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Thermal transport plays a pivotal role across diverse disciplines, yet the intricate relationship between amorphous network structures and thermal conductance properties remains elusive due to the absence of a reliable and comprehensive network's dataset to be investigated. In this study, we have created a dataset comprising multiple amorphous network structures of varying sizes, generated through a combination of the node disturbance method and Delaunay triangulation, to fine-tune an initially random network toward both increased and decreased thermal conductance C. The tuning process is guided by the simulated annealing algorithm. Our findings unveil that C is inversely dependent on the normalized average shortest distance L n o r m connecting heat source nodes and sink nodes, which is determined by the network topological structure. Intuitively, the amorphous network with increased C is associated with an increased number of bonds oriented along the thermal transport direction, which shortens the heat transfer distance from the source to sink node. Conversely, thermal transport encounters impedance with an augmented number of bonds oriented perpendicular to the thermal transport direction, which is demonstrated by the increased L n o r m . This relationship can be described by a power law C = L n o r m α , applicable to the diverse-sized amorphous networks we have investigated. [ABSTRACT FROM AUTHOR]

The thermal and electrical properties of photovoltaic cell (PVC) under linear phenomenological heat transfer law between it and the environment is studied through finite time thermodynamics and the volt-ampere characteristic equation. The properties of PVC are affected by heat transfer between PVC and environment. There are optimal solar radiation intensity and PVC output voltage (OV), which make the photoelectric conversion efficiency (PECE) of PVC reach the highest value. When OV and solar radiation intensity are 28.50 V and 700 W/m2, the maximum PECE is 0.156. There is also the best solar radiation intensity, which makes the open-circuit voltage (OCV) reach the maximum. When solar radiant intensity is 669 W/m2, the maximum OCV is 33.14 V. The values of power output and short-circuit current (SCC) are monotonically increasing with solar radiation intensity. Given solar radiation intensity, the power output and OV exhibit a parabolic shape. The operating temperature falls first and then grows with the OV. However, the change of operating temperature with OV is not much. Band gap is a decreasing function of operating temperature. This article can give theoretical support for the design and use of PVCs. [ABSTRACT FROM AUTHOR]

Microwave heating has been applied to improve cement-based materials (CBMs) structures deicing efficiency, which reduce the effect of icing on their service life. Moreover, the enhanced thermal properties of CBMs can further improve their deicing efficiency. In this study, silicon carbide (SiC) ceramic particles with favorable thermal properties were used to prepare the SiC cement-based materials (SCBMs). The effect of SiC ceramic on the mechanical strength of CBMs was investigated. Next, the heat transfer and storage ability of CBMs were investigated to analyze the changes in their thermal-physical parameters. Then, the surface thermal responses of the CBMs during microwave heating and off were recorded to characterize their temperature evolution in a low-temperature condition. Furthermore, the deicing efficiency of CBMs was determined using the ice layer shedding time (ILST) as the index. The results show that the filling effect of SiC ceramic on CBMs improved their mechanical strength within a certain content range. However, a large content of SiC ceramic caused an increase in the weak area and porosity within the CBMs, further reducing their mechanical properties. The thermal-physical parameters of CBMs were improved after adding SiC ceramic. The thermal conductivity and thermal effusivity of SCBMs-100 were 13.17 W/(m·K) and 1.44 W/(mm2·K), respectively, 7.90 and 2.35 times higher than the control group. Due to the favorable thermal properties of SCBMs, their heating rate and surface temperature field distribution characteristics were further improved. Furthermore, with the increase of SiC ceramic content, the ILST of SCBMs was further reduced during microwave heating. Therefore, the SiC ceramic was a favorable material, improving the thermal properties and deicing efficiency of CBMs to a certain extent. However, considering its effect on the deterioration of the mechanical properties of CBMs, the optimal content of SiC ceramic might be 75%. [ABSTRACT FROM AUTHOR]

