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C-lignin is a homo-biopolymer, being made up of caffeyl alcohol exclusively. There is significant interest in developing efficient and selective catalyst for depolymerization of C-lignin, as it represents an ideal feedstock for producing catechol derivatives. Here we report an atomically dispersed Ru catalyst, which can serve as an efficient catalyst for the hydrogenolysis of C-lignin via the cleavage of C−O bonds in benzodioxane linkages, giving catechols in high yields with TONs up to 345. A unique selectivity to propenylcatechol (77%) is obtained, which is otherwise hard to achieve, because this catalyst is capable of hydrogenolysis rather than hydrogenation. This catalyst also demonstrates good reusability in C-lignin depolymerization. Detailed investigations by model compounds concluded that the pathways involving dehydration and/or dehydrogenation reactions are incompatible routes; we deduced that caffeyl alcohol generated via concurrent C−O bonds cleavage of benzodioxane unit may act as an intermediate in the C-lignin hydrogenolysis. Current demonstration validates that atomically dispersed metals can not only catalyze small molecules reactions, but also drive the transformation of abundant and renewable biopolymer. C-lignin represents an ideal feedstock for producing catechol derivatives. Here, the authors engineered an atomically dispersed Ru catalyst, which can cleave C−O bonds efficiently and circumvent C=C bonds hydrogenation selectively, thus leading to propenylcatechol in high yields with high TONs.

Producing monomeric phenols from lignin biopolymer depolymerization in a detachable and efficient manner comes under the spotlight on the fullest utilization of sustainable lignocellulosic biomass. Here, we report a low-loaded and highly dispersed Ru anchored on a chitosan-derived N -doped carbon catalyst (RuN/ZnO/C), which exhibits outstanding performance in the reductive catalytic fractionation of lignocellulose. Nearly theoretical maximum yields of phenolic monomers from lignin are achieved, corresponding to TON as 431 mol phenols mol Ru −1 , 20 times higher than that from commercial Ru/C catalyst; high selectivity toward propyl end-chained guaiacol and syringol allow them to be readily purified. The RCF leave high retention of (hemi)cellulose amenable to enzymatic hydrolysis due to the successful breakdown of biomass recalcitrance. The RuN/ZnO/C catalyst shows good stability in recycling experiments as well as after a harsh hydrothermal treatment, benefiting from the coordination of Ru species with N atoms. Characterizations of the RuN/ZnO/C imply a transformation from Ru single atoms to nanoclusters under current reaction conditions. Time-course experiment, as well as reactivity screening of a series of lignin model compounds, offer insight into the mechanism of current RCF over RuN/ZnO/C. This work opens a new opportunity for achieving the valuable aromatic products from lignin and promoting the industrial economic feasibility of lignocellulosic biomass. Lignin valorization becomes the spotlight on the full utilization of biomass. Here, the authors report a highly dispersed Ru catalyst for reductive catalytic fractionation of lignocellulose, which affords monophenols in theoretical maximum yields, along with high preservation of carbohydrate.

Laser ignition (LI) allows for precise manipulation of ignition timing and location and is promising for green combustion of automobile and rocket engines and aero-turbines under lean-fuel conditions with improved emission efficiency; however, achieving completely effective and reliable ignition is still a challenge. Here, we report the realization of igniting a lean methane/air mixture with a 100% success rate by an ultrashort femtosecond laser, which has long been regarded as an unsuitable fuel ignition source. We demonstrate that the minimum ignition energy can decrease to the sub-mJ level depending on the laser filamentation formation, and reveal that the resultant early OH radical yield significantly increases as the laser energy reaches the ignition threshold, showing a clear boundary for misfire and fire cases. Potential mechanisms for robust ultrashort LI are the filamentation-induced heating effect followed by exothermal chemical reactions, in combination with the line ignition effect along the filament. Our results pave the way toward robust and efficient ignition of lean-fuel engines by ultrashort-pulsed lasers.

