Crystal structure and Hirshfeld surface analysis of two iHOFs based on CH...NC hydrogen bonding.

Two ionic hydrogen-bonded organic frameworks (iHOFs) assembled from 4-cyano-N-(4-cyanobenzyl)pyridinium, have been crystallized with Br- and antimony(III) pentabromide, [SbBr5]2-, as counter-ions and characterized. These are 4-cyano-N-(4-cyanobenzyl)pyridinium bromide, C14H10N3+·Br-, and bis[4-cyano-N-(4-cyanobenzyl)pyridinium] antimony(III) pentabromide, (C14H10N3)2[SbBr5]. The CH...NC interactions induced by templating anions construct disparate frameworks. Hirshfeld surface analysis indicated that these crystals exhibit two types of hydrogen-bonding interactions, specifically CH...NC and CH...Br. Consecutive reverse-parallel CH...NC hydrogen-bonding interactions in these crystals induced the formation of a large number of CH...NC bonds which exhibit both cis and trans configurations.

Achieving a solar-to-chemical efficiency of 3.6% in ambient conditions by inhibiting interlayer charges transport.

Efficiently converting solar energy into chemical energy remains a formidable challenge in artificial photosynthetic systems. To date, rarely has an artificial photosynthetic system operating in the open air surpassed the highest solar-to-biomass conversion efficiency (1%) observed in plants. In this study, we present a three-dimension polymeric photocatalyst achieving a solar-to-H2O2 conversion efficiency of 3.6% under ambient conditions, including real water, open air, and room temperature. The impressive performance is attributed to the efficient storage of electrons inside materials via expeditious intramolecular charge transfer, and the fast extraction of the stored electrons by O2 that can diffuse into the internal pores of the self-supporting three-dimensional material. This construction strategy suppresses the interlayer transfer of excitons, polarizers and carriers, effectively increases the utilization of internal excitons to 82%. This breakthrough provides a perspective to substantially enhance photocatalytic performance and bear substantial implications for sustainable energy generation and environmental remediation.

Environmental transformation and hazards of decachlorobiphenyl on suspended particles under sunlight irradiation.

Decachlorobiphenyl (PCB-209) can be widely detected in suspended particles and sediments due to its large hydrophobicity, and some of its transformation products may potentially threaten organisms through the food chain. Here we investigate the photochemical transformation of PCB-209 on suspended particles from the Yellow River. It was found that the suspended particles had an obvious shielding effect to largely inhibit the photodegradation of PCB-209. Meanwhile, the presence of inorganic ions (e.g. Mg2+ and NO3-) and organic matters (e.g. humic acid, HA) in the Yellow River water inhibited the reaction. The main transformation products of PCB-209 were lower-chlorinated and hydroxylated polychlorinated biphenyls (OH-PCBs), and small amounts of pentachlorophenol (PCP) and polychlorinated dibenzofurans (PCDFs) were also observed. The mechanisms of PCP formation by double •OH attacking carbon bridge and PCDFs formation by elimination reaction of ionic state OH-PCBs were proposed using theoretical calculations, which provided some new insights into the inter-transformations between persistent organic pollutants. In combination with VEGA and EPI Suite software, some intermediates such as PCDFs were more toxic to organisms than PCB-209. This study deepens the understanding of the transformation behavior of PCB-209 on suspended particles under sunlight.

On-Site Ratiometric Analysis of UO22+ with High Selectivity.

Uranyl ions (UO22+) are recognized as important indicators for monitoring sudden nuclear accidents. However, the interferences coexisting in the complicated environmental matrices impart serious constraints on the reliability of current on-site monitoring methods. Herein, a novel ratiometric method for the highly sensitive and selective detection of UO22+ is reported based on a [Eu(diaminoterephthalic acid)] (Eu-DATP) metal-organic framework. Benefiting from the unique chemical structure of Eu-DATP, energy transfer from DATP to UO22+ was enabled, resulting in the up-regulated fluorescence of UO22+ and the simultaneous down-regulated fluorescence of Eu3+. The limit of detection reached as low as 2.7 nM, which was almost 2 orders of magnitude below the restricted limit in drinking water set by the United States Environmental Protection Agency (130 nM). The Eu-DATP probe showed excellent specificity to UO22+ over numerous interfering species, as the intrinsic emissions of UO22+ were triggered. This unprecedentedly high selectivity is especially beneficial for monitoring UO22+ in complicated environmental matrices with no need for tedious sample pretreatment, such as filtration and digestion. Then, by facilely equipping a Eu-DATP-based sampler on a drone, remotely controlled sampling and on-site analysis in real water samples were realized. The concentrations of UO22+ were determined to be from 16.5 to 23.5 nM in the river water of the Guangzhou downtown area, which was consistent with the results determined by the gold-standard inductively coupled plasma mass spectrometry. This study presents a reliable and convenient method for the on-site analysis of UO22+.