In the realm of high-tech materials and energy applications, accurately measuring the transient heat flow at media boundaries and the internal thermal conductivity of materials in harsh heat exchange environments poses a significant challenge when using conventional direct measurement methods. Consequently, the study of photothermal parameter reconstruction in translucent media, which relies on indirect measurement techniques, has crucial practical value. Current research on reconstructing photothermal properties within participating media typically focuses on single-objective or time-invariant properties. There is a pressing need to develop effective methods for the simultaneous reconstruction of time-varying thermal flow fields and internal thermal conductivity at the boundaries of participating media. This paper introduces a computational model based on the numerical simulation theory of internal heat transfer systems in participating media, stochastic particle swarm optimization algorithms, and Kalman filter technology. The model aims to enable the simultaneous reconstruction of various thermal parameters within the target medium. Our results demonstrate that under varying levels of measurement noise, the inversion results for different target parameters exhibit slight oscillations around the true values, leading to a reduction in reconstruction accuracy. However, overall, the model demonstrates robustness and accuracy in ideal conditions, validating its effectiveness. [ABSTRACT FROM AUTHOR]

Transient vehicle dynamics are deeply affected by the spatial distribution of tire temperature, heat generation, and heat dissipation capacity. However, the tire testing process involves numerous parameters, adding the complexity to monitoring the tire's thermal properties variation. To address this issue, this paper proposes a data-driven framework that combines the well-known Magic Formula with scaling factors and a long- and short-term memory (LSTM) neural network. The framework forecasts tire thermal properties and functionality under given conditions by learning from time-series data on tire operating conditions and mechanical characteristics. It predicts future spatially varying thermal properties of the tire, including temperatures, heat generation, and heat dissipation. This is achieved with a high degree of forecasting ability quantified by train size/test size ratios equaling 22 and 38% with an impressive Relative Root Mean Square Error (RRMSE) around 1−2%. Here, this framework is applied to quantify the impacts of various operating conditions, including vertical load, slip angle, and vehicle speed, on heat transfer for three crucial radial positions on the tire. Our results show that the vertical load is the primary influencing parameter among all operating conditions. The knowledge about transient tire dynamics will be advantageous to their performance optimization. [ABSTRACT FROM AUTHOR]

A hybrid nanofluid (HYNF) is an excellent working medium for improved heat transfer performance. To investigate the convective heat transfer performance of an HYNF of various particle mixing ratios (MR), at first samples of MgO-SiO2/water HYNF are prepared by varying the MR of MgO to SiO2 as 0:100, 40:60, 50:50, 60:40, and 100:0 at 0.05 mass % of concentration; then convective heat transfer characteristics are determined experimentally by exposing different mass flows of HYNF from 0.03–0.27 kg/s to a constant heat flux of 5630 W/m2. The results describe that the variation in MR strongly influences the Nusselt number (Nu) of HYNF. The maximum Nu, which is 1.94 times (~ 94.50%) higher than the same with base fluid, is obtained at MR of 60:40 and the mass flow rate of 0.13 kg/s. The variation of MR does not affect the friction factor of HYNF and it is nearly comparable to that of the base fluid. Further, a new concept has been proposed to determine the thermo-economic feasibility of HYNF in heat transfer applications. It was recommended to maintain the MR at 60:40 and 0.13 kg/s mass flow rate for optimum thermo-economic performance. [ABSTRACT FROM AUTHOR]

Modelling effective thermal properties is crucial for optimizing the thermal performance of materials such as new green insulating fibrous media. In this study, a numerical model is proposed to calculate the effective thermal conductivity of these materials. The fibers are considered to be non-overlapping and randomly oriented in space. The numerical model is based on the finite element method. Particular attention is paid to the accuracy of the results and the influence of the choice of the representative elementary volume (REV) for calculation (cubic or rectangular parallelepiped slice). The calculated effective thermal conductivity of fibrous media under different boundary conditions is also investigated. A set of usual mixed boundary conditions is considered, alongside the uniform temperature gradient conditions. The REV rectangular slice and uniform temperature gradient boundary conditions provide a more accurate estimate of the effective thermal conductivity and are therefore recommended for use in place of the usual cubic representative elementary volume and the usual mixed boundary conditions. This robust model represents a principal novelty of the work. The results are compared with experimental and analytical data previously obtained in the literature for juncus maritimus fibrous media, for different fiber volume fractions, with small relative deviations of 7%. Analytical laws are generally based on simplified assumptions such as infinitely long fibers, and may neglect heat transfer between different phases. Both short and long fiber cases are considered in numerical calculations. [ABSTRACT FROM AUTHOR]

