Selective control of secondary alcohols upgrading using Ir-catalyzed cross-coupling strategy

The selective coupling of alcohols is a fascinating yet challenging approach for upgrading alcohols. Herein, we accomplished the controlled production of β-disubstituted ketones or upgraded secondary alcohols via the Ir-catalyzed cross-coupling of secondary alcohols in excellent yields with broad substrate scopes. This selective control was achieved by using an in-situ generated monoNHC-Ir or a tris-NHC-Ir complex as the catalysts, respectively. Mechanistic studies revealed that the delicate balance between dehydrogenation and hydrogenation abilities of these bifunctional catalysts is crucial for achieving different selectivity. The tris-NHC-Ir complex effectively facilitated dehydrogenation of alcohols and hydrogenation of intermediates, leading to the desired upgraded secondary alcohols. Conversely, the high dehydrogenation ability of the mono-NHC-Ir complex promoted the conversion of formed secondary alcohols back to ketones.

Controlling Reactivity of Palladium Amides for Selective Carbonylation towards Urea and Oxamide Derivatives

Carbonylation reactions,crucial for carbonyl group incorporation,struggle with the inherent complexity of achieving selective mono-or double-carbonylation on single substrates,often due to competing reaction pathways.Herein,our study introduces a strategy employing palladium amides,harnessing their unique reactivity control,to direct the selective carbonylation of amines for the targeted synthesis of urea and oxamide derivatives.The palladium amide structure was elucidated using single-crystal X-ray diffraction.Controlled experiments and cyclic voltammetry studies further elucidate that the oxidation of palladium amide or its insertion into a carbonyl group diverges into distinct pathways.By employing sodium percarbonate as an eco-friendly oxidant and base,we have successfully constructed a switchable carbonylation system co-catalyzed by palladium and iodide under room temperature.The utilizing strategy in this study not only facilitates effective control over reaction selectivity but also mitigates the risk of explosions,a critical safety concern in traditional carbonylation methods.

Control of reactive dividing wall column for selective hydrogenation and separation of C3 stream

In our previous work, the reactive dividing wall column(RDWC) was proposed and proved to be effective for selective hydrogenation and separation of C3 stream. In the present paper, the dynamics and control of the proposed RDWC are investigated. Four control structures including composition and temperature controls are proposed. The feed forward controllers are employed in the four control strategies to shorten the dynamic response time, reduce the maximum deviations and offer an immediate adjustment. The control structures are compared by applying them into the RDWC system with 20% disturbances in both the feed flow rate and the feed compositions, and the results are discussed.

Developing selective mining capability for longwall shearers using thermal infrared-based seam tracking

Longwall mining continues to remain the most efficient method for underground coal recovery. A key aspect in achieving safe and productive longwall mining is to ensure that the shearer is always correctly positioned within the coal seam. At present, this machine positioning task is the role of longwall personnel who must simultaneously monitor the longwall coal face and the shearer's cutting drum position to infer the geological trends of the coal seam. This is a labour intensive task which has negative impacts on the consistency and quality of coal production. As a solution to this problem, this paper presents a sensing method to automatically track geological coal seam features on the longwall face, known as marker bands, using thermal infrared imaging. These non-visible marker bands are geological features that link strongly to the horizontal trends present in layered coal seams. Tracking these line-like features allows the generation of a vertical datum that can be used to maintain the shearer in a position for optimal coal extraction. Details on the theory of thermal infrared imaging are given, as well as practical aspects associated with machine-based implementation underground. The feature detection and tracking tasks are given with real measurements to demonstrate the efficacy of the approach. The outcome is important as it represents a new selective mining capability to help address a long-standing limitation in longwall mining operations.

Type selection and maintenance of the control system of large-scale air separation units

Based on the practice of Baosteel＇ s 60000 m3/h air separation unit （ASU） ,which is the first domestically- integrated unit of such a scale, this paper studies the principles of type selection of the distribution control system （DCS）. It discusses the design of the unit＇s control system,which involves a compressor system,a purification system （molecular sieving）, a turbo expansion system and an air separation system. The final part of the paper discusses the maintenance and future development of the ASU control system at Baosteel.