Photodegradation of polycyclic aromatic hydrocarbons on soil surface: Kinetics and quantitative structure-activity relationship (QSAR) model development.

Polycyclic aromatic hydrocarbons (PAHs) have attracted much attention because of their widespread existence and toxicity. Photodegradation is the main natural decay process of PAHs in soil. The photodegradation kinetics of benzopyrene (BaP) on 16 kinds of soils and 10 kinds of PAHs on Hebei (HE) soil were studied. The results showed that BaP had the highest degradation rate in Shaanxi (SN) soil (kobs = 0.11 min-1), and anthracene (Ant) was almost completely degraded after 16 h of irradiation in HE soil. Two quantitative structure-activity relationship (QSAR) models were established by the multiple linear regression (MLR) method. The developed QSAR models have good stability, robustness and predictability. The model revealed that the main factors affecting the photodegradation of PAHs are soil organic matter (SOM) and the energy gap between the highest occupied molecular orbital and the lowest unoccupied molecular orbital (Egap). SOM can function as a photosensitizer to induce the production of active species for photodegradation, thus favoring the photodegradation of PAHs. In addition, compounds with lower Egap are less stable and more reactive, and thus are more prone to photodegradation. Finally, the QSAR model was optimized using machine learning approach. The results of this study provide basic information on the photodegradation of PAHs and have important significance for predicting the environmental behavior of PAHs in soil.

Enhancing Photosynthesis Efficiency of Hydrogen Peroxide by Modulating Side Chains to Facilitate Water Oxidation at Low-Energy Barrier Sites.

Hydrogen peroxide (H2O2) is a crucial oxidant in advanced oxidation processes. In situ, photosynthesis of it in natural water holds the promise of practical application for water remediation. However, current photosynthesis of H2O2 systems primarily relies on oxygen reduction, leading to limited performance in natural water with low dissolved oxygen or anaerobic conditions found in polluted water. Herein, a novel photocatalyst based on conjugated polymers with alternating electron donor-acceptor structures and electron-withdrawing side chains on electron donors is introduced. Specifically, carbazole functions as the electron donor, triazine serves as the electron acceptor, and cyano acts as the electron-withdrawing side chain. Notably, the photocatalyst exhibits a remarkable solar-to-chemical conversion of 0.64%, the highest reported in natural water. Furthermore, even in anaerobic conditions, it achieves an impressive H2O2 photosynthetic efficiency of 1365 µmol g-1 h-1, surpassing all the reported photosynthetic systems of H2O2. This remarkable improvement is attributed to the effective relocation of the water oxidation active site from a high-energy carbazole to a low-energy acetylene site mediated by the side chains, resulting in enhanced O2 or H2O2 generation from water. This breakthrough offers a new avenue for efficient water remediation using advanced oxidation technologies in oxygen-limited environments, holding significant implications for environmental restoration.

Rapid analysis of dichlorvos via releasing the phosphate core.

Monitoring the residual dichlorvos (O,O-dimethyl-O-2,2-dichlorovinylphosphate, DDVP) in food has received extensive attention owing to its large consumption in agriculture. However, the previous sensing methods are not time-efficient enough due to the long incubation time for enzyme inhibition (tens of minutes to hours) or bottlenecked by the complicated procedures for senor fabrication. Herein, a novel sensing strategy is proposed based on the hydrolysis of DDVP into PO43-. By using alkaline phosphatase for hydrolysis, a certain portion of DDVP was transformed to PO43- within only 8 min. Then, the released PO43- was detected by a fluorescent terbium metal-organic framework (Tb-MOF). The coordination of the naked P-O groups to the metal nodes of the Tb-MOF disturbed the antenna effects of its ligands. Thus, DDVP was quantified by the decrease of the fluorescence of Tb ions. Based on this method, DDVP residues on plum surfaces were collected by swabs and successfully detected. The recovery of DDVP was determined in the range from 105 % to 115 %, demonstrating the quantification accuracy of this method. The detection limit reached 4.7 µM, which was lower than the restricted amount in fruit set by the National Standard of China. The present method provides an efficient and user-friendly way for the detection of DDVP and many other organophosphorus pesticides in food.

Research on product detection and recognition methods for intelligent vending machines.