Luminescence thermal quenching (TQ) is one of the most critical problems to be solved for further improvement of phosphors' applications in lighting and many other fields. Herein, a novel strategy is demonstrated to achieve outstanding anti‐TQ performance with a substantial enhancement of Eu3+ red emission at 613 nm from Sc2MO3O12:Tb3+,Eu3+ phosphor. Its anti‐TQ performance is endowed by dual energy transfer (ET) pathways and intensified by the negative thermal expansion (NTE) property of the Sc2MO3O12 host. Remarkably, the photoluminescence (PL) emission intensity of Eu3+ from Sc2MO3O12:20%Eu3+,2%Tb3+ phosphor at 648 K reaches 507.3% of the initial intensity taken at 298 K. The lifetime of Eu3+ emission at 613 nm elongates with increasing measurement temperature. The experimental data and density functional theory (DFT) calculations reveal that the host structure shrinkage via NTE leads to the thermally boosted Eu3+ red emission by intensifying the consecutive ET and confinement of the absorption light. The potential of this phosphor as a dual‐mode high temperature thermometer based on both emission lifetime and intensity ratio read‐out modes is realized. This work provides inspiration to combine multiple strategies to achieve broad and dramatic anti‐TQ phosphors with enhanced performance for various optical applications. [ABSTRACT FROM AUTHOR]

Efficiently thermal management in electronic devices calls for creating polymer composites that have superior thermal transport abilities. Compared with single-filler composites, hybrid-filler composites have gained significant attention as they not only utilize the advantages of each filler individually but also allow for the potential cooperation in constructing filler networks between them. Differing from the previous research on hybrid-filler size, geometry, composition, and content and their impact on thermal conductivity (TC) in composites, this study aims to examine the spatial distribution effects of hybrid-fillers. A binary-filler strategy comprising of anisotropic 2D h- BN and 0D isotropic S–Al 2 O 3 has been proposed, with the primary h -BN filler aligned to filly utilize its anisotropy and intrinsic TC properties. Furthermore, positioning a small quantity of S–Al 2 O 3 between adjacent parallel h -BN considerably improves TC in the non-oriented direction of silicon rubber (SiR) composites. A special focus is given to the influence of the interstitial S–Al 2 O 3 filler on the individual through-plane TC (λ ⊥) and in-plane TC (λ ⫽) variation of the binary-filler hybrid SiR composites, which are perpendicular and parallel to the oriented h -BN. When incorporating 40 vol% h -BN and 5 vol% S–Al 2 O 3 , the binary SiR composites exhibit the highest concurrent λ ⫽ and λ ⊥ values of 14.581 and 2.132 W/m·K, respectively. The corresponding effects on the ductility, hardness, and dielectric properties of the SiR composites were explained. Moreover, the use of commercially available TC fillers, along with the effortless preparation method, enables the potential to produce h -BN based TIMs materials on a large scale for thermal management usage. [Display omitted] • A binary filler system consisting in-plane oriented 2D h -BN and 0D isotropic S–Al 2 O 3 is designed. • The addition of S–Al 2 O 3 filler between adjacent parallel h -BN flakes facilitates vertical heat transfer. • The binary structure of h -BN/S–Al 2 O 3 exhibits an anisotropic feature in the filler contact thermal resistances. • The binary filler strategy mitigates the trade-off between in-plane and through-plane thermal conductivity. [ABSTRACT FROM AUTHOR]

This study explores the use of copper foam (CF) as a thermally conductive scaffold for phase change materials (PCMs), enhancing shape stability. Employing the ice template method (ITM), we fabricate a CF with a low-density, honeycomb structure and anisotropy from flake copper powders. The resulting phase change composites (PCCs), infused with paraffin wax (PW), showcase high thermal conductivity and shape retention compared to pure PW, with anisotropic heat flow due to directional channels in the CF. With a 34.4 wt% filling ratio, the PCCs achieve axial and lateral thermal conductivities of 2.85 W m−1 K−1 and 1.51 W m−1 K−1, respectively, and a thermal storage enthalpy of 158.18 J g−1. This work presents a novel method for advanced thermal energy storage and management, indicating the promise of ITM-derived CF in high-performance PCCs for diverse applications. • 3D porous copper with lighter weight, honeycomb-like and directional structure was prepared. • The composite has good form-stability and anti-leakage property. • The composite indicates high thermal conductivity of 2.85 W m−1k−1. • The composite indicates heat storage capability of 158.18 J g−1. • The composite has good anisotropic thermal conductivity. [ABSTRACT FROM AUTHOR]

