Although equipment-based gas sensor systems (e.g., high-performance liquid chromatography) have been widely applied for formaldehyde gas detection, pre-treatment and expensive instrumentation are required. To overcome these disadvantages, we developed a colorimetric sensor based on polymer-based core–shell nanoparticles (PCSNPs), which are inexpensive, stable, and exhibit enhanced selectivity. Spherical and uniform poly(styrene-co-maleic anhydride) (PSMA)/polyethyleneimine (PEI) core–shell nanoparticles were prepared and then impregnated with Methyl Red (MR), Bromocresol Purple (BCP), or 4-nitrophenol (4-NP) to construct colorimetric sensors for formaldehyde gas. The intrinsic properties of these dyes were maintained when introduced into the PCSNPs. In the presence of formaldehyde, the MR, BCP, and 4-NP colorimetric sensors changed to yellow, red, and gray, respectively. The colorimetric response was maximized at a PEI/PSMA ratio of four, likely owing to the high content of amine groups. Effective formaldehyde gas detection was achieved at a relative humidity of 30% using the MR colorimetric sensor, which exhibited a large color change (92%) in 1 min. Advantageously, this stable sensor allowed sensitive and rapid naked-eye detection of low formaldehyde concentrations (0.5 ppm). Hence, this approach is promising for real-time formaldehyde gas visualization and can also be adapted to other colorimetric gas sensor systems to improve sensitivity and simplicity.

An ideal dye‐based sensing array has essential design requirements, including facile preparation methodology, tolerance to water vapor, a broad range of color‐responsive changes, and a simple readout system. Here, a brief synthetic route is developed for ion‐pairing dyes exhibiting unusual chromatic changes across the entire visible spectrum. It requires only mixing and precipitation under mild conditions. The dyes are applied to a sensing array containing 12 sensing elements with different initial states. Owing to the numerous color variations of the dyes, the color map generated by the array is highly simple yet sufficiently accurate to distinguish among the different functional groups (such as amines, aldehydes, and carboxylic acids) as well as carbon chain lengths. Principle component analysis (PCA) demonstrates that volatile organic compounds (VOCs) can be well classified according to the color changes of the sensing array. The ion‐pairing dyes are embedded into 3D stacked nanofibers via electrospinning, and function as effective harmful‐gas (e.g., formaldehyde) sensors with sub‐ppm theoretical detection limits (0.15 ppm). Finally, the 3D stacked nanofibers can be employed in an optoelectronic filter system that automatically checks for formaldehyde in the surroundings and also confirms the effective removal of the detected formaldehyde by the gas filter cartridge.

To design metal nanoparticles (NPs) on a perovskite surface, the exsolution method has been extensively used for efficient catalytic reactions. However, there are still the challenges of finding a combination and optimization for the NPs’ control. Thus, we report in situ control of the exsolved Ni NPs from perovskite to apply as a catalyst for dry reforming of methane (DRM). The La_0.8Ce_0.1Ti_0.6Ni_0.4O₃ (LCTN) is designed by Ce doping to incorporate high amounts of Ni in the perovskite lattice and also facilitate the exsolution phenomenon. By control of the eluted Ni NPs through exsolution, the morphological properties of exsolved Ni NPs are observed to have a size range of 10~49 nm, while the reduction temperatures are changed. At the same time, the chemical structure of the eluted Ni NPs is also changed by an increased reduction temperature to a highly metallic Ni phase with an increased oxygen vacancy at the perovskite oxide surface. The optimized composite nanomaterial displays outstanding catalytic performance of 85.5% CH₄ conversion to produce H₂ with a value of 15.5 × 10¹¹ mol/s·gcat at 60.2% CO conversion, which shows the importance of the control of the exsolution mechanism for catalytic applications.