Here, a high-performance graded bandgap structure-based solar cell was designed and demonstrated, comprising a CsPbI 2 Br bottom cell and a CsPbI 3 QD top cell. Several optimizations were conducted to boost the device performance. As a result, the extended photoresponse, high carrier mobility, and well-matched energy levels afford a record power conversion efficiency of 14.45%, coupled with a high J SC of 15.25 mA/cm 2 . The result shows that optical and energy-band manipulation is an effective approach for improving the performance of inorganic perovskite solar cells.

The highest certified power conversion efficiency (PCE) of black phase based CsPbI3 perovskite solar cells has exceeded 18%, and become a hotspot in recent progress. However, the black phase of CsPbI3 rapidly transforms to yellow phase in ambient conditions due to its thermodynamic instability. Here, a Ruddlesden–Popper 2D structure is introduced into γ‐CsPbI3 film to stabilize the black phase via reducing dimensionality. It is found that a judicious amount of phenylethylammonium iodide can adjust the dimensionality of γ‐CsPbI3 film from 2D to quasi‐2D and 3D phase. Comprehensive consideration to obtain both the stability and high PCE, quasi‐2D (n = 40) γ‐CsPbI3 delivers a reproducible PCE of 13.65% with negligible hysteresis. By utilizing femtosecond transient absorption and time‐resolved PL decay, similar carrier kinetics in n = 40 and ∞ samples are observed, meaning an efficient charge extraction. More importantly, when the device is placed at 80 °C in N2 condition or in air with RH of 25–30%, its PCE keeps ≈88% and ≈89% of its initial PCE after 12 days, respectively. Such results are better than the 3D one (≈69% and ≈16%, respectively).

The replacement of a small amount of organic cations with bulkier organic spacer cations in the perovskite precursor solution to form a 2D perovskite passivation agent (2D‐PPA) in 3D perovskite thin films has recently become a promising strategy for developing perovskite solar cells (PSCs) with long‐term stability and high efficiency. However, the long, bulky organic cations often form a barrier, hindering charge transport. Herein, for the first time, 2D‐PPA engineering based on wide‐bandgap (≈1.68 eV) perovskites are reported. Pentafluorophenethylammonium (F5PEA+) is introduced to partially replace phenylethylammonium (PEA+) as the 2D‐PPA, forming a strong noncovalent interaction between the two bulky cations. The charge transport across and within the planes of pure 2D perovskites, based on mixed ammoniums, increases by a factor of five and three compared with that of mono‐cation 2D perovskites, respectively. The perovskite films based on mixed‐ammonium (F5PEA+‐PEA+) 2D‐PPA exhibit similar surface morphology and crystal structure, but longer carrier lifetime, lower exciton binding energy, less trap density and higher conductivity, in comparison with those using mono‐cation (PEA+) 2D‐PPA. The performance of PSCs based on mixed‐cation 2D‐PPA is enhanced from 19.58% to 21.10% along with improved stability, which is the highest performance for reported wide‐bandgap PSCs.

Abstract. The response of seasonal soil freeze depth to climate change has repercussions for the surface energy and water balance, ecosystems, the carbon cycle, and soil nutrient exchange. Despite its importance, the response of soil freeze depth to climate change is largely unknown. This study employs the Stefan solution and observations from 845 meteorological stations to investigate the response of variations in soil freeze depth to climate change across China. Observations include daily air temperatures, daily soil temperatures at various depths, mean monthly gridded air temperatures, and the normalized difference vegetation index. Results show that soil freeze depth decreased significantly at a rate of −0.18 ± 0.03 cm yr −1 , resulting in a net decrease of 8.05 ± 1.5 cm over 1967-2012 across China. On the regional scale, soil freeze depth decreases varied between 0.0 and 0.4 cm yr −1 in most parts of China during 1950-2009. By investigating potential climatic and environmental driving factors of soil freeze depth variability, we find that mean annual air temperature and ground surface temperature, air thawing index, ground surface thawing index, and vegetation growth are all negatively associated with soil freeze depth. Changes in snow depth are not correlated with soil freeze depth. Air and ground surface freezing indices are positively correlated with soil freeze depth. Comparing these potential driving factors of soil freeze depth, we find that freezing index and vegetation growth are more strongly correlated with soil freeze depth, while snow depth is not significant. We conclude that air temperature increases are responsible for the decrease in seasonal freeze depth. These results are important for understanding the soil freeze-thaw dynamics and the impacts of soil freeze depth on ecosystem and hydrological process.