CFRP composite laminates decompose by pyrolysis/oxidation when exposed to high heat fluxes. While gaseous and solid residues created within the composite material are ignored in most models, a multi-species approach is proposed to describe transient decompositions from the virgin to the final charred state of the material. The model is based on a porous medium description that includes gas creation and transport within the laminate, respectively driven by Arrhenius and Darcy's laws. Orthotropic heat transfer and decomposition gas transport within the laminate up to the surface remain often uninvestigated while it can drastically act upon the dynamics of a fire event. An experiment is carried out to analyze multi-dimensional heat and mass transfer during the charring processes of a composite laminate exposed to a high power laser beam. Transient temperature measurements on the unexposed surface of the material are confronted to 3D unsteady numerical results to validate the model formulation. • 3D numerical solver of heat and mass transfer within decomposing composite material. • Microstructure-based homogenization technique of thermal conductivity tensor. • Characterization of physical and chemical properties of virgin and charred laminates. • Experimental and numerical restitution of laser-induced material decomposition. [ABSTRACT FROM AUTHOR]

• Measured through-thickness thermal conductivity of 2 × 2 twill carbon fabric using C-Therm Trident system with MTPS sensor. • Thermal conductivity is reduced by 38% under vacuum, attributed to presence of air in the porous structure of the fabric. • Layup thickness/configuration have negligible impact on thermal conductivity due to limited heat penetration. • Higher fabric compaction increases thermal conductivity owing to improved fibre nesting and contact. For high-fidelity simulation of heat transfer responses during various composite manufacturing processes involving heat, thermal conductivity (TC) of dry carbon weaves is essential. In this paper, the through-thickness TC properties of a 2 × 2 carbon twill weave are presented, employing a state-of-the-art C-Therm Trident module equipped with a Modified Transient Plane Source (MTPS) sensor. The effects of air presence, layup thickness, layup configuration, and fabric compaction on TC are investigated. Results reveal negligible impacts of layer number and configuration on TC. However, TC is significantly influenced by air presence and stack compaction. These findings provide improved comprehension of heat transfer in textile composites for more accurate process modelling and simulation using homogenized models. [ABSTRACT FROM AUTHOR]

Two-dimensional (2D) nanosheets, such as graphene and hexagonal boron nitride, are considered as the most promising fillers for enhancing thermal conductivity of polymers and phase-change materials. Nevertheless, the effect of various 2D nanosheets on the effective thermal conductivity of composites is not fully understood, and the corresponding prediction model is still lacking, since numerous influence factors and complex thermal transfer networks are involved. This paper aims to study the macroscopically effective thermal conductivity of the nanosheets-reinforced composites in a systematical way, and develop a robust machine learning based prediction model. To this end, a series of representative volume elements are reconstructed based on the SEM observations of experimental samples, and high-throughput simulations are performed via the updated lattice Boltzmann scheme proposed in our recent work. The effects of shape, size, orientation, intrinsic thermal conductivity, interface thermal resistance, surface coating, and hybrid filling of the 2D nanosheets are clarified. This work could provide a deep insight into the effective thermal conductivity of the nanosheets-reinforced composites, and may offer important guidelines for the custom-design of polymer and phase-change composites with targeted thermal performances. [Display omitted] [ABSTRACT FROM AUTHOR]

The prediction of the prevailing self-heat transfer parameters of a glass/epoxy composite coupon during fatigue testing in general and the distinction between viscoelastic- and frictional crack growth-related energy dissipation in particular, are not trivial problems. This work investigates the feasibility of predicting the convective film coefficient, the total work loss as well as the ratio between viscoelastic and fracture-induced damping from thermal images using Bayesian optimization in conjunction with 3D FE thermal analysis. To this end, glass fiber/epoxy biax coupons are pre-damaged by means of a drop weight impact machine and subsequently tested under uniaxial tension-tension high cycle fatigue conditions. IR images are taken of the self-heating thermal profile at steady-state conditions. Synthetic surface thermal images are generated by numerical thermal analysis of the damage distribution obtained by μ -CT scanning prior to testing. Bayesian optimization of the aforementioned parameters is conducted by minimizing the loss function between the as-measured and the synthetic IR image. The predicted work-loss is consequently validated against the measured hysteretic response, from which a very good agreement is found. [Display omitted] • Thermal heat transfer parameters are predicted via Bayesian optimization (BO). • FEM and BO are combined to find physically meaningful parameters. • The method efficiently handles 3D parameters compared to other approaches. [ABSTRACT FROM AUTHOR]

