<p dir="ltr">Acoustic guitar soundboard material selection is based on selective evaluations that have been developed over centuries. These traditional practices are not conducive to the guitar industry we experience today because the supply of traditionally acceptable soundboard wood has decreased greatly. The purpose of this research was to develop an objective wood selection metric to determine the sound quality of an acoustic guitar’s soundboard. The metric would replace the subjective evaluations traditionally used to select materials for acoustic guitar soundboards.</p><p dir="ltr">The acoustic properties of sound radiation coefficient, material’s speed of sound, resonance and damping and the material properties of longitudinal and radial elastic modulus, density, and specific modulus were used in an attempt to construct a material selection metric. These variables were selected because the literature review revealed that these were the most critical variables in determining sound quality. The gaps in the literature were testing and analyzing samples that represented the true dimensions of an acoustic guitar soundboard blank and forming the metric. The literature revealed that the previous experimental studies did not have the appropriate test sample dimensions that correspond to the test samples evaluated by the subjective methods.</p><p dir="ltr">The methodology was carried out by using the objective testing counterparts to the subjective assessments found in the literature review. Instrumented hammer tap testing collected data to determine damping and resonance frequencies. A three-point static bending test collected data for longitudinal and radial elastic modulus. Mass and dimension measurements were recorded to calculate density. Calculations were done to compute the acoustic properties and specific modulus of the test samples. These variables were put into a table and underwent statistical analysis in the form of predictor correlation and logistical regression. The experimental variables were modeled against the subjective evaluation of an expert on the usability of the test samples.</p><p dir="ltr">Statistical analysis proved that the dataset did not show any significant separation between “good” or “bad” test samples or a significant correlation between the usability of the test sample and the variables in the dataset. The methodology did not produce an objective material selection metric to determine the sound quality of an acoustic guitar’s soundboard. Future research should include a wider range of measured frequencies and the collection of time domain data.</p>

<p dir="ltr">A three-dimensional numerical model was developed using the commercial CFD code CONVERGE to study the gas-dynamic interactions between the two chambers in a gasoline engine. The geometry and parameters of the engine used were based on a modified turbocharged GM four-cylinder 2.0 L GDI gasoline engine. Pre-chambers with nozzle diameters of 0.75 mm and 1.5 mm were used to investigate the effect of pre-chamber geometry on pre-chamber charging, combustion, and jet formation. The local developments of gas temperature and velocity were captured by adaptive mesh refinement, while the turbulence was resolved with the k-epsilon model of the Reynolds averaged Navier–Stokes (RANS) equations. The combustion process was modeled with the extended coherent flamelet model (ECFM). Data from engine experiments were compared with the computed main chamber pressures and heat release rates, and the results show good consistency with the model calculations. The scavenging and air–fuel equivalence ratio (λ) distribution of the pre-chambers improved with the larger nozzle, while the smaller nozzle generated jets with higher velocity, greater turbulence kinetic energy, and longer penetration length. Moreover, after the primary jet formation, secondary pre-chamber charging, combustion, and secondary jet formation were observed.</p><p dir="ltr">Two active PC injection strategies were designed to investigate the effect of injected hydrogen mass and PC mixture air-to-fuel equivalence ratio λ on PC combustion, jet formation, and main-chamber combustion. Stoichiometric or rich hydrogen/oxygen mixtures are actively injected into the pre-chamber to enhance the combustion processes in the pre-chamber and the main chamber. A three-dimensional numerical engine model is developed using the commercial CFD code CONVERGE. The engine geometry and parameters adopt a modified GM 4-cylinder 2.0L GDI gasoline engine. The local developments of gas temperature and velocity are resolved with the adaptive mesh refinement (AMR). The turbulence of the flow is computed with the k-epsilon model of the Reynolds averaged Navier–Stokes (RANS) equations. The turbulent combustion process is modeled with the extended coherent flamelet model (ECFM). Numerical results such as main chamber pressures and heat release rates are compared with experimental measurements, showing good consistency. Detailed analysis is performed to study the effect of the active pre-chamber injection with hydrogen on jet properties and turbulence chemistry interactions. An EGR limit of 36% was observed by injecting a stoichiometric hydrogen-oxygen mixture into the pre-chamber due to its high laminar flame speed and adiabatic flame temperature.</p>

The paradigm change in automotive power electronics towards wide bandgap semiconductor devices poses new challenges and requirements for the die-related packaging technologies as well as the assessment of reliability and lifetime. Here, the use of sintered silver for the die-related packaging in particular has proven promising. However, the empirical lifetime models for power modules developed over many years are not suitable any more. A holistic Physics-of-Failure approach can provide remedy as it allows for a significant reduction of testing time via finite element simulations. This approach requires a detailed understanding of the relevant failure mechanisms as well as an electrical, thermal and mechanical characterisation of the involved materials. A failure analysis of the complete power module revealed that the top-side sinter layer connecting the copper foil to the semiconductor die is prone to degradation. Therefore, the core of this work is the mechanical characterisation of porous sintered silver and, in particular, the primary and secondary creep behaviour. A newly developed creep model which - for the first time - takes load reversal for primary creep into account is implemented with a subroutine. This allows for lifetime simulations within a Physics-of-Failure framework resulting in a first lifetime model on module level for a complex automotive power module employing sintered silver.

