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Photoelectrochemical reaction is emerging as a powerful approach for biomass conversion. However, it has been rarely explored for glucose conversion into value-added chemicals. Here we develop a photoelectrochemical approach for selective oxidation of glucose to high value-added glucaric acid by using single-atom Pt anchored on defective TiO 2 nanorod arrays as photoanode. The defective structure induced by the oxygen vacancies can modulate the charge carrier dynamics and band structure, simultaneously. With optimized oxygen vacancies, the defective TiO 2 photoanode shows greatly improved charge separation and significantly enhanced selectivity and yield of C 6 products. By decorating single-atom Pt on the defective TiO 2 photoanode, selective oxidation of glucose to glucaric acid can be achieved. In this work, defective TiO 2 with single-atom Pt achieves a photocurrent density of 1.91 mA cm −2 for glucose oxidation at 0.6 V versus reversible hydrogen electrode, leading to an 84.3 % yield of glucaric acid under simulated sunlight irradiation. Photoelectrochemical oxidation provides a promising strategy for glucaric acid production. Here, selective oxidation of glucose to glucaric acid is realized on the photoanode of defective TiO2 decorated with single-atom Pt via a photoelectrochemical strategy.

The COVID-19 pandemic is impacting human activities, and in turn energy use and carbon dioxide (CO 2 ) emissions. Here we present daily estimates of country-level CO 2 emissions for different sectors based on near-real-time activity data. The key result is an abrupt 8.8% decrease in global CO 2 emissions (−1551 Mt CO 2 ) in the first half of 2020 compared to the same period in 2019. The magnitude of this decrease is larger than during previous economic downturns or World War II. The timing of emissions decreases corresponds to lockdown measures in each country. By July 1st, the pandemic’s effects on global emissions diminished as lockdown restrictions relaxed and some economic activities restarted, especially in China and several European countries, but substantial differences persist between countries, with continuing emission declines in the U.S. where coronavirus cases are still increasing substantially. The COVID-19 pandemic has stopped many human activities, which has had significant impact on emissions of greenhouse gases. Here, the authors present daily estimates of country-level CO 2 emissions for different economic sectors and show that there has been a 8.8% decrease in global CO2 emissions in the first half of 2020.

The annual increase in total methane emissions in China has decelerated, largely due to the continued implementation of effective emission reduction policies. Tracking methane emission hotspots and characterizing their emission profiles are critical for driving further reductions. However, the specific role of strong methane emissions in the annual trend changes of China’s methane emissions, as well as seasonal emission profiles, has not been clarified. In this study, we use TROPOMI satellite observations to estimate methane emissions in China for 2019-2023 and comprehensively analyze the driving mechanisms of the trend changes and seasonal variations of methane emissions in China by combining the emissions structural properties with the emission regions’ signatures. Our results reveal that methane emission trends in China over the five years, based on top-down estimations, have significantly moderated. Emission intensity has declined, stability has improved, and the geographic distribution exhibits a pronounced north-heavy and south-light pattern. While the proportion of unstable strong emissions has remained small and consistent over the years, their contribution to total emissions remains significant. Moreover, the influence of stable strong emissions and unstable weak emissions on overall methane levels has grown over time. Unstable strong emissions, primarily linked to energy-related activities, play a pivotal role in shaping seasonal methane emission trends in China. These findings highlight the complex interplay of emission sources and provide valuable insights for refining methane reduction strategies.

Hyperspectral remote sensing images (HSIs) have a higher spectral resolution compared to multispectral remote sensing images, providing the possibility for more reasonable and effective analysis and processing of spectral data. However, rich spectral information usually comes at the expense of low spatial resolution owing to the physical limitations of sensors, which brings difficulties for identifying and analyzing targets in HSIs. In the super-resolution (SR) field, many methods have been focusing on the restoration of the spatial information while ignoring the spectral aspect. To better restore the spectral information in the HSI SR field, a novel super-resolution (SR) method was proposed in this study. Firstly, we innovatively used three-dimensional (3D) convolution based on SRGAN (Super-Resolution Generative Adversarial Network) structure to not only exploit the spatial features but also preserve spectral properties in the process of SR. Moreover, we used the attention mechanism to deal with the multiply features from the 3D convolution layers, and we enhanced the output of our model by improving the content of the generator’s loss function. The experimental results indicate that the 3DASRGAN (3D Attention-based Super-Resolution Generative Adversarial Network) is both visually quantitatively better than the comparison methods, which proves that the 3DASRGAN model can reconstruct high-resolution HSIs with high efficiency.

