Thermochromic Smart Windows Assisted by Photothermal Nanomaterials

Smart Window with Reversible and Instantaneous Photoluminescence based on Microsphere Structure

A smart window that dynamically regulates light transmittance is crucial for modern life end-users and promising for on-demand optical devices. The advent of three-dimensional (3D) photonic crystal microspheres has enriched the functions of a smart window. However, the smart window formed by polymer microspheres encounters poor mechanical strength and microstructural defects. Herein, to solve this limitation, we report the microsphere-based smart window composed of tightly packed cross-linked polymer microspheres (as a precursor) containing organic photochromic dyes, followed by compression under a high elastic state. When excited under an ultraviolet supply, our smart window showed a rapid and reversible fluorescent photoluminescence without fatigue (50 cycles). Moreover, the bulk devices with a microsphere cross-linked network structure enable excellent mechanical strength (hardness reached 0.158 GPa) and visible-light transparency. Interestingly, a QR code can be recognized under visible light exposure but not under ultraviolet light exposure because of photoluminescence of the smart window. Our method generally provided a paradigm for various amorphous polymers, which can be regarded as a simple and effective approach to build a versatile strategy to introduce an ideal marketplace with economic and community benefits.

Thermochromic Polymer Nanocomposites for the Heat Detection System: Recent Progress on Properties, Applications, and Challenges

Reversible thermochromic polymers have emerged as compelling candidates in recent years, captivating attention for their application in heat detection systems. This comprehensive review navigates through the multifaceted landscape, intricately exploring both the virtues and hurdles inherent in their integration within these systems. Their innate capacity to change colour in response to temperature fluctuations renders reversible thermochromic nanocomposites promising assets for heat detection technologies. However, despite their inherent potential, certain barriers hinder their widespread adoption. Factors such as a restricted colour spectrum, reliance on external triggers, and cost considerations have restrained their pervasive use. For instance, these polymer-based materials exhibit utility in the domain of building insulation, where their colour-changing ability serves as a beacon, flagging areas of heat loss or inadequate insulation, thus alerting building managers and homeowners to potential energy inefficiencies. Nevertheless, the limited range of discernible colours may impede precise temperature differentiation. Additionally, dependency on external stimuli, such as electricity or UV light, can complicate implementation and inflate costs. Realising the full potential of these polymer-based materials in heat detection systems necessitates addressing these challenges head-on. Continuous research endeavours aimed at augmenting colour diversity and diminishing reliance on external stimuli offer promising avenues to enhance their efficacy. Hence, this review aims to delve into the intricate nuances surrounding reversible thermochromic nanocomposites, highlighting their transformative potential in heat detection and sensing. By exploring their mechanisms, properties, and current applications, this manuscript endeavours to shed light on their significance, providing insights crucial for further research and potential applications.

Immobilization of rare-earth doped aluminate nanoparticles encapsulated with silica into polylactic acid-based color-tunable smart plastic window

An inorganic/organic nanocomposite was used to develop an afterglow and color-tunable smart window. A combination of polylactic acid (PLA) plastic waste as an environmentally-friendly hosting agent, and lanthanide-activated strontium aluminum oxide nanoparticles (SAON) encapsulated with silica nanoparticles (SAON@Silica) as a photoluminescent efficient agent resulted in a smart organic/inorganic nanocomposite. In order to prepare SAON-encapsulated silica nanoparticles (SAON@Silica), the SAON nanoparticles were coated with silica using the heterogeneous precipitation method. By using transmission electron microscopy (TEM), SAON showed a diameter range of 5-12 nm, while the SAON@Silica nanoparticles showed a diameter range of 50-100 nm. In order to ensure the development of a colorless plastic film, a homogeneous dispersion of the phosphorescent Phosphor@Silica nanoparticles throughout the plastic bulk was confirmed. CIE Lab coordinates and luminescence spectra were used to study the color shift characteristics. Under visible light conditions, the plastic films were transparent. The photoluminescent films emitted green light at 525 nm when excited at 375 nm. The hydrophobicity and ultraviolet protection were enhanced without altering the fundamental physico-mechanical performance of the plastic sheet. The current color-tunable plastic can be used in many potential applications, such as warning signs, anti-counterfeiting barcodes, smart windows, and protective apparel.

Recent Developments in Photoluminescent, Photothermal and Photocatalytic Nanomaterials

Photoactive nanomaterials exhibit myriad customized properties, including a photon converting ability, specific surface area, physicochemical stability, and chemical reactivity, making them appealing for a wide range of practical applications [...].

Machine Learning-Assisted Synthesis of Two-Dimensional Materials

Two-dimensional (2D) materials have intriguing physical and chemical properties, which exhibit promising applications in the fields of electronics, optoelectronics, as well as energy storage. However, the controllable synthesis of 2D materials is highly desirable but remains challenging. Machine learning (ML) facilitates the development of insights and discoveries from a large amount of data in a short time for the materials synthesis, which can significantly reduce the computational costs and shorten the development cycles. Based on this, taking the 2D material MoS₂ as an example, the parameters of successfully synthesized materials by chemical vapor deposition (CVD) were explored through four ML algorithms: XGBoost, Support Vector Machine (SVM), Naïve Bayes (NB), and Multilayer Perceptron (MLP). Recall, specificity, accuracy, and other metrics were used to assess the performance of these four models. By comparison, XGBoost was the best performing model among all the models, with an average prediction accuracy of over 88% and a high area under the receiver operating characteristic (AUROC) reaching 0.91. And these findings showed that the reaction temperature (T) had a crucial influence on the growth of MoS₂. Furthermore, the importance of the features in the growth mechanism of MoS₂ was optimized, such as the reaction temperature (T), Ar gas flow rate (R_f), reaction time (t), and so on. The results demonstrated that ML assisted materials preparation can significantly minimize the time spent on exploration and trial-and-error, which provided perspectives in the preparation of 2D materials.