<p dir="ltr">The goal of this thesis is to develop a method of evaluating long-term IAQ performance of an industrial facility and use machine-learning to model the relationship between critical air pollutants and the facility’s HVAC systems and processes. The facility under study for this thesis is an electroplating manufacturer. The air pollutants at this facility that were studied were particulate matter, total-volatile organic compounds, and carbon-dioxide. Upon sensor installation, seven “zones” were identified to isolate areas of the plant for measurement and analysis. A statistical review of the long-term data highlighted how this facility performed in terms of compliance. Their gaseous pollutants were well within regulation. Particulate matter, however, was found to be a pressing issue. PM10 was outside of compliance more than 15% of the time in five out of seven of the zones of study. Some zones were out of compliance up to 80% of the total collection period. The six pollutants that met these criteria were deemed critical and moved on to machine learning modeling. Our model of best fit for each pollutant used a gaussian process regression model, which fits best for non-linear rightly skewed datasets. The performance of each of our models was deemed significant. Every model had at least a regression coefficient of 0.935 and above for both validation and testing. The maximum average error was 12.64 ug.m^3, which is less than 10% of the average PM10 concentration. Through our modeling, we were able to study how HVAC and production played a role in particulate matter presence for each zone. Exhaust systems of the west side of the plant were found to be insufficient at removing particulates from their facility. Overall, the methods developed in this thesis project were able to meet the goal of analyzing IAQ compliance, modeling critical pollutants using machine learning, and identifying a relationship between these pollutants and an industrial facility’s HVAC and production systems.</p>

<p dir="ltr">There is a growing trend towards the development of parsimonious surrogate models for studying physical phenomena. While they typically offer less accuracy, these models bypass the computational costs of numerical methods, usually by multiple orders of magnitude, allowing statistical applications such as sensitivity analysis, stochastic treatments, parametric problems, and uncertainty quantification. Researchers have explored generalized surrogate frameworks leveraging Gaussian processes, various basis function expansions, support vector machines, and neural networks. Dynamical fields, represented through time-dependent partial differential equation, pose a particular hardship for existing frameworks due to their high dimensional representation, and possibly multi-scale solutions.</p><p dir="ltr">In this work, we present a novel architecture for solving time-dependent partial differential equations using co-ordinate neural networks and time-marching updates through hyper-networks. We show that it provides a temporally meshed and spatially mesh-free solution which are causally coherent as justified through a theoretical treatment of Lie groups. We showcase results on some benchmark problems in computational physics while discussing their performance against similar physics-informed approaches like physics-informed DeepOnets and Physics informed neural networks.</p>

<p dir="ltr">This thesis explores novel methods of steel reheating, simulating hydrogen as a cleaner fuel in the combustion furnace and magnetic induction heating as a viable alternative, by utilizing advanced numerical simulations, including Computational Fluid Dynamics (CFD) and Finite Element Analysis (FEA), to assess their performance and feasibility.</p><p dir="ltr">Hydrogen, known for its potential to significantly reduce carbon dioxide emissions, is examined as a substitute for natural gas. Simulations revealed that hydrogen combustion results in higher flame temperatures and heat fluxes. While the CFD model achieved a high level of accuracy, with a maximum temperature error of 3% and an average deviation of 7% from real-world data, hydrogen fuel caused an increase in heat flux by up to 12% and higher slab surface temperatures. These changes led to steeper thermal gradients and increased stress, with peak stress levels reaching 90% of material limit. This simulation approach provides valuable data on the performance of different furnace fuels, helping to identify optimal fuel blends and configurations that minimize the risk of material failure while enhancing furnace efficiency.</p><p dir="ltr">The impact of scale formation on steel surfaces during reheating was also investigated. A mathematical model based on linear-parabolic equations was integrated into CFD simulations to predict scale growth. This model was validated against experimental data, showing an average error of 6%. The presence of scale led to a reduction in core temperature by up to 31 K and a 7.6% decrease in heat flux, which negatively affected heating efficiency. Scale formation also caused a significant drop in thermal conductivity, impacting heat transfer and slab uniformity. Pre-heating zone contributed minimally to overall scale formation despite its extended duration whereas a majority of scale growth was observed in the heating zone. Applications of this model include improving reheat furnace model efficiency and optimizing furnace operation to minimize scale.