Abstract Phase change materials (PCMs) offer a promising solution to address the challenges posed by intermittency and fluctuations in solar thermal utilization. However, for organic solid–liquid PCMs, issues such as leakage, low thermal conductivity, lack of efficient solar-thermal media, and flammability have constrained their broad applications. Herein, we present an innovative class of versatile composite phase change materials (CPCMs) developed through a facile and environmentally friendly synthesis approach, leveraging the inherent anisotropy and unidirectional porosity of wood aerogel (nanowood) to support polyethylene glycol (PEG). The wood modification process involves the incorporation of phytic acid (PA) and MXene hybrid structure through an evaporation-induced assembly method, which could impart non-leaking PEG filling while concurrently facilitating thermal conduction, light absorption, and flame-retardant. Consequently, the as-prepared wood-based CPCMs showcase enhanced thermal conductivity (0.82 W m −1 K −1 , about 4.6 times than PEG) as well as high latent heat of 135.5 kJ kg −1 (91.5% encapsulation) with thermal durability and stability throughout at least 200 heating and cooling cycles, featuring dramatic solar-thermal conversion efficiency up to 98.58%. In addition, with the synergistic effect of phytic acid and MXene, the flame-retardant performance of the CPCMs has been significantly enhanced, showing a self-extinguishing behavior. Moreover, the excellent electromagnetic shielding of 44.45 dB was endowed to the CPCMs, relieving contemporary health hazards associated with electromagnetic waves. Overall, we capitalize on the exquisite wood cell structure with unidirectional transport inherent in the development of multifunctional CPCMs, showcasing the operational principle through a proof-of-concept prototype system. 

Leakage Proof, Flame-Retardant, and Electromagnetic Shield Wood Morphology Genetic Composite Phase Change Materials for Solar Thermal Energy Harvesting

Intelligent cyclic fire warning sensor based on hybrid PBO nanofiber and montmorillonite nanocomposite papers decorated with phenyltriethoxysilane.

https://doi.org/10.1016/j.isci.2023.106718

Progress and challenges of emerging MXene based materials for thermoelectric applications

To achieve high capability mixed matrix membranes for CO2 separation, it is vital to construct the suitable transport channels to enhance the diffusivity-enabled selectivity as well as an excellent affinity toward target gas molecules to increase the solubility-based selectivity. Herein, composite membranes were constructed by integrating two-dimensional MXene nanosheets into a carboxylated nanocellulose matrix. The carboxyl-rich cellulose interacts with the surface groups of MXene, which contributes to a superior interface compatibility and repairs the non-selective voids. In particular, one-dimensional nanocellulose can intercalate into MXene laminates to regulate the interlayer spacing and suppress the re-stacking of MXenes. The resulting MC-3 membrane (with a MXene loading of 15.4 wt%) possesses the optimal separation performance, bearing a CO2 permeability of 156.7 Barrer with ideal selectivities of 47.8 for CO2/CH4 and 42.6 for CO2/N2. For the mixed gas separation, the MC-3 membrane exhibits separation factors of 48.5 and 55.3 for CO2/N2 and CO2/CH4, respectively. This excellent separation performance is resulted from the enhanced interfacial property, suitable MXene channel size and good CO2-solubility of nanocellulose. 

Integrating two-dimensional MXene fillers into nanocellulose for the fabrication of CO2 separation membranes

Multifunctional cellulose-based aerogel for intelligent fire fighting.

Bioinspired, stable adhesive Ti3C2Tx MXene-based coatings towards fire warning, smoke suppression and VOCs removal smart wood

The outbreak of COVID-19 has spread worldwide, causing great damage to the global economy. Raman spectroscopy is expected to become a rapid and accurate method for the detection of coronavirus. A classification method of coronavirus spike proteins by Raman spectroscopy based on deep learning was implemented. A Raman spectra dataset of the spike proteins of five coronaviruses (including MERS-CoV, SARS-CoV, SARS-CoV-2, HCoVHKU1, and HCoV-OC43) was generated to establish the neural network model for classification. Even for rapidly acquired spectra with a low signal-to-noise ratio, the average accuracy exceeded 97%. An interpretive analysis of the classification results of the neural network was performed, which indicated that the differences in spectral characteristics captured by the neural network were consistent with the experimental analysis. The interpretative analysis method provided a valuable reference for identifying complex Raman spectra using deep-learning techniques. Our approach exhibited the potential to be applied in clinical practice to identify COVID-19 and other coronaviruses, and it can also be applied to other identification problems such as the identification of viruses or chemical agents, as well as in industrial areas such as oil and gas exploration. 

/pdf/classification-of-coronavirus-spike-proteins-by-deep-2anj0svz.pdf

Classification of Coronavirus Spike Proteins by Deep-Learning-Based Raman Spectroscopy and its Interpretative Analysis

Abstract Photodetectors play a crucial role in converting light signals into electrical signals and have significant applications in various fields such as communications, imaging, and sensing. However, the fabrication of a photodetector is a complex process that involves precise control of surface preparation, lithography, and deposition techniques. Here the study demonstrates that GaN/Ga 2 O 3 heterojunctions can be fabricated utilizing laser processing to transform the surface of GaN into Ga 2 O 3 . The GaN/Ga 2 O 3 heterojunctions exhibit good reproducibility, uniformity, and ability to operate under zero bias, with a responsivity of 110.22 mA W −1 , a detection rate of 5.56 × 10 11 jones, and an external quantum efficiency of 42.34%. Moreover, an 8 × 8 photodetector array based on GaN/Ga 2 O 3 heterojunction is fabricated via laser writing and is demonstrated to have ultraviolet imaging capabilities. This report presents the pioneering fabrication of a photodetector array using laser writing. The findings offer a versatile and scalable approach for the production of large‐area heterojunction photodetector arrays. 

Laser Writing of GaN/Ga2O3 Heterojunction Photodetector Arrays

X-ray detectors have numerous applications in medical imaging, industrial inspection, and crystal structure analysis. Gallium oxide (Ga2O3) shows potential as a material for high-performance X-ray detectors due to its wide bandgap, relatively high mass attenuation coefficient, and resistance to radiation damage. In this study, we present Sn-doped Ga2O3 microwire detectors for solar-blind and X-ray detection. The developed detectors exhibit a switching ratio of 1.66 × 102 under X-ray irradiation and can operate stably from room temperature to 623 K, which is one of the highest reported operating temperatures for Ga2O3 X-ray detectors to date. These findings offer a promising new direction for the design of Ga2O3-based X-ray detectors.

/pdf/x-ray-detectors-based-on-ga2o3-microwires-2s645dn7.pdf

X-ray Detectors Based on Ga2O3 Microwires

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Bioinspired, stable adhesive Ti3C2Tx MXene-based coatings towards fire warning, smoke suppression and VOCs removal smart wood

Classification of Coronavirus Spike Proteins by Deep-Learning-Based Raman Spectroscopy and its Interpretative Analysis

Laser Writing of GaN/Ga2O3 Heterojunction Photodetector Arrays

X-ray Detectors Based on Ga2O3 Microwires