With the continuous development of China's economy and the improvement of residents' living standards, it also brings increasing costs of labor and rent. In addition, the impact of the pandemic on the entity industry has brought opportunities for the development of new retail models. Based on the booming development of artificial intelligence, big data, and mobile payment in the new era, the new retail industry using artificial intelligence technology has shown outstanding performance in the market. Among them, intelligent vending machines have emerged in the new retail model. In order to provide users with a good shopping experience, the product detection speed and accuracy of intelligent vending machines must be high enough. We adopt Faster R-CNN, a mature object detection algorithm in deep learning, to solve the commodity settlement scenario of intelligent vending machines.

Noncovalent Tagging for Identifying Unknown Contaminants of Specific Bioactivity in Environmental Water.

Identifying contaminants of specific bioactivities from complicated environmental matrices remains costly and time-consuming, as it requires us to not only resolve their structures but also determine their bioactivities. Herein, a novel noncovalent tagging method is integrated in mass spectrometry for identifying unknown contaminants that target dopamine (DA) receptors. Via proteolysis of bovine serum albumin, a stereoselective hexapeptide (ACFAVE) is selected for noncovalently tagging the contaminants that possess the stereostructural characteristics of binding to DA receptors. The tagged contaminants can be readily distinguished from the coexisting species for subsequent structural analysis based on the tagging-induced shifts of the mass-to-charge ratios. Thus, both bioactivity evaluation and structure analysis are accomplished via mass spectrometry. By using this method, 1,3-diphenylguanidine (DPG), a widely used additive in rubber and plastics, is successfully identified out of 2495 features detected in the Pearl River water, with its concentration determined as only 9.8 µg L-1. Furthermore, DPG is confirmed as a potential disrupter to the DA receptors via a simulated docking experiment, which has not been reported before. The present noncovalent tagging method provides a cost-effective and time-efficient way of identifying bioactive molecules in complicated matrices. And proteolysis of proteins is promising for developing more taggants with other desired stereoselectivities in the future.

Two-Dimensional Conductive Metal-Organic Framework for Small-Molecule Sensing in Aqueous Solution.

Two-dimensional (2D) conductive metal-organic frameworks (cMOFs) have emerged as powerful transducers for electrochemical sensing. However, electrochemical sensing in aqueous solutions remains at a very early stage for 2D cMOFs. Herein, the interfacial capacitances of a 2D cMOF are utilized for electrochemical sensing for the first time. Various redox-innocent compounds along with redox-active compounds in aqueous solutions are successfully detected based on the responses of two capacitance peaks at low voltages. The quantitative sensitivity to ascorbic acid is even an order of magnitude higher than the previous voltammetric method. Further investigation demonstrates that the responses are rooted in the pseudocapacitances of the 2D cMOF, i.e., the transitions among the multiple redox states of the ligands. The analytes are suggested to alert the d-p conjugation and exchange electrons with the 2D cMOF. These deep insights in response mechanisms represent an important step for promoting the application of 2D cMOFs in chemical sensing.

Advances in immune response to pulmonary infection: Nonspecificity, specificity and memory.

The lung immune response consists of various cells involved in both innate and adaptive immune processes. Innate immunity participates in immune resistance in a nonspecific manner, whereas adaptive immunity effectively eliminates pathogens through specific recognition. It was previously believed that adaptive immune memory plays a leading role during secondary infections; however, innate immunity is also involved in immune memory. Trained immunity refers to the long-term functional reprogramming of innate immune cells caused by the first infection, which alters the immune response during the second challenge. Tissue resilience limits the tissue damage caused by infection by controlling excessive inflammation and promoting tissue repair. In this review, we summarize the impact of host immunity on the pathophysiological processes of pulmonary infections and discuss the latest progress in this regard. In addition to the factors influencing pathogenic microorganisms, we emphasize the importance of the host response.

Disease tolerance: a protective mechanism of lung infections.

Resistance and tolerance are two important strategies employed by the host immune response to defend against pathogens. Multidrug-resistant bacteria affect the resistance mechanisms involved in pathogen clearance. Disease tolerance, defined as the ability to reduce the negative impact of infection on the host, might be a new research direction for the treatment of infections. The lungs are highly susceptible to infections and thus are important for understanding host tolerance and its precise mechanisms. This review focuses on the factors that induce lung disease tolerance, cell and molecular mechanisms involved in tissue damage control, and the relationship between disease tolerance and sepsis immunoparalysis. Understanding the exact mechanism of lung disease tolerance could allow better assessment of the immune status of patients and provide new ideas for the treatment of infections.

A New Risk Model based on the Machine Learning Approach for Prediction of Mortality in the Respiratory Intensive Care Unit.