</p><p dir="ltr">Magnetic induction heating was also explored as an alternative to combustion-based reheating, assessing its potential benefits and challenges. The simulation results, validated with an average error of approximately 7% compared to literature data. showed uniform temperature distribution, and reduced stress levels with optimal power settings around 80 kW. A 3D transient simulation modeled an adaptive power cycle to minimize thermal stress highlighting the effectiveness of adaptive soaking strategies over continuous soaking in managing thermal stress, improving heating efficiency and material integrity.</p>

Polyethylene Terephthalate (PET) is one of the most important polymers in use today for packaging due to its outstanding properties. The usage of PET has grown at the highest rate compared with other plastic packaging over the last 20 years, and it is anticipated that the increase in global demand will be around 6% in the 2010 ¿ 2015 period. The rheological behaviour, thermal properties, tensile modulus, permeability properties and degradation phenomena of PET/clay nanocomposites have been investigated in this project. An overall, important finding is that incorporation of nanoclays in PET gives rise to improvements in several key process and product parameters together ¿ processability/ reduced process energy, thermal properties, barrier properties and stiffness. The PET pellets have been compounded with carefully selected nanoclays (Somasif MAE, Somasif MTE and Cloisite 25A) via twin screw extrusion to produce PET/clay nanocomposites at various weight fractions of nanoclay (1, 3, 5, 20 wt.%). The nanoclays vary in the aspect ratio of the platelets, surfactant and/or gallery spacing so different effect are to be expected. The materials were carefully prepared prior to processing in terms of sufficient drying and re-crystallisation of the amorphous pellets as well as the use of dual motor feeders for feeding the materials to the extruder. The rheological properties of PET melts have been found to be enhanced by decreasing the viscosity of the PET i.e. increasing the ¿flowability¿ of the PET melt during the injection or/and extrusion processes. The apparent shear viscosity of PETNCs is show to be significantly lower than un-filled PET at high shear rates. The viscosity exhibits shear thinning behaviour which can be explained by two mechanisms which can occur simultaneously. The first mechanism proposed is that some polymer has entangled and few oriented molecular chain at rest and when applying high shear rates, the level of entanglements is reduced and the molecular chains tend to orient with the flow direction. The other mechanism is that the nanoparticles align with the flow direction at high shear rates. At low shear rate, the magnitudes of the shear viscosity are dependent on the nanoclay concentrations and processing shear rate. Increasing nanoclay concentration leads to increases in shear viscosity. The viscosity was observed to deviate from Newtonian behaviour and exhibited shear thinning at a 3 wt.% concentration. It is possible that the formation of aggregates of clay is responsible for an increase in shear viscosity. Reducing the shear viscosity has positive benefits for downstream manufacturers by reducing power consumption. It was observed that all ii three nanoclays used in this project act as nucleation agents for crystallisation by increasing the crystallisation temperature from the melt and decreasing the crystallisation temperature from the solid and increasing the crystallisation rate, while retaining the melt temperature and glass transition temperatures without significant change. This enhancement in the thermal properties leads to a decrease in the required cycle time for manufacturing processes thus potentially reducing operational costs and increasing production output. It was observed that the nanoclay significantly enhanced the barrier properties of the PET film by up to 50% this potentially allows new PET packaging applications for longer shelf lives or high gas pressures. PET final products require high stiffness whether for carbonated soft drinks or rough handling during distribution. The PET/Somasif nanocomposites exhibit an increase in the tensile modulus of PET nanocomposite films by up to 125% which can be attributed to many reasons including the good dispersion of these clays within the PET matrix as shown by TEM images as well as the good compatibility between the PET chains and the Somasif clays. The tensile test results for the PET/clay nanocomposites micro-moulded samples shows that the injection speed is crucial factor affecting the mechanical properties of polymer injection moulded products.