Day-to-day changes in CO 2 emissions from human activities, in particular fossil-fuel combustion and cement production, reflect a complex balance of influences from seasonality, working days, weather and, most recently, the COVID-19 pandemic. Here, we provide a daily CO 2 emissions dataset for the whole year of 2020, calculated from inventory and near-real-time activity data. We find a global reduction of 6.3% (2,232 MtCO 2 ) in CO 2 emissions compared with 2019. The drop in daily emissions during the first part of the year resulted from reduced global economic activity due to the pandemic lockdowns, including a large decrease in emissions from the transportation sector. However, daily CO 2 emissions gradually recovered towards 2019 levels from late April with the partial reopening of economic activity. Subsequent waves of lockdowns in late 2020 continued to cause smaller CO 2 reductions, primarily in western countries. The extraordinary fall in emissions during 2020 is similar in magnitude to the sustained annual emissions reductions necessary to limit global warming at 1.5 °C. This underscores the magnitude and speed at which the energy transition needs to advance. Observed daily changes in CO 2 emissions from across the globe reveal the sectors and countries where pandemic-related emissions declines were most pronounced in 2020.

We constructed a near-real-time daily CO2 emission dataset, the Carbon Monitor, to monitor the variations in CO2 emissions from fossil fuel combustion and cement production since January 1, 2019, at the national level, with near-global coverage on a daily basis and the potential to be frequently updated. Daily CO2 emissions are estimated from a diverse range of activity data, including the hourly to daily electrical power generation data of 31 countries, monthly production data and production indices of industry processes of 62 countries/regions, and daily mobility data and mobility indices for the ground transportation of 416 cities worldwide. Individual flight location data and monthly data were utilized for aviation and maritime transportation sector estimates. In addition, monthly fuel consumption data corrected for the daily air temperature of 206 countries were used to estimate the emissions from commercial and residential buildings. This Carbon Monitor dataset manifests the dynamic nature of CO2 emissions through daily, weekly and seasonal variations as influenced by workdays and holidays, as well as by the unfolding impacts of the COVID-19 pandemic. The Carbon Monitor near-real-time CO2 emission dataset shows a 8.8% decline in CO2 emissions globally from January 1st to June 30th in 2020 when compared with the same period in 2019 and detects a regrowth of CO2 emissions by late April, which is mainly attributed to the recovery of economic activities in China and a partial easing of lockdowns in other countries. This daily updated CO2 emission dataset could offer a range of opportunities for related scientific research and policy making.Measurement(s)carbon dioxide emissionTechnology Type(s)computational modeling techniqueFactor Type(s)geographic location • sector • temporal intervalSample Characteristic - Environmentclimate systemSample Characteristic - LocationglobalMachine-accessible metadata file describing the reported data: 10.6084/m9.figshare.12994058

Growing nanotechnology use in agriculture can transform traditional practices. This study investigated the application of zinc oxide nanoparticles (ZnO NPs) in Hibiscus syriacus L. cultivation, examining: particle size (30, 50, and 90 nm), concentration (10, 50, 100, 500, and 1000 mg/L), and application method (seed priming and foliar application). In seed priming experiments, low concentrations (10–100 mg/L) of all tested NP sizes enhanced germination parameters, while higher concentrations showed inhibitory effects. The optimal treatment was 30 nm ZnO NPs at 50 mg/L, achieving a 68.89% germination rate. Seed priming also significantly improved seedling growth and physiological biochemistry. In pot experiments, foliar application studies showed that while both ZnSO 4 and ZnO NPS increased leaves Zn content, nanoparticle treatments (especially smaller particles at 30 and 50 nm) produced more sustained growth benefits, with ZnO NPS outperforming traditional zinc fertilizers, particularly at 50 mg/L with 50 nm particles. These findings highlight dual advantages of ZnO NPs as an effective seed priming and foliar application, demonstrating potential as an efficient fertilizer for ornamental woody plants. In addition, significant concentration thresholds were found in both application methods (with varying thresholds), above which phytotoxic effects were observed.

