The development of materials toward ppb-level nitric oxide (NO) sensing at room temperature remains in high demand for the monitoring of respiratory inflammatory diseases. In order to find an iron-containing molecule without steric hindrance to combine with graphene for room temperature NO gas sensing, here a supramolecular assembly of ferrocene (Fc) and reduced graphene oxide (rGO) was designed and prepared for NO sensing. The assembly of Fc/rGO was characterized using FT-IR, TEM, and XPS measurements. The Fc/rGO-based sensors exhibited superior NO sensing properties at room temperature including high response (R_a/R_g = 1.73, 1 ppm), high selectivity against other exhaled gases, reliable repeatability and stability (less than 4 % decrease after 40 days). A practical limit of detection (LOD) of 200 ppb was achieved. The theoretical simulation demonstrates that ferrocene is assembled via π-π interaction with rGO in edge-to-face configuration which provides relatively lower energy than face-to-face configuration does for the whole assembly. It was first verified that the enhanced adsorption capacity and the charge transfer between NO and Fc/rGO would result in improvement of the assembly's sensitivity toward NO after ferrocene was assembled with graphene. This work provides a fresh approach of anchoring iron on graphene for gas sensing via supramolecular methods.

The building sector plays a pivotal role in climate change mitigation. By regulating the demand for services and products from supply sectors, building sector can contribute to decarbonization. To assess the decarbonization and cost-saving potential of demand-side solutions for China’s residential building sector, this study develops a demand-side solution framework and an end-use technology model. The model covers the building sector and major supply sectors, considering the heterogeneous impacts of demand-side solution measures on different supply sectors. Here we show that the most optimistic cost-effective demand-side solution can reduce cumulative CO₂ emissions by 47% (42.21 Gt CO₂-eq), while achieving a 16% saving in the net present value of costs over the period 2020−2060. Additionally, results indicate that the demand-side solution enable China’s rural residential buildings to achieve carbon neutrality without carbon dioxide remove options, while simultaneously mitigating uncertainties in reaching carbon neutrality targets.

Several global or regional databases for various types of soils have been developed due to their importance in engineering design and analysis. However, a database is not yet available for collapsible loess in which severe geohazards often occur. In this study, a comprehensive loess database with twelve soil parameters is compiled by collecting results of field and laboratory tests on collapsible loess from the city of Xi’an, China. Basic statistics, marginal probability distribution functions (PDFs), and a correlation matrix for loess parameters are estimated from the database. To the best of the authors’ knowledge, this is the first collapsible loess database at a municipal level. In addition, existing databases often lack sufficiently complete multivariate measurement data for a proper estimation of statistical correlations among multiple soil properties. In this study, this incomplete multivariate measurement data problem is tackled by Bayesian methods (i.e., Bayesian Gaussian mixture model and Bayesian compressive sampling (BCS) with Karhunen–Loève (KL) expansion, BCS-KL), which are illustrated and validated using the incomplete and complete subsets of the loess database, respectively. Both the Bayesian Gaussian mixture model and BCS-KL are non-parametric, and they offer a flexible way of modeling marginal PDFs and a correlation matrix from incomplete measurements in a realistic manner.

Implementing parts per billion-level nitric oxide (NO) sensing at room temperature (RT) is still in extreme demand for monitoring inflammatory respiratory diseases. Herein, we have prepared a kind of core-shell structural Hemin-based nanospheres (Abbr.: Hemin-nanospheres, defined as HNSs) with the core of amorphous Hemin and the shell of acetone-derived carbonized polymer, whose core-shell structure was verified by XPS with argon-ion etching. Then, the HNS-assembled reduced graphene oxide composite (defined as HNS-rGO) was prepared for RT NO sensing. The acetone-derived carbonized polymer shell not only assists the formation of amorphous Hemin core by disrupting their crystallization to release more Fe-N4 active sites, but provides protection to the core. Owing to the unique core-shell structure, the obtained HNS-rGO based sensor exhibited superior RT gas sensing properties toward NO, including a relatively higher response (Ra/Rg = 5.8, 20 ppm), a lower practical limit of detection (100 ppb), relatively reliable repeatability (over 6 cycles), excellent selectivity, and much higher long-term stability (less than a 5% decrease over 120 days). The sensing mechanism has also been proposed based on charge transfer theory. The superior gas sensing properties of HNS-rGO are ascribed to the more Fe-N4 active sites available under the amorphous state of the Hemin core and to the physical protection by the shell of acetone-derived carbonized polymer. This work presents a facile strategy of constructing a high-performance carbon-based core-shell nanostructure for gas sensing.