Tuning the selectivity of natural oils and fatty acids/esters deoxygenation to biofuels and fatty alcohols:A review

The chemical transformation of natural oils provides alternatives to limited fossil fuels and produces compounds with added value for the chemical industries.The selective deoxygenation of natural oils to diesel-ranged hydrocarbons,bio-jet fuels,or fatty alcohols with controllable selectivity is especially attractive in natural oil feedstock biorefineries.This review presents recent progress in catalytic deoxygenation of natural oils or related model compounds(e.g.,fatty acids)to renewable liquid fuels(green diesel and bio-jet fuels)and valuable fatty alcohols(unsaturated and saturated fatty alcohols).Besides,it discusses and compares the existing and potential strategies to control the product selectivity over heterogeneous catalysts.Most research conducted and reviewed has only addressed the production of one category;therefore,a new integrative vision exploring how to direct the process toward fuel and/or chemicals is urgently needed.Thus,work conducted to date addressing the development of new catalysts and studying the influence of the reaction parameters(e.g.,temperature,time and hydrogen pressure)is summarized and critically discussed from a green and sustainable perspective using efficiency indicators(e.g.,yields,selectivity,turnover frequencies and catalysts lifetime).Special attention has been given to the chemical transformations occurring to identify key descriptors to tune the selectivity toward target products by manipulating the reaction conditions and the structures of the catalysts.Finally,the challenges and future research goals to develop novel and holistic natural oil biorefineries are proposed.As a result,this critical review provides the readership with appropriate information to selectively control the transformation of natural oils into either biofuels and/or value-added chemicals.This new flexible vision can help pave the wave to suit the present and future market needs.

Ru particle size effect in Ru/CNT-catalyzed Fischer-Tropsch synthesis

Carbon nanotube （CNT）-supported Ru nanoparticles with mean sizes ranging from 2.3 to 9.2 nm were prepared by different post-treatments and studied for Fischer-Tropsch （FT） synthesis. The effects of Ru particle size on catalytic behaviors were investigated at both shorter and longer contact times. At shorter contact time, where the secondary reactions were insignificant, the turnover frequency （TOF） for CO conversion was dependent on the mean size of Ru particles; TOF increased with the mean size of Ru particles from 2.3 to 6.3 nm and then decreased slightly. At the same time, the selectivities to C5＋ hydrocarbons increased gradually with the mean size of Ru particles up to 6.3 nm and then kept almost unchanged with a further increase in Ru particle size. At longer contact time, C10-C20 selectivity increased significantly at the expense of C21＋ selectivity, suggesting the occurrence of the selective hydrocracking of C21＋ to C10-C20 hydrocarbons.

Water promoted photocatalytic transfer hydrogenation of furfural to furfural alcohol over ultralow loading metal supported on TiO_(2)

Photocatalytic hydrogenation of furfural offers an ideal method for selective biomass upgrading into value-added chemicals or fuel additives under mild conditions. However, it is still challenging to control the product selectivity due to side reactions of functional groups and reactive radical intermediates.Herein, photocatalytic transfer hydrogenation of furfural was studied using the TiO_(2)-based photocatalysts with alcohols as both the solvent and hydrogen donor. Ultralow loading metal supported on TiO_(2),together with adding a small amount of water in the system, were demonstrated to greatly increase the selectivity of furfuryl alcohol product. Electron paramagnetic resonance(EPR), ultraviolet-visible spectroscopy(UV-Vis) and photoluminescence(PL) measurements gave evidence that ultralow loading Pt or Pd on TiO_(2)increase the oxygen vacancy concentration and the photogenerated charge separation efficiency, which accelerates the photocatalytic reduction of furfural. In situ diffuse reflectance infrared Fourier transform spectroscopy(DRIFTS) and mechanistic studies confirmed that photogenerated holes and electrons are active species, with dissociatively adsorbed methanol being directly oxidized by holes,furfural hydrogenated by protons and electrons and H_(2)O modifying the intermediate diffusion which contributes to high selectivity of furfuryl alcohol. This work demonstrates a simple approach to design photocatalysts and tune product selectivity in biomass valorization.