[ES] Los problemas de selección de talla y ajuste en la compra online de ropa son la causa de aproximadamente el 70% de las devoluciones. Esto se debe principalmente a la falta de estandarización del tallaje y al funcionamiento deficiente de los métodos de recomendación de talla. Actualmente, el comercio online de ropa tiene mucho potencial, sin embargo, las elevadas tasas de devolución, suponen costes relevantes en logística y gestión de stocks. En el estado del arte de esta tesis se refleja la complejidad del problema del ajuste de ropa, en el que intervienen múltiples factores tanto objetivos (tipo de tejido, patronaje, número de tallas, moda, morfotipo del usuario, medidas corporales, etc.), como subjetivos (percepción de ajuste y preferencias del usuario). Siendo el ajuste de las prendas uno de los problemas de mayor relevancia en el sector de la confección es de crucial interés avanzar en la generación de un conocimiento que permita relacionar con mayor precisión las dimensiones corporales, el ajuste de las prendas y el tallaje. El objetivo general de esta tesis consiste en establecer los fundamentos para desarrollar un sistema de recomendación del ajuste y talla de prendas de ropa a partir de medidas antropométricas del usuario y valoraciones de ajuste planteando un desarrollo metodológico que sirve de punto de partida para posteriormente escalar el proceso a cualquier tipo de prenda, estilo y sistema de tallaje de ropa. La aproximación propuesta consiste en la predicción del ajuste por zonas de la prenda a partir de medidas antropométricas del usuario y pruebas de ajuste previas utilizando el método estadístico de regresión logística multinomial. A partir de esta predicción de ajuste por zonas, y aplicando de nuevo modelos de regresión logística multinomial, se obtiene la probabilidad de ajuste de la serie de tallas de la prenda analizada. En primer lugar, se ha determinado la fiabilidad de las medidas antropométricas obtenidas a partir de escaneados 3D del cuerpo. Para desarrollar los modelos de predicción, se ha puesto a punto un método de caracterización del ajuste de ropa mediante valoración subjetiva de usurarios y expertos. Además, se han definido los conjuntos de medidas antropométricas relacionadas con el ajuste de la prenda en cada zona. El proceso de entrenamiento de los modelos de predicción de ajuste ha permitido determinar cuáles son las medidas antropométricas más relevantes para el ajuste de cada tipo de prenda, así como las zonas de ajuste que influyen en la selección de la talla. En la fase de validación, se ha demostrado que, con un porcentaje de acierto entre el 80-100%, los modelos de predicción de talla basados en probabilidades de ajuste obtenidas mediante regresión logística multinomial en zonas relevantes de la prenda, ofrecen mayor fiabilidad que los métodos actuales que solo consideran una variable corporal y sus intervalos. Finalmente, se ha propuesto un método para extrapolar los modelos individuales de predicción de talla a toda población objetivo, estimar la cuota de mercado potencial y optimizar la distribución de tallas de cada prenda. / [CA] Els problemes de selecció de talla i ajust en les compres de roba en la xarxa representen aproximadament el 70% de les devolucions. Això es degut principalment a la manca d'estandardització en les talles i al funcionament deficient dels mètodes de recomanació de talles. Actualment, el comerç de roba en la xarxa té molt potencial, no obstant això, les altes taxes de devolució comporten costos rellevants en logística i gestió d'estocs. L'estat de l'art en aquesta tesi reflecteix la complexitat del problema de l'ajust de la roba, que implica múltiples factors, tant objectius (tipus de teixit, patró, nombre de talles, tendències de moda, tipus de cos de l'usuari, mesures corporals, etc.) com subjectius (percepció de l'ajust per part de l'usuari i preferències). Ja que l'ajust de les peces de vestir és un dels problemes més importants en la indústria de la moda, és crucial avançar en la generació de coneixement que permeti establir una relació més precisa entre les dimensions del cos, l'ajust de la roba i les talles. L'objectiu general d'aquesta tesi és establir els fonaments per al desenvolupament d'un sistema de recomanació de l'ajust i la talla de peces de roba basat en les mesures antropomètriques de l'usuari. Això implica un desenvolupament metodològic que serveix com a punt de partida per a posteriorment escalar el procés a qualsevol tipus de peça de roba, estil i sistema de mides. L'aproximació proposada consistix en predir l'ajust per zones de la peça de roba basat en les mesures antropomètriques de l'usuari i proves prèvies d'ajust mitjançant el mètode estadístic de la regressió logística multinomial. A partir d'aquesta predicció d'ajust per zones, i aplicant novament models de regressió logística multinomial, s'obté la probabilitat d'ajust per a la gamma de talles de la peça de roba analitzada. S'ha determinat la fiabilitat de les mesures antropomètriques