Osteosarcoma (OS) is the most common malignant bone tumor with high pathological heterogeneity. Our study aimed to investigate disulfidptosis-related modification patterns in OS and their relationship with survival outcomes in patients with OS. We analyzed the single-cell-level expression profiles of disulfidptosis-related genes (DSRGs) in both OS microenvironment and OS subclusters, and HMGB1 was found to be crucial for intercellular regulation of OS disulfidptosis. Next, we explored the molecular clusters of OS based on DSRGs and related immune cell infiltration using transcriptome data. Subsequently, the hub genes of disulfidptosis in OS were screened by applying multiple machine models. In vitro and patient experiments validated our results. Three main disulfidptosis-related molecular clusters were defined in OS, and immune infiltration analysis suggested high immune heterogeneity between distinct clusters. The in vitro experiment confirmed decreased cell viability of OS after ACTB silencing and higher expression of ACTB in patients with lower immune scores. Our study systematically revealed the underlying relationship between disulfidptosis and OS at the single-cell level, identified disulfidptosis-related subtypes, and revealed the potential role of ACTB expression in OS disulfidptosis.

Carcinoembryonic antigen (CEA), carbohydrate antigen 19-9 (CA19-9), carbohydrate antigen 125 (CA125), and alpha-fetoprotein (AFP) are widely used tumor markers for colorectal cancer (CRC), but their clinical significance is unknown when the levels of these tumor markers were within the normal range. This retrospective study included 2145 CRC patients. The entire cohort was randomly divided into training and validation datasets. The optimal cut-off values of tumor markers were calculated using X-tile software, and univariate and multivariate analyses were performed to assess its association with overall survival (OS). The nomogram model was constructed and validated. The entire cohort was randomly divided into a training dataset (1502 cases, 70%) and a validation dataset (643 cases,30%). Calculated from the training dataset, the optimal cut-off value was 2.9 ng/mL for CEA, 10.1 ng/mL for CA19-9, 13.4 U/mL for CA125, and 1.8 ng/mL for AFP, respectively. Multivariate analysis revealed that age, tumor location, T stage, N stage, preoperative CA19-9, and CA125 levels were independent prognostic predictors. Even within the normal range, CRC patients with relatively high levels of CA19-9 or CA125 worse OS compared to those with relatively low levels. Then, based on the independent prognostic predictors from multivariate analysis, two models with/without (model I/II) CA19-9 and CA125 were built, model I showed better prediction and reliability than model II. Within the normal range, relatively high levels of preoperative CA19-9 and CA125 were significantly associated with poor OS in CRC patients. The nomogram based on CA19-9 and CA125 levels showed improved predictive accuracy ability for CRC.

Assessing land use-based changes in ecosystem service values (ESVs) is a beneficial approach for land resource planning and ecologically sustainable development. Located in the south of China, Guilin is one of the first Sustainable Development Goals (SDGs) Innovation Demonstration Zones set up by China. It is a typical ecotourism city with an important ecological and economic status. In recent years, the time series, model fit, and spatial scale of ESV assessment in Guilin have needed to be improved in the context of rapid urbanization and natural change. In this study, an improved ESV assessment methodology was utilized to incorporate the effects of biomass, soil conservation, and precipitation and to adjust the equivalence factors based on the ratio of geographic and environmental parameters to the national average to make them heterogeneous in time and space in improving the practical fit of the assessment results. The study analyzed the evolution of land use and its contribution to ESVs in Guilin from 2000 to 2020. County and 3 km × 3 km grid scales were combined to reveal both broad and detailed spatial and temporal characteristics of ESVs in Guilin. The results show that the expansion of building land in Guilin is notable, and the amount of land use transfer continues to increase. ESVs fluctuated in a lateral S-shape, with significant differences in ESV effectiveness between counties, consistently high ESVs near waterbodies and ecological reserves, and low ESVs near commercial and industrial land and cultivated land. Despite the recovery trend in ESVs in the later years, there is still a gap between 2020 and 2000. To a certain extent, it helps Guilin optimize land allocation from different perspectives and promote ecological improvement and resource planning optimization.