We present an upper bound of cumulative return in financial trading time series to formulate the most possible profit of many trades. The bound can be used to formally analyze the cumulative return varied by the number of trades, the mean return, and transaction cost rate. We also prove and show the validation of the upper bound, and verify the trend of cumulative return is consistent with that of the proposed bound via simulation experiments. Introducing a set of stochastic assessment methodology based on bootstrap into the organization of experimental data, we illustrate the influence on cumulative return from the relationship between the mean of return and transaction cost rate, technical trading rules, and stock indexes. To the best of our knowledge, this is the first to present and prove a bound of cumulative return of a stock trading series in theory. Both theoretical analyses and simulation experiments show the presented bound is a good mathematical tool to evaluate the trading risks and chances using given trading rules in stock trading markets.

The anticipation of carbon emissions stemming from community electricity usage stands as a pivotal field of study. Such analysis holds the potential to furnish communities with informed pathways towards crafting viable carbon reduction strategies, thereby contributing significantly to the overarching goals of carbon peaking and eventual neutrality. The intricacies of carbon emissions from community electricity consumption are manifold, entangled with variables like energy consumption patterns, energy mix, and carbon emission coefficients. Hence, the imperative lies in crafting a predictive framework adept at holistically integrating these variables to enhance prognostic precision and reliability. Within this research, we propose a dynamic carbon emission factor regression model. This model is uniquely poised to capture real-time shifts influenced by changes in energy structure and policy landscapes, thereby amplifying predictive sensitivity. Leveraging the Particle Swarm Optimization (PSO) algorithm, we synchronously optimize the autoregressive terms ( p ) of the Autoregressive Integrated Moving Average (ARIMA) model and the moving average terms ( q ) of the MA model to attain a globally optimal solution. Crucially, this approach obviates the need for manual intervention and arbitrary parameter selection. In contrast to conventional optimization methodologies, our paper advances the PSO’s weight calculation mechanism. By assigning greater weight values during the initial iterations, the algorithm maintains robust global search capabilities. Subsequently, the inertia weight diminishes progressively throughout the iteration process, fostering precise local exploration.

This paper presents a novel carbon metering method for distribution grids that fully considers the impact of harmonics on the grid. Harmonics are sinusoidal waves with frequencies that are integer multiples of the fundamental frequency, and the presence of harmonics can lead to power losses, damage to equipment and degradation of power quality, thus increasing carbon emissions from the grid. Currently available carbon measurement methods often do not take this into account, so a more accurate method is needed to assess the carbon emissions of distribution grids. This new method, based on the carbon flow theory of trending power distribution, combines harmonic energy correction and node carbon potential correction, and is able to accurately calculate the carbon emissions as well as the harmonic carbon correction for each node and branch in the distribution network. In order to evaluate the carbon behavior characteristics of users more comprehensively, the study also adopts the DTW-K-Means clustering technique to group distribution network users. This innovative approach provides an effective technical means and theoretical basis for the monitoring, assessment and management of distribution network carbon emissions. This innovative method provides effective technical means and theoretical basis for distribution network carbon emission monitoring, assessment and management, not only considering the impact of harmonics on power metering, but also more accurately portraying the carbon behavior of users, which is expected to provide strong support for the reduction of grid carbon emissions.

The reductive catalytic fractionation (RCF) of lignocellulose, considering lignin valorization at design time, has demonstrated the entire utilization of all lignocellulose components; however, such processes always require catalysts based on precious metals or high‐loaded nonprecious metals. Herein, the study develops an ultra‐low loaded, atomically dispersed cobalt catalyst, which displays an exceptional performance in the RCF of lignocellulose. An approximately theoretical maximum yield of phenolic monomers (48.3 wt.%) from lignin is realized, rivaling precious metal catalysts. High selectivity toward 4‐propyl‐substituted guaiacol/syringol facilitates their purification and follows syntheses of highly adhesive polyesters. Lignin nanoparticles (LNPs) are generated by simple treatment of the obtained phenolic dimers and oligomers. RCF‐resulted carbohydrate pulp are more obedient to enzymatic hydrolysis. Experimental studies on lignin model compounds reveal the concerted cleavage of Cα–O and Cβ–O pathway for the rupture of β‐O‐4 structure. Overall, the approach involves valorizing products derived from lignin biopolymer, providing the opportunity for the comprehensive utilization of all components within lignocellulose. An ultra‐low loaded, atomically dispersed cobalt demonstrates exceptional catalytic performance in the reductive catalytic fractionation of lignocellulose. Such a process 1) maximizes the theoretical yield of phenolic monomers suitable for synthesizing highly adhesive polyesters, 2) produces phenolic dimers and oligomers feedstocks for preparing lignin nanoparticles, and 3) preserves cellulose and hemicellulose amenable to enzymatic hydrolysis.