Bacterial infection is one of the major causes of human death worldwide. To prevent bacterial infectious diseases from spreading, it is of critical importance to develop convenient, ultrasensitive, and cost-efficient methods for bacteria detection. Here, an electrochemical detector of a functional two-dimensional (2D) metal–organic framework (MOF) nanozyme was developed for the sensitive detection of pathogenic Staphylococcus aureus. A dual recognition strategy consisting of vancomycin and anti-S. aureus antibody was proposed to specifically anchor S. aureus. The 2D MOFs with excellent peroxidase-like activity can efficiently catalyze o-phenylenediamine to 2,2-diaminoazobenzene, which is an ideal electrochemical signal readout for monitoring the bacteria concentration. Under optimal conditions, the present bioassay provides a wide detection range of 10–7.5 × 107 colony-forming units CFU/mL with a detection limit of 6 CFU/mL, which is better than most of the previous reports. In addition, the established electrochemical sensor can selectively and accurately identify S. aureus in the presence of other bacteria. The present work provides a new pathway for sensitive and selective detection of S. aureus and presents a promising potential in the realm of clinical diagnosis.

Abstract. Snow depth is one of the key physical parameters for understanding land surface energy balance, soil thermal regime, water cycle, and assessing water resources from local community to regional industrial water supply. Previous studies by using in situ data are mostly site specific; data from satellite remote sensing may cover a large area or global scale, but uncertainties remain large. The primary objective of this study is to investigate spatial variability and temporal change in snow depth across the Eurasian continent. Data used include long-term ground-based measurements from 1814 stations. Spatially, long-term (1971Spatially, long-term ( -2000 mean annual snow depths of >20 cm were recorded in northeastern European Russia, the Yenisei River basin, Kamchatka Peninsula, and Sakhalin. Annual mean and maximum snow depth increased by 0.2 and 0.6 cm decade −1 from 1966 through 2012. Seasonally, monthly mean snow depth decreased in autumn and increased in winter and spring over the study period. Regionally, snow depth significantly increased in areas north of 50 • N. Compared with air temperature, snowfall had greater influence on snow depth during November through March across the former Soviet Union. This study provides a baseline for snow depth climatology and changes across the Eurasian continent, which would significantly help to better understanding climate system and climate changes on regional, hemispheric, or even global scales.

A novel method based on laser induced breakdown spectroscopy(LIBS) combined with random forest regression(RFR) was proposed to quantitative analysis of multielement of fourteen steel samples. Normalized LIBS spectrum of steel in which characteristic line(Si, Mn, Cr, Ni and Cu) identified by NIST database was used as analysis spectrum. Then, two parameters of RFR were optimized by out-of-bag (OOB) error estimation. The performance of calibration model was investigated by different input variables(the whole spectral bands(220-800nm) and spectra feature bands(220-400nm), respectively). In order to validate the predictive ability of multielement calibration model in steels, we compared RFR with partial least-squares(PLS) and support vector machines(SVM) to predict the concentrations of multielement in steels. And, the three quantitative techniques are evaluated in terms of prediction accuracy and root mean square error(RMSE).Random forest is shown to correctly model nonlinear effects dues to self-absorption in the plasma and to provide the best results. It confirms that LIBS technique coupled with RFR has a good potential for the in situ rapid determination of multielement in steels and even metallurgy field.A novel method based on laser induced breakdown spectroscopy(LIBS) and random forest regression(RFR) was proposed to quantitative analyze of multi-elements in fourteen steel samples. Normalized LIBS spectra of steel in which characteristic line(Si, Mn, Cr, Ni and Cu) identified by NIST 10 database were used as analysis spectra. Then, two parameters of RFR were optimized by out-of-bag (OOB) error estimation. The performance of calibration model was investigated by different input variables(the whole spectral bands(220-800nm) and spectra feature bands(220-400nm), respectively). In order to validate the predictive ability of multi-elements calibration RFR model in steels, we compared RFR with partial least-squares(PLS) and support vector machines(SVM) by means of prediction accuracy 15 and root mean square error(RMSE). Thus, RFR model can eliminate the influence of nonlinear factors dues to self-absorption in the plasma and provide a better predictive result. It confirms that LIBS technique coupled with RFR has a good potential for the in situ rapid determination of multi-elements in steels and even metallurgy field.

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