The electronics in logging tools are susceptible to thermal failure due to the extreme thermal environment. Passive thermal management system (PTMS) has been proven to protect the electronics for several hours. In this paper, a numerical model based on the equivalent thermal conductivity method was developed to determine the transient temperature of downhole electronics. Compared with the traditional analytical model or numerical model considering only one single heat transfer process, the complex heat transfer in the logging tool were comprehensively considered through the proposed model. Briefly, the heat transfer processes in the vacuum bottle were theoretically calculated rather than empirically acquired, and the convective and radiative heat transfer inside the vacuum bottle was equated to the temperature-dependent thermal conductivity of air. In addition, the proposed numerical model was compared with experiments and previous numerical models to verify the accuracy of the model, and the temperature deviation of the electronics was within 5 °C. Moreover, the computational time was greatly reduced compared with the traditional three-dimensional numerical simulation process while ensuring the accuracy of calculation. Therefore, the proposed numerical model achieved both computational accuracy and efficiency, which was expected to be widely used in the development of PTMS for logging tools. • A numerical model based on equivalent thermal conductivity method was proposed. • The complex heat transfer processes in PTMS were comprehensively considered. • The proposed model possessed high computational accuracy and efficiency. [ABSTRACT FROM AUTHOR]

U-bend deep borehole heat exchanger (DBHE) is a new promising kind of ground heat exchanger, but nowadays there are lack of accurate efficient models to simulate U-bend DBHE. This paper has developed a semi-analytical model of U-bend DBHE, which considers the unsteady heat transfer of borehole, heterogenous soil thermal properties and geothermal gradient. A 2D numerical model and an analytical model based on infinite line-source theory are also developed, and the three models are compared with experimental data for different boundary conditions. Based on the soil thermal properties estimated by logging data and rock identification, the three models have large errors when compared with experimental data. Based on a soil thermal conductivity λ s estimated by matching experimental data, the three models match better with experimental data, and it is inferred that λ s should be measured by thermal response test instead of rock identification. The semi-analytical model and 2D numerical model match very well for all the time, but the analytical model has large differences with 2D numerical model, which is because the analytical model ignores borehole heat capacity and time-varying heat flows at the borehole wall. This paper could provide a cost-effective semi-analytical model and some advices for U-bend DBHE. [ABSTRACT FROM AUTHOR]

U-bend deep borehole heat exchanger (DBHE) is a new promising kind of ground heat exchanger, but nowadays there are lack of accurate efficient models to simulate U-bend DBHE. This paper has developed a semi-analytical model of U-bend DBHE, which considers the unsteady heat transfer of borehole, heterogenous soil thermal properties and geothermal gradient. A 2D numerical model and an analytical model based on infinite line-source theory are also developed, and the three models are compared with experimental data for different boundary conditions. Based on the soil thermal properties estimated by logging data and rock identification, the three models have large errors when compared with experimental data. Based on a soil thermal conductivity λ s estimated by matching experimental data, the three models match better with experimental data, and it is inferred that λ s should be measured by thermal response test instead of rock identification. The semi-analytical model and 2D numerical model match very well for all the time, but the analytical model has large differences with 2D numerical model, which is because the analytical model ignores borehole heat capacity and time-varying heat flows at the borehole wall. This paper could provide a cost-effective semi-analytical model and some advices for U-bend DBHE. [ABSTRACT FROM AUTHOR]

We introduce the concept of physics-guided transfer learning to predict the thermal conductivity of an additively manufactured short-fiber reinforced polymer (SFRP) using micro-structural characteristics extracted from tensile tests. Developing composite manufacturing digital twins for SFRP composite processes like extrusion deposition additive manufacturing (EDAM) require extensive experimental material characterization. Even the same material system printed on different EDAM systems can result in significant changes to the printed micro-structure, affecting the mechanical and transport properties. This, in turn, makes characterization efforts expensive and time-consuming. Therefore, the objective of the paper is to address this experimental bottleneck and use prior information about the material manufactured in one extrusion system to predict its properties when manufactured in another system. To enable this framework, we assume that changes in properties of the same material when manufactured in different systems arise solely due to microstructural changes. To that end, we present a Bayesian framework that can transfer thermal conductivity properties across extrusion deposition additive manufacturing systems. While we discuss the transfer of thermal conductivity properties, the development is such that the framework can be used for the transfer of other properties that depend on microstructural characteristics defined in the manufacturing process. These include the coefficient of thermal expansion, viscoelastic properties, etc. The framework begins by using thermal conductivity data of the composite printed in one extrusion system to probabilistically infer the constituent thermal properties of the fiber and the polymer. Next, by conducting limited tensile tests of the same material printed in another extrusion system, we infer its orientation tensor. Finally, the inferred constituent thermal conductivity properties and the inferred orientation tensor are coupled using a micromechanics model to predict the thermal conductivity properties of the composite printed in the second extrusion system. We experimentally verify the predictions and show that our method provides a reliable framework for transferring material properties while accounting for epistemic and aleatory uncertainties. • A probabilistic framework to transfer material information across manufacturing systems. • A framework to account for surrogate uncertainty in parameter inference. • Demonstrate transfer of thermal conductivity across additive manufacturing systems. • Applications to transfer other properties including coefficient of thermal expansion. [ABSTRACT FROM AUTHOR]

