Efficient CO_(2) fixation with acetophenone on Ag-CeO_(2) electrocatalyst by a double activation strategy

The electrocarboxylation reaction is an attractive means to convert CO_(2) into valuable chemicals under ambient conditions,while it still suffers from low efficiency due to the high stability of CO_(2).In this work,we report a double activation strategy for simultaneously activating CO_(2) and acetophenone by silver-doped CeO_(2)(Ag-CeO_(2)) nanowires,featuring as an effective electrocatalyst for electrocarboxylation of acetophenone with CO_(2).Compared to the Ag foil,Ag nanoparticles and CeO_(2) nanowires,the Ag-CeO_(2)nanowire catalyst allowed to reduce the onset potential difference between CO_(2) and acetophenone activation,thus enabling efficient electrocarboxylation to form 2-phenyllactic acid.The Faradaic efficiency for producing 2-phenyllactic acid reached 91%at−1.8 V versus Ag/AgI.This double activation strategy of activating both CO_(2)and organic substrate molecules can benefit the catalyst design to improve activities and selectivities in upgrading CO_(2)fixation for higher-value electrocarboxylation.

Enhanced cold active lipase production by metagenomic library recombinant clone CALIP3 with a step-wise temperature and dissolved oxygen level control strategy

A metagenomic library recombinant clone CAPL3, an Escherichia coli strain generated by transformed with metagenomic library from deep-sea sediments, can efficiently produce cold active lipase. The effects of both temperature and dissolved oxygen（DO） on cold active lipase production by batch culture of metagenomic library recombinant clone（CAPL3） from deep-sea sediment were investigated. First, a two-stage temperature control strategy was developed, in which the temperature was kept at 34 ℃ for the first 15 h, and then switched to30 ℃. The cold active lipase activity and productivity reached 315.2 U·ml^-1and 8.08 U·ml^-1·h^-1, respectively,increased by both 14.5% compared to the results obtained with temperature controlled at 30℃. In addition, different DO control modes were conducted, based on the data obtained from the different DO control strategies and analysis of kinetics parameters at different DO levels. A step-wise temperature and DO control strategy were developed to improve lipase production, i.e., temperature and DO level were controlled at 34℃, 30% during 0–15 h;30 ℃, 30% during 15–18 h, and 30 ℃, 20% during 18–39 h. With this strategy, the maximum lipase activity reached 354.6 U·ml^-1at 39 h, which was 28.8% higher than that achieved without temperature and DO control（275.3 U·ml^-1）.

An Extended Algorithm of Flexibility Analysis in Chemical Engineering Processes

An extended algorithm of flexibility analysis with a local adjusting method for flexibility region of chemical processes, which is based on the active constraint strategy, is proposed, which fully exploits the flexibility region of the process system operation. The hyperrectangular flexibility region determined by the extended algorithm is larger than that calculated by the previous algorithms. The limitation of the proposed algorithm due to imperfect convexity and its corresponding verification measure are also discussed. Both numerical and actual chemical process examples are presented to demonstrate the effectiveness of the new algorithm.

What Information Gap Activities can Teach Us in TESOL

Starting from presenting and analyzing some information gap activities during the previous teaching experience, this article has inferred the major roles of information gap activities. Some strategies to implement the information gap activities are also recommended together with the functions of the instructors via these activities. What information gap activities can teach us in TESOL （teaching English for speakers of other languages） is that information gap activities contribute to setting up a climate of a mutual autonomous learning style both for the learners and the instructors, and these activities activate a diversity in the learning atmosphere.