obtingudes a partir d'escaneigs 3D del cos. Per desenvolupar els models de predicció, s'ha posat a punt un mètode per caracteritzar l'ajust de la roba mitjançant avaluacions subjectives dels usuaris i experts. A més, s'han definit conjunts de mesures antropomètriques relacionades amb l'ajust de la peça a cada zona. El procés de formació dels models de predicció de l'ajust ha permès determinar les mesures antropomètriques més rellevants per a l'ajust de cada tipus de peça, així com les zones d'ajust que influeixen en la selecció de la talla. En la fase de validació, s'ha demostrat que, amb un percentatge d'encert entre el 80-100%, els models de predicció de talla basats en les probabilitats d'ajust obtingudes mitjançant la regressió logística multinomial en zones rellevants de la peça de roba ofereixen una major fiabilitat que els mètodes actuals que només consideren una variable corporal i els seus intervals. Finalment, s'ha proposat un mètode per extrapol·lar els models individuals de predicció de talla a tota la població objectiu, estimar la quota de mercat potencial i optimitzar la distribució de talles per a cada peça. / [EN] The problems of size selection and fit in online clothing purchases account for approximately 70% of returns. This is primarily due to the lack of standardization in sizing and the inefficient performance of current size recommendation methods. Currently, online clothing retail has a lot of potential; however, the high product return rates result in significant costs in logistics and stock management. The state of the art in this thesis reflects the complexity of the clothing fit problem, which involves multiple factors, both objective (fabric type, pattern, number of sizes, fashion trends, user body type, body measurements, etc.) and subjective (user's perception of fit and preferences). Since garment fit is one of the most significant issues in the fashion industry, it is crucial to advance in generating knowledge that allows for a more precise relationship between body dimensions, garment fit, and sizing. The general objective of this thesis is to establish the foundations for developing a recommendation system for clothing fit and size based on user anthropometric measurements and fit evaluations. This involves a methodological development that serves as a starting point for subsequently scaling the process to any type of garment, style, and sizing system. The proposed approach consists of predicting the fit by garment zones based on user anthropometric measurements and previous fit trials using the statistical method of multinomial logistic regression. From this prediction of fit by zones, and by once again applying multinomial logistic regression models, the probability of fit for the range of sizes of the analyzed garment is obtained. The reliability of anthropometric measurements obtained from 3D body scans has been determined. To develop the prediction models, a method for characterizing garment fit through subjective assessments by users and experts has been refined. In addition, sets of anthropometric measurements related to garment fit in each zone have been defined. The training process of the fit prediction models has enabled determining the most relevant anthropometric measurements for the fit of each type of garment, as well as the fit zones that influence size selection. In the validation phase, it has been demonstrated that, with an accuracy rate between 80-100%, size prediction models based on fit probabilities obtained through multinomial logistic regression in relevant garment zones offer greater reliability than current methods that only consider a single body variable and its intervals. Finally, a method has been proposed to extrapolate individual size prediction models to the entire target population, estimate the potential market share, and optimize the distribution of sizes for each garment. / Alemany Mut, MS. (2023). Desarrollo de modelos estadísticos de predicción del ajuste y talla de prendas de ropa a partir de la percepción y características antropométricas del usuario [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/202617

<p dir="ltr">Granular flow of non-spherical particles is common in nature and industrial processes. To understand the behavior of granular systems of these non-spherical particles, computational methods are employed to simulate these systems. However, current state-of-the-art simulation methods (TFM and DEM)have two primary drawbacks: (1) high computational cost restricting this method to small-scale systems (DEM) and (2) the use of empirical correlations that cannot be reliably extrapolated to different systems (TFM). Also, due to the statistical limitation and lack of physics-based continuum description, making progress in non-spherical particle flow dynamics with the study of its higher-order moments and transport coefficients is virtually unfeasible. To address these challenges, a DSMC model is developed to simulate a granular gas of spherocylinders with varying aspect ratios. In this work, a 3D classical trajectory calculation (CTC) code is developed to generate pairwise collision data sets. In addition, the Gaussian mixture model, an unsupervised machine learning technique, is used to construct the complex probability distributions required by the DSMC model. Subsequently, the model is implemented and validated against exact solutions derived from equivalent DEM simulations. The model shows high accuracy on both the macroscopic and microscopic scales and is more than 50 times faster than the DEM. The distribution functions of energies and velocities are extracted over time. Following the methodology presented, this approach can be easily adjusted to accommodate different particle shapes.