This work presents experimental investigation on specific heat capacity and thermal conductivity as a function of temperature and vertical density profile over the sample cross section of oriented strand board, particle board, medium density fibreboard, and plywood. A possibility of introducing an universal set of parameters describing various EWPs by averaging the parameters of the four studied materials obtained in this work is investigated. A one-dimensional heat transfer simulation sensitivity study is performed using the experimentally measured values. The study aims to investigate the effect of using spatial- and temperature-dependent properties compared to constant values and to study the importance of these parameters and their combination in predicting one-dimensional heat transfer below the thermal decomposition temperature of engineered wood products. [ABSTRACT FROM AUTHOR]

[Display omitted] • Thermal transport at pillared-graphene (PG)/epoxy polymer (EP) interface is investigated. • We detected some fantastic phenomena at the pillared-graphene (PG)/epoxy polymer (EP) interface. • We detect the number of PG layers was revealed to be one of the most influential elements on ITC. • It predicts the performance of thermal design for PG/epoxy polymer (EP) interface. We investigate the thermal properties of pillared-graphene (PG)/epoxy polymer (EP) interface architectures by using reverse non-equilibrium molecular dynamics. The results show that the interface thermal conductance (ITC) of the PG/EP contact is 147.85 MW/m2 K. The ITC between PG and EP can be substantially enhanced when optimizing the number and length of PG interlayer single-walled carbon nanotubes. In addition, we detected the number of PG layers was revealed to be one of the most influential elements on ITC. The findings of this study offer valuable insights for optimizing thermal transfer interfaces in electronic device thermal design. [ABSTRACT FROM AUTHOR]

Nanofluid has emerged as a promising heat transfer fluid (HTF) due to their significant thermophysical, and optical characteristics enhancement over base fluids. Hybrid nanofluids with multiple nanomaterials have the advantage of synergistic properties in comparison to monocomponent nanofluids. The present study proposes an energy-efficient and cleaner synthesis method for developing carbon quantum dot (C-dot), MXene, and a hybrid MXene/C-dot hybrid nanofluids, for heat transfer application. In-situ microwave pyrolysis technique and two-step method were adopted for nanomaterial and nanofluid synthesis. The morphological, phase structural, chemical, and elemental compositional analysis of the nanomaterials was performed. The material characterization confirms the hybridization of C-dot on MXene nanosheets. The thermal conductivity and volumetric heat capacity of the nanofluids were measured using the transient plane source (TPS) method. Thermal conductivity was observed to increase with nanofluid concentration and temperature. Results indicate that MXene has the highest thermal conductivity enhancement (50 %) over water, followed by hybrid (42.2 %) and C-dot nanofluid (33.2 %). The volumetric heat capacity of nanofluids decreased with concentration and temperature. A semi-empirical correlation, as a function of nanofluid concentration and temperature, was coined for predicting thermal conductivity and volumetric heat capacity. Optical property characterization study shows that C-dot nanofluid exhibited considerable absorption along the UV range, while MXene nanofluid showed absorption in the visible and near-infrared (NIR) region. Hybrid nanofluids demonstrated complementary absorption properties of C-dot and MXene nanofluids. [Display omitted] • Novel MXene/C-dot hybrid nanofluid was synthesized for heat transfer applications. • Study reveals the material and thermal-optical properties of C-dot, MXene and hybrid nanofluids. • Semi-empirical correlation were coined to predict thermal properties of nanofluids. • Highest thermal conductivity enhancement was exhibited by 0.2 wt% MXene nanofluid. • Hybrid nanofluid showed complementary absorption property of component nanofluids. [ABSTRACT FROM AUTHOR]