EFFICIENT NONNEGATIVE MATRIX FACTORIZATION VIA MODIFIED MONOTONE BARZILAI-BORWEIN METHOD WITH ADAPTIVE STEP SIZES STRATEGY

In this paper,we develop an active set identification technique.By means of the active set technique,we present an active set adaptive monotone projected Barzilai-Borwein method(ASAMPBB)for solving nonnegative matrix factorization(NMF)based on the alternating nonnegative least squares framework,in which the Barzilai-Borwein(BB)step sizes can be adaptively picked to get meaningful convergence rate improvements.To get optimal step size,we take into account of the curvature information.In addition,the larger step size technique is exploited to accelerate convergence of the proposed method.The global convergence of the proposed method is analysed under mild assumption.Finally,the results of the numerical experiments on both synthetic and real-world datasets show that the proposed method is effective.

Vocabulary Teaching Strategies Focusing on Language Learner

As we enter the 21st century, acquisition of vocabulary has assumed a more important role, and as some would argue, it has assumed the central role in learning a second language. On one hand, students have a strong desire to improve their English level in order to make them more adaptable when they step into this competitive society after graduation. On the other hand, most students are facing a great difficulty in learning a large number of vocabularies though they do spend a lot of time and energy on it. Also, recent researches show that students don＇t like the classroom that is still teacher-centered and lecture-based. They want to be more involved in class and more responsible for their own studies. Hence, it is important to equip students with necessary strategies for dealing with vocabulary learning activities. In this paper, three strategies are recommended： extensive listening ＆ reading and contextual guesswork, communicative activities and group-work and the use of dictionary.

Luminescence-activated Pt(Ⅳ)prodrug for in situ triggerable cancer therapy

Anticancer platinum prodrugs that can be controllably activated are highly desired for personalized precision medicine and patient compliance in cancer therapy.However,the clinical application of platinum(Ⅳ)prodrugs(Pt(Ⅳ))is restricted by tissue penetration of external irradiation.Here,we report a novel Pt(Ⅳ)activation strategy based on endogenous luminescence of tumor microenvironment responsiveness,which completely circumvents the limitation of external irradiation.The designed Pt(Ⅳ)–Lu,a mixture of trans,trans,trans-[Pt(N_(3))_(2)(OH)_(2)(py)_(2)]and luminol(Lu),has controllable activation property:it remains inert in reductant environment and normal tissues,but under tumor microenvironment,Lu will be oxidized to produce blue luminescence,which rapidly reduce Pt(Ⅳ)to Pt(Ⅱ)without the need of any external activator.Pt(Ⅳ)–Lu shows excellent responsive antitumor ability both in vitro and in vivo.Compared to cisplatin,the median lethal dose in BALB/c mice increased by an order of magnitude.Our results suggest that Pt(Ⅳ)–Lu exhibits highly controllable activation property,superior antitumor activity,and good biosafety,which may provide a novel strategy for the design of platinum prodrugs.

Automatic Requirement Dependency Extraction Based on Integrated Active Learning Strategies

Since requirement dependency extraction is a cognitively challenging and error-prone task,this paper proposes an automatic requirement dependency extraction method based on integrated active learning strategies.In this paper,the coefficient of variation method was used to determine the corresponding weight of the impact factors from three different angles:uncertainty probability,text similarity difference degree and active learning variant prediction divergence degree.By combining the three factors with the proposed calculation formula to measure the information value of dependency pairs,the top K dependency pairs with the highest comprehensive evaluation value are selected as the optimal samples.As the optimal samples are continuously added into the initial training set,the performance of the active learning model using different dependency features for requirement dependency extraction is rapidly improved.Therefore,compared with other active learning strategies,a higher evaluation measure of requirement dependency extraction can be achieved by using the same number of samples.Finally,the proposed method using the PV-DM dependency feature improves the weight-F1 by 2.71%,the weight-recall by 2.45%,and the weight-precision by 2.64%in comparison with other strategies,saving approximately 46%of the labelled data compared with the machine learning approach.