A Survivability Routing Mechanism in SDN Enabled Wireless Mesh Networks:Design and Evaluation

In software-defined networking,the separation of control plane from forwarding plane introduces new challenges to network reliability.This paper proposes a fault-tolerant routing mechanism to improve survivability by converting the survivability problem into two sub-problems:constructing an elastic-aware routing tree and controller selection.Based on the shortest path tree,this scheme continuously attempts to prune the routing tree to enhance network survivability.After a certain number of iterations,elastic-aware routing continues to improve network resiliency by increasing the number of edges in this tree.Simulation results demonstrate this fault-tolerant mechanism performs better than the traditional method in terms of the number of protected nodes and network fragility indicator.

Polarization-dependent photoinduced metal–insulator transitions in manganites

In correlated oxides,collaborative manipulation on light intensity,wavelength,pulse duration and polarization has yielded many exotic discoveries,such as phase transitions and novel quantum states.In view of potential optoelectronic applications,tailoring long-lived static properties by light-induced effects is highly desirable.So far,the polarization state of light has rarely been reported as a control parameter for this purpose.Here,we report polarization-dependent metal-to-insulator transition(MIT)in phaseseparated manganite thin films,introducing a new degree of freedom to control static MIT.Specifically,we observed giant photoinduced resistance jumps with striking features:(1)a single resistance jump occurs upon a linearly polarized light incident with a chosen polarization angle,and a second resistance jump occurs when the polarization angle changes;(2)the amplitude of the second resistance jump depends sensitively on the actual change of the polarization angles.Linear transmittance measurements reveal that the origin of the above phenomena is closely related to the coexistence of anisotropic micro-domains.Our results represent a first step to utilize light polarization as an active knob to manipulate static phase transitions,pointing towards new pathways for nonvolatile optoelectronic devices and sensors.

Control of selectivity in organic synthesis via heterogeneous photocatalysis under visible light

Organic synthesis driven by heterogeneous catalysis is a central research theme to both fundamental research and industrial production of fine chemicals.However,the employment of stoichiometric strong oxidizing or reducing reagents(e.g.,K_(2)Cr_(2)O_(7) and LiAlH_(4))and harsh reaction conditions(e.g.,high temperature and pressure)always leads to the products of overreaction and other by-product residues(e.g.,salt and acid waste).Thus the poor control of product selectivity and tremendous energy consumption result in the urgent demand to develop novel technologies for heterogeneous catalysis.Given the current global theme of development in CO_(2) reduction and sustainable energy utilization,one promising protocol is heterogeneous photocatalysis.It enables sustainable solar-to-chemical energy conversion under mild conditions(e.g.,room temperature,ambient pressure,and air as the oxidant)and offers unique reaction pathways for improved selectivity control.To accurately tailor the selectivity of desired products,the electronic structure(e.g.,positions of valence-band maximum and conduction-band minimum),geometric structure(e.g.,nanorod,nanosheet,and porous morphology),and surface chemical micro-environment(e.g.,vacancy sites and co-catalysts)of heterogeneous photocatalysts require rational design and construction.In this review,we will briefly analyze some effective photocatalytic systems with the excellent regulation ability of product selectivity in organic transformations(mainly oxidation and reduction types)under visible light irradiation,and put forward opinions on the optimal fabrication of nanostructured photocatalysts to realize selective organic synthesis.

Active Disturbance Rejection Control for PMLM Servo System in CNC Machining

Uncertain friction is a key factor that influences the accuracy of servo system in CNC machine.In this paper,based on the principle of Active Disturbance Rejection Control(ADRC),a control method is proposed,where both the extended state observer(ESO) and the reduced order extended state observer(RESO) are used to estimate and compensate for the disturbance.The authors prove that both approaches ensure high accuracy in theory,and give the criterion