BACKGROUND: Intensive care unit (ICU) resources are inadequate for the large population in China, so it is essential for physicians to evaluate the condition of patients at admission. In this study, our objective was to construct a machine-learning risk prediction model for mortality in respiratory intensive care units (RICUs). METHODS: This study involved 817 patients who made 1,063 visits and who were admitted to the RICU from 2012 to 2017. Potential predictors such as demographic information, laboratory results, vital signs and clinical characteristics were considered. We constructed eXtreme Gradient Boosting (XGBoost) models and compared performances with random forest models, logistic regression models and clinical scores such as Acute Physiology and Chronic Health Evaluation II (APACHE II) and the sequential organ failure assessment (SOFA) system. The model was externally validated using data from Medical Information Mart for Intensive Care (MIMIC-III) database. A web-based calculator was developed for practical use. RESULTS: Among the 1,063 visits, the RICU mortality rate was 13.5%. The XGBoost model achieved the best performance with the area under the receiver operating characteristics curve (AUROC) of 0.860 (95% confidence interval (CI): 0.808 - 0.909) in the test set, which was significantly greater than APACHE II (0.749, 95% CI: 0.674 - 0.820; P = 0.015) and SOFA (0.751, 95% CI: 0.669 - 0.818; P = 0.018). The Hosmer-Lemeshow test indicated a good calibration of our predictive model in the test set with a P-value of 0.176. In the external validation dataset, the AUROC of XGBoost model was 0.779 (95% CI: 0.714 - 0.813). The final model contained variables that were previously known to be associated with mortality, but it also included some features absent from the clinical scores. The mean N-terminal pro-B-type natriuretic peptide (NTproBNP) of survivors was significantly lower than that of the non-survival group (2066.43 pg/mL vs. 8232.81 pg/mL; P < 0.001). CONCLUSIONS: Our results showed that the XGBoost model could be a suitable model for predicting RICU mortality with easy-to-collect variables at admission and help intensivists improve clinical decision-making for RICU patients. We found that higher NT-proBNP can be a good indicator of poor prognosis.

Novel lanthanide nanoparticle frameworks for highly efficient photoluminescence and hypersensitive detection.

Despite the excellent luminescent properties of lanthanide clusters (LnCs), their suprastructures that inherit their characteristic luminescent properties are scarcely reported. Herein, novel and highly luminescent suprastructures are synthesized via a two-step assembly method to incorporate LnCs in covalent organic frameworks (COFs). COFs are pre-synthesized and decorated with rigid anchoring groups on their nanochannel walls, which provide one-dimensional confined spaces for the subsequent in situ assembly of luminescent LnCs. The confined LnCs are termed nanoparticles (NPs) to distinguish them from the pure LnCs. Secondary micropores with predictable sizes are successfully formed between the walls of the nanochannels and the orderly aligned NPs therein. By using a small organic ligand that can efficiently sensitize Ln(iii) cations in the assembly processes, the obtained composites show high quantum yields above 20%. The fluorescence can even be effectively maintained across nine pH units. The secondary micropores further enable the unambiguous discrimination of six methinehalides and ultrasensitive detection of uranyl ions. This study provides a new type of luminescent material that has potential for sensing and light emitting.

Multi-objective steel plate cutting optimization problem based on real number coding genetic algorithm.

The rectangular packing problem is an NP-complete combinatorial optimization problem. This problem occurs widely in social production scenarios, with steel plate cutting being one example. The cutting scheme for the rectangular packing problem needs to be improved because, without the globally optimal solution, there are many unnecessary edges in the steel cutting process. Based on a practical roll-fed disc shearing steel plate optimization problem, this paper explores a generalized packing method for rectangles of special dimensions and abstractly condenses complex quantitative relationships to establish a multi-objective mixed-integer nonlinear programming model. An innovative algorithm design based on a genetic algorithm is established to plan the cutting scheme in a high-speed and efficient way. The outcome is a utilization rate of up to 92.73% for raw materials and a significant reduction in labor, providing a guide for practical production and processing tasks. The advantages and disadvantages of the model and algorithm are discussed, and it is concluded that this rectangular packing method has strong universality and generalization ability, allowing rectangular packing tasks with large data volumes to be completed within a short time.

Hydrogen bond networks in gas-phase complex anions.