Strategies for bubble removal in electrochemical systems

Bubbles are known to affect energy and mass transfer in gas-evolving electrodes,including those in water splitting,chlorine generation,direct methanol fuel cells,and carbon dioxide generation.As bubbles vigorously evolve in electrochemical reactions,undesired blockage of active sites and ion conducting pathways result in serious energy losses.Since new advances are made with the development of new theories,materials,and techniques,this review discusses the recent works on promoting bubble removal in electrochemical systems with the aim of guiding and motivating future research in this area.We first provide the mechanism of bubble evolution in electrochemical systems and the resultant overpotentials in detail.Then,recent advances in mitigating bubble issues are presented from the perspectives of passive and active strategies.Passive strategies act on the macro-and micro-structures of the electrode,surface wettability,and electrolyte properties.Active strategies employ out-fields,including flowing electrolytes,acoustic fields,magnetic forces,and photothermal effects,to guide bubbles out of reaction sites aiming at high reaction rates,whereas external energy is needed.Finally,the pros and cons of both strategies and future outlooks are presented.This review leads to design guidelines for highperformance gas-evolving electrochemical systems.

Ir-Catalyzed Asymmetric Hydrogenation of Unprotected Indoles:Scope Investigations and Mechanistic Studies

Transition metal-catalyzed asymmetric hydrogenation(AH)of unprotected indoles has mainly been applied to alkyl substituted unprotected indoles.However,the challenging aryl substituted unprotected indoles with poor reactivity and enantioselectivity(≤42%ee)could not be hydrogenated well.In this work,a highly efficient Ir/bisphosphine-thiourea ligand ZhaoPhos catalytic system for the AH of challenging aryl substituted unprotected indoles has been successfully developed for the first time with high reactivity and excellent stereoselective control.Moreover,a series of 2-alkyl-substituted and 2,3-disubstituted unprotected indoles were also well tolerated in this catalytic system.A wide variety of chiral indoline derivatives were obtained in good to high yields with excellent stereoselectivities(75–99%yields,>20:1 dr,and 86–99%ee).The anion-binding activation strategy played an important role in accessing both high reactivity and excellent stereoselectivity,which was formed between the catalyst and unprotected indoles in situ-generating iminium ion with the assistance of Brønsted acid.A possible catalytic mechanism was proposed for this Ir-catalyzed AH according to density functional theory calculations and control experiment results.Readily available substrates,a broad range of substrate tolerance,an efficient chiral catalytic system,and a gram-scale protocol further demonstrated the potential practicality of this methodology.

Recent advances in film cooling enhancement:A review

Film cooling is an indispensable scheme in the design of highly-efficient cooling configurations to satisfy the thermal protection requirement of turbine hot section components.During the last few decades,vast efforts have been paid on the discrete-hole film cooling enhancement.In this paper,some of the recent literatures related to the passive strategies(such as shaped film cooling holes,upstream ramps,shallow trenches,mesh-fed slots)and the active strategies(such as the use of pulsation modulating device or plasma actuator)for film cooling enhancement are surveyed,with the aim at presenting an updated overview about the state of the art in advanced film cooling.In addition,some challenging issues are also outlined to motivate further investigations in such a broad topic.

Active Power Correction Strategies Based on Deep Reinforcement Learning Part II:A Distributed Solution for Adaptability

This article is the second part of Active Power Correction Strategies Based on Deep Reinforcement Learning.In Part II,we consider the renewable energy scenarios plugged into the large-scale power grid and provide an adaptive algorithmic implementation to maintain power grid stability.Based on the robustness method in Part I,a distributed deep reinforcement learning method is proposed to overcome the infuence of the increasing renewable energy penetration.A multi-agent system is implemented in multiple control areas of the power system,which conducts a fully cooperative stochastic game.Based on the Monte Carlo tree search mentioned in Part I,we select practical actions in each sub-control area to search the Nash equilibrium of the game.Based on the QMIX method,a structure of offine centralized training and online distributed execution is proposed to employ better practical actions in the active power correction control.Our proposed method is evaluated in the modified global competition scenario cases of“2020 Learning to Run a Power Network.Neurips Track 2”.