The Innovation最新文献

The chemical recycling of polyolefin presents a considerable challenge, especially as upcycling methods struggle with the reality that plastic wastes typically consist of mixtures of polyethylene (PE), polystyrene (PS), and polypropylene (PP). We report a catalytic aerobic oxidative approach for polyolefins upcycling with the corresponding carboxylic acids as the product. This method encompasses three key innovations. Firstly, it operates under atmospheric pressure and mild conditions, using O₂ or air as the oxidant. Secondly, it is compatible with HDPE (high-density polyethylene), LDPE (low-density polyethylene), PS, PP, and their blends; Thirdly, it utilizes an economical and recoverable metal catalyst. It has been demonstrated that this approach can efficiently degrade mixed wastes of plastic bags, bottles, masks, and foam boxes.

The tumor microenvironment is comprised of a complex mixture of different cell types interacting under conditions of nutrient deprivation, but the metabolism therein is not fully understood due to difficulties in measuring metabolic fluxes and exchange of metabolites between different cell types in vivo. Genome-scale metabolic modeling enables estimation of such exchange fluxes as well as an opportunity to gain insight into the metabolic behavior of individual cell types. Here, we estimated the availability of nutrients and oxygen within the tumor microenvironment using concentration measurements from blood together with a metabolite diffusion model. In addition, we developed an approach to efficiently apply enzyme usage constraints in a comprehensive metabolic model of human cells. The combined modeling reproduced severe hypoxic conditions and the Warburg effect, and we found that limitations in enzymatic capacity contribute to cancer cells’ preferential use of glutamine as a substrate to the citric acid cycle. Furthermore, we investigated the common hypothesis that some stromal cells are exploited by cancer cells to produce metabolites useful for the cancer cells. We identified over 200 potential metabolites that could support collaboration between cancer cells and cancer associated fibroblasts, but when limiting to metabolites previously identified to participate in such collaboration, no growth advantage was observed. Our work highlights the importance of enzymatic capacity limitations for cell behaviors and exemplifies the utility of enzyme constrained models for accurate prediction of metabolism in cells and tumor microenvironments.

Heat is almost everywhere. Unlike for electricity, which can be easily manipulated, the current ability to control heat is still highly limited owing to spontaneous thermal dissipation imposed by the second law of thermodynamics. Optical illumination and pressure have been used to switch endothermic/exothermic responses of materials via phase transitions; however, these strategies are less cost-effective and unscalable. Herein, we spectroscopically demonstrate the glassy crystal state of 2-amino-2-methyl-1,3-propanediol (AMP) to realise an affordable, easily manageable approach for thermal energyrecycling. The supercooled state of AMP is so sensitive to pressure that even several mega-pascals can induce crystallization to the ordered crystal, resulting in an substantial temperature increase of 48 K within 20 s. Furthermore, we demonstrate a proof-of-concept device capable of programming heat with an extremely high work-to-heat conversion efficiency of ∼383. Such delicate, efficient tuning of heat might remarkably facilitate rational utilisation of waste heat.

Differences in progress across sustainable development goals (SDGs) are widespread globally; meanwhile, the rising call for prioritizing specific SDGs may exacerbate such gaps. Nevertheless, how these progress differences would influence global sustainable development has been long neglected. Here, we present the first quantitative assessment of SDGs’ progress differences globally by adopting the SDGs progress evenness index. Our results highlight that the uneven progress across SDGs has been a hindrance to sustainable development because: (1) it is strongly associated with many public health risks (e.g., air pollution), social inequalities (e.g., gender inequality, modern slavery, and wealth gap), and a reduction in life expectancy; (2) it is also associated with deforestation and habitat loss in terrestrial and marine ecosystems, increasing the challenges related to biodiversity conservation; (3) most countries with a low average SDGs performance show lower progress evenness, which further hinders their fulfillment of SDGs; (4) many countries with a high average SDGs performance also showcase stagnation or even retrogression in progress evenness, which is partly ascribed to the antagonism between climate actions and other goals. These findings highlight that while setting SDGs priorities might be more realistic under the constraints of multiple global stressors, caution must be exercised to avoid new problems from intensifying uneven progress across goals. Moreover, our study reveals that the urgent needs regarding SDGs of different regions seem complementary, emphasizing that regional collaborations (e.g., demand-oriented carbon trading between SDGs poorly-performed and well-performed countries) may promote sustainable development achievements at the global scale.

Organic crystal structures exert a profound impact on the physicochemical properties and biological effects of organic compounds. Quantum mechanics (QM) based crystal structure predictions (CSP) have somewhat alleviated the dilemma that experimental crystal structure investigations struggle to conduct complete polymorphism studies, but the high computing cost poses a challenge to its widespread application. The current study aims to construct DeepCSP, a feasible pure machine learning framework for minute-scale rapid organic crystal structure prediction. Initially, based on 177,746 data entries from the Cambridge Crystal Structure Database (CSD), a generative adversarial network was built to conditionally generate trial crystal structures under selected feature constraints for the given molecule. Simultaneously, a graph convolutional attention network was employed to predict the density of stable crystal structures for the input molecule. Subsequently, the distances between the predicted density and the definition-based calculated density would be considered as the crystal structure screening and ranking basis, and finally, the density-based crystal structure ranking would be output. Such two distinct algorithms, performing the generation and ranking functionalities respectively, collectively constitute the DeepCSP, which has demonstrated compelling performance in marketed drug validations, achieving an accuracy rate exceeding 80% and hit rate surpassing 85%. Inspiringly, the computing speed of the pure machine learning methodology demonstrates the potential of artificial intelligence in advancing CSP research.