. The random deployment of sensor nodes results in poor coverage in WSNs. Additionally, battery depletion at the sensor nodes creates coverage holes in the ROI and affects network coverage. To enhance the coverage, determining the optimal position of the sensor nodes in the ROI is essential. The objective of this study is to define the optimal locations of sensor nodes prior to their deployment in the given network terrain and to increase the coverage area using the proposed version of an enhanced particle swarm optimization (EPSO) algorithm for different frequency bands. The EPSO algorithm avoids the deployment of sensor nodes in close proximity to each other and ensures that every target is covered by at least one sensor node. It applies a probabilistic coverage model based on the Euclidean distances to detect the coverage holes in the initial deployment of sensor nodes and guarantees a higher coverage probability. Delaunay triangulation (DT) helps to enhance the coverage of a given network terrain in the presence of targets. The combination of EPSO and DT is applied to cover the holes and optimize the position of the remaining sensor nodes in the WSN. The fitness function of the EPSO algorithm yielded converged results with the average number of iterations of 78, 82, and 80 at 3.6 GHz, 26 GHz, and 38 GHz frequency bands, respectively. The results of the sensor deployment and coverage showed that the required coverage conditions were met with a communication radius of 4 m compared with 6-120 m with the existing works.

Unveiling Versatile Electronic Properties and Contact Features of Metal-Semiconductor Graphene/γ-Ge2SSe van der Waals Heterostructures.

Recently, searching for a metal-semiconductor junction (MSJ) that exhibits low-contact resistance has received tremendous consideration, as they are essential components in next-generation field-effect transistors. In this work, we design a MSJ by integrating two-dimensional (2D) graphene as the metallic electrode and 2D Janus γ-Ge2SSe as the semiconducting channel using first-principles simulations. All the graphene/γ-Ge2SSe MSJs are predicted to be energetically, mechanically, and thermodynamically stable, characterized by the weak van der Waals (vdW) interactions. The graphene/γ-SGe2Se MSJ-vdWH form the n-type Schottky contact (SC), while the graphene/γ-SeGe2S MSJ-vdWH form the p-type one, suggesting that the switching between p-type and n-type SC in the graphene/γ-Ge2SSe MSJ-vdWHs can occur spontaneously by simply altering the stacking patterns, without requiring any external conditions. Notably, the contact features, including contact types and barriers of the graphene/γ-Ge2SSe MSJs are significant in versatility and can be altered by applying electric gating and adjusting interlayer spacing. Both the applied electric gating and strain engineering induce switchability between p- and n-type and SC to OC in the graphene/γ-Ge2SSE MSJs. This versatility underscores the potential of the graphene/γ-Ge2SSe MSJ for next-generation applications that require low-contact resistance and high performance.

Interfacial thermal resistance in stanene/ hexagonal boron nitride van der Waals heterostructures: A molecular dynamics study

Recently, the stanene (Sn)/hexagonal boron nitride (h-BN) van der Waals heterostructure (vdW) has garnered significant attention among the scientific community due to its distinctive electrical, optical, and thermal characteristics. Despite the promising potential of this heterostructure, the interfacial thermal resistance (ITR) between the Sn and h-BN layers remains unexplored. Understanding and modulating this ITR are essential steps towards harnessing the maximum potential of these materials in practical nanodevices. This study aims to investigate the interfacial thermal resistance (ITR) between the Sn and h-BN layers through the use of conventional molecular dynamics (MD) simulation. The transient pump–probe heating technique, commonly referred to as the Fast Pump Probe (FPP) approach, is utilized to analyze the ITR of the Sn/h-BN heterostructure. The estimated ITR value of a 30 × 10 nm2 Sn/h-BN nanosheet is found to be around ∼ 7 × 10-8 K.m2/W at room temperature. This study comprehensively investigates the impact of various internal and external parameters including nanosheet size, system temperature, contact pressure, vacancy concentration, and mechanical tensile strain (uniaxial and biaxial) on ITR, providing an extensive understanding of how these factors collectively affect the thermal resistance between Sn and h-BN layers. The simulationresults demonstrate a consistent decline in ITR by approximately ∼ 93 %, ∼45 %, ∼65 %, and ∼ 33 % with the increasing system size, temperature, contact pressure, and defect concentration, respectively. In contrast, increasing mechanical strain leads to a substantial enhancement in ITR, with a maximum increase of approximately ∼ 47 % under uniaxial tensile strain and almost ∼ 99 % under biaxial tensile strain. Moreover, the pristine Sn/h-BN heterostructure exhibits no significant thermal rectification effect. The Phonon Density of States (PDOS) profile of the Sn and h-BN layer is calculated to elucidate this underlying behavior of ITR. The PDOS analysis reveals that heat is transported from h-BN to the Sn layer through efficient coupling of low-frequency flexural phonons between these two materials. This work will provide both theoretical support and logical guidelines for modulating thermal resistance across diverse dissimilar material interfaces, which will be necessary for the development of advanced nanodevices used in next-generation nanoelectronics, nanophotonic, and optoelectronics applications.