</p>

<p dir="ltr">In recent years, the world has witnessed a growing trend of record high temperatures, heat waves, and extreme weather events due to climate change. Thus, there is an urgent need to develop technologies that enhance quality of life while mitigating further contributions to climate change. Radiative cooling, a passive cooling technique, offers a promising solution to this challenge. Nature serves as a vast, largely unexplored source of inspiration, with various biological systems utilizing radiative cooling to thrive in extreme environments. This work looks at what can be learned from nature to better develop radiative cooling technologies.</p><p dir="ltr">While nanoparticle-based coatings and biologically-inspired nanocellulose-based structures have shown promise in radiative cooling, each has its limitations. Nanocellulose-based structures exhibit high mechanical strength but lower solar reflectance due to UV absorption. On the other hand, nanoparticle-based coatings require a high volume of nanoparticles, resulting in brittleness. This work introduces a dual-layer system comprising a cellulose-based substrate and a thin nanoparticle-based radiative cooling paint, maximizing both radiative cooling potential and mechanical strength. The relationship is studied between thickness and reflectance of the top coating layer with a consistent thickness of the bottom layer. The saturation point is identified and used to determine the optimal thickness for the top-layer. With the use of cotton paper painted with a 125 microns BaSO⁴-based layer, the cooling performance is enhanced to 149.6 W/m² achieved by the improved total solar reflectance from 80% to 93%.</p><p dir="ltr">Looking at another source of biological inspiration, radiative cooling potential of the white shell of the <a href="" target="_blank"><i>Sphincterochila</i></a><i> zonata</i> desert snail is investigated through experimental techniques, revealing a remarkable 90.8% total solar reflectance and 0.88 sky window emissivity, which is achieved through nanoscale features and layered platelet-like morphologies. This is a record high for a biological system. The porosity, nanostructure, and material composition are analyzed, and compared to relative biological systems in other white shells, including those living in the same Negev desert and highly contrasting ocean dwellers. Structural analysis demonstrates layered platelet-like morphologies that optimize for light scattering in solar wavelengths. We investigate the shell's porosity, nanostructure, and material composition through comparison with other species’ shells in the Negev desert and marine environments. Through this, we gain inspiration from <i>Sphincterochila zonata</i> to develop our own radiative cooling technologies.</p><p dir="ltr">In weight-sensitive applications, thin and lightweight radiative cooling paints are crucial, but achieving high solar reflectance remains a challenge. Using inspiration of the layered structure seen in desert snails, this research introduces ultrawhite <a href="" target="_blank">hBN</a>-Acrylic paints that achieve a remarkable solar reflectance of 97.9% with only 150 µm thickness and 0.029 g/cm² weight. The unique properties of hexagonal boron nitride (hBN), including a high refractive index and nanoplatelet morphology, enable a combination of Mie and Rayleigh scattering, while a 44.3% porosity enhances refractive index contrast. Field tests demonstrate that hBN-Acrylic paints provide full daytime cooling under direct sunlight, reducing temperatures by 5-6℃ below ambient.</p><p dir="ltr">Furthermore, biodegradable chitosan-hBN films are introduced as a promising advancement in sustainable cooling technology. These films, composed of up to 60% hBN nanoplatelets within a chitosan matrix, offer flexibility, mechanical robustness, and significant cooling potential. Preliminary results show that these films achieve high solar reflectance and maintain structural integrity, with further potential for optimization through nanoplatelet alignment techniques like hot pressing. By integrating bio-inspired and synthetic approaches, this work contributes to the broader goal of developing sustainable, high-performance materials for passive cooling.</p>