Femtosecond filament-induced breakdown spectroscopy (FIBS) is an efficient approach in remote and in situ detection of a variety of trace elements, but it was recently discovered that the FIBS of water is strongly dependent on the large-bandgap semiconductor property of water, making the FIBS signals sensitive to laser ionization mechanisms. Here, we show that the sensitivity of the FIBS technique in monitoring metal elements in water can be efficiently improved by using chirped femtosecond laser pulses, but an asymmetric enhancement of the FIBS intensity is observed for the negatively and positively chirped pulses. We attribute the asymmetric enhancement to their different ionization rates of water, in which the energy of the photons participating in the ionization process in the front part of the negatively chirped pulse is higher than that in the positively chirped pulse. By optimizing the pulse chirp, we show that the limit of detection of the FIBS technique for metal elements in water, e.g., aluminum, can reach to the sub-ppm level, which is about one order of magnitude better than that by the transform-limited pulse. We further examine the FIBS spectra of several representative water samples including commercial mineral water, tap water, and lake water taken from two different environmental zones, i.e., a national park and a downtown business district (Changchun, China), from which remarkably different concentrations of Ca, Na, and K elements of these samples are obtained. Our results provide a possibility of using FIBS for direct and fast metal elemental analysis of water in different field environments.

Functional steel surfaces engineered through tailored micro-nano structures are increasingly vital for various applications such as high-performance aerospace components, energy conversion systems and defense equipment. Femtosecond laser filament processing is a recently proposed remote fabrication technique, showing the capability of fabricating micro-nano structures on irregular and large-area surfaces without the need of tight focusing. Nevertheless, the mechanisms underlying the formation of filament-induced structures remain not fully understood. Here we systematically investigate the formation mechanisms of filament-induced micro-nano structures on stainless steel surfaces by processing stainless steel in three manners: point, line, and area. We clarify the decisive role of the unique core–reservoir energy distribution of the filament in the formation of filament-induced micro-nano structures, and reveal that ablation, molten metal flow, and metal vapor condensation jointly drive the structure evolution through a dynamic interplay of competition and coupling, giving rise to the sequential morphological transitions of surface structures, from laser-induced periodic surface structures to ripple-like, crater-like, honeycomb-like, and ultimately taro-leaf-like structures. Our work not only clarifies the mechanisms of femtosecond laser filament processed morphological structures on steels but also provides insights onto intelligent manufacturing and design of advanced functional steel materials.

In order to reduce the damage of blasting to rock mass and improve the half-hole rate of presplitting blasting, the dynamic finite element analysis software ANSYS/LS-DYNA is used to simulate and analyze the action process of sequential controlled blasting. The effects of the detonation delay time of the postblasting hole and the hole spacing of the postblasting hole on the crack formation of the sequential controlled presplitting blasting are studied. The results show that when the blast hole with a diameter of 42 mm is used for sequential controlled presplitting blasting and the first blast hole pitch is 60 cm, the reasonable detonation delay time is 80∼120 μs. When the detonation delay time is 80 μs, the reasonable postblast hole spacing is 60 cm. Field tests show that when reasonable optimized blasting parameters are used, presplit blasting with sequential control can reduce drilling workload and explosive consumption. The sequential controlled presplitting blasting not only increases the hole spacing but also plays a better role in protecting the surrounding rock.