Hydrogen bond networks (HBNs) have piqued the interest of the scientific community due to their crucial roles in nature. However, HBNs that are isolated from complicated backgrounds for unraveling their characteristics are still scarce. Herein, we propose that HBNs exist in complex anions formed between α-cyclodextrin (α-CD) and four benzoic acids (RBAs) in the gas phase. The complex anions are facilely extracted from solutions via the electrospray ionization technique, and subsequently activated through collision for the investigation of their transition dynamics. It is revealed that the generation of deprotonated α-CD and neutral RBAs is the unexpected dominant dissociation pathway for all the four complex anions, and the complex anions formed from more acidic RBAs exhibit higher stabilities. These dissociation results are successfully explained by the cooperative stretching dynamics of the proposed HBNs that are formed involving the intramolecular HBN of α-CD and the intermolecular hydrogen bonds (HBs) between α-CD and RBAs. Furthermore, the rarely observed low barrier HBs (LBHBs) are suggested to be present in the HBNs. It is believed that the present complex anions can serve as a facilely accessible and informative model for studying HBNs in the future.

A Linear Ultrasonic Motor Based on Coupling Vibration Mode.

A coupled linear ultrasonic motor (LUSM) based on an eccentric constraint was proposed. Two pieces of oblique piezoelectric ceramics were arranged at each end of the elastomer, and the polarization direction of the ceramics was vertically upward. Using the tilting characteristics of the piezoelectric ceramics, the two ends of the fixed piezoelectric ceramics formed an eccentric restraint on the motor, providing conditions for the motor to generate coupled modes. When the elastomer of the motor generated the coupling vibration, the motion trajectories of the driving feet ends were oblique straight lines, and the oblique straight-line motion trajectories of the upper and lower driving feet ends were in opposite directions, driving the upper and lower sliders to run simultaneously. The stator parameters were optimized by using ANSYS to obtain larger amplitudes for the ends of the driving feet in both X and Z directions. The structure and operation principle of the motor are explained in detail. A prototype was fabricated to study the arrangement scheme with fixed constraints at the ends of the motor. The frequency-velocity characteristics, voltage-velocity characteristics, and mechanical characteristics of the motor were tested. The no-load speed and maximum output power were measured to be 45.9 mm/s and 3.24 mW.

Identifying a selective oligopeptide clamp in the gas phase.

Oligopeptide foldamers are promising as minimalist functional analogues to proteins. Herein, we report a versatile and cost-effective experimental scheme in the gas phase that can facilely identify selective oligopeptides and unambiguously resolve the corresponding folding conformations. Based on this methodology, a stereoselective oligopeptide clamp targeting ß2-blockers is successfully identified.

Deep Reinforcement Learning Based Resource Allocation Strategy in Cloud-Edge Computing System.

The rapid development of mobile device applications put tremendous pressure on edge nodes with limited computing capabilities, which may cause poor user experience. To solve this problem, collaborative cloud-edge computing is proposed. In the cloud-edge computing, an edge node with limited local resources can rent more resources from a cloud node. According to the nature of cloud service, cloud service can be divided into private cloud and public cloud. In a private cloud environment, the edge node must allocate resources between the cloud node and the edge node. In a public cloud environment, since public cloud service providers offer various pricing modes for users' different computing demands, the edge node also must select the appropriate pricing mode of cloud service; which is a sequential decision problem. In this stydy, we model it as a Markov decision process and parameterized action Markov decision process, and we propose a resource allocation algorithm cost efficient resource allocation with private cloud (CERAI) and cost efficient resource allocation with public cloud (CERAU) in the collaborative cloud-edge environment based on the deep reinforcement learning algorithm deep deterministic policy gradient and P-DQN. Next, we evaluated CERAI and CERAU against three typical resource allocation algorithms based on synthetic and real data of Google datasets. The experimental results demonstrate that CERAI and CERAU can effectively reduce the long-term operating cost of collaborative cloud-side computing in various demanding settings. Our analysis can provide some useful insights for enterprises to design the resource allocation strategy in the collaborative cloud-side computing system.

High-quality full-color carbon quantum dots synthesized under an unprecedentedly mild condition.

Carbon quantum dots (CQDs) are highly promising to be applied in light-emitting, chemosensing, and other cutting-edge domains. Herein, we successfully fabricate high-quality full-color CQDs under unprecedentedly low temperature and pressure (85°C, 1.88 bar). Stable and narrow fluorescent emissions ranging from blue to green and red light were realized by simple amine engineering, which were further mixed into white-light CQDs with the absolute photoluminescent quantum yield reaching 19.2%. The average mass yield of the CQDs reached 69.0%. The optical performances demonstrated that the CQDs possessed uniform luminescent centers and dominant radiative decay channels. Component analysis further suggested that dehydrated condensation between carboxyl and amine groups directed the growth of the CQDs. By utilizing the CQDs, full-color light-emitting diodes and logic gate sensors were developed. This study paves an important step for promoting the application of CQDs by providing an energy-efficient, safe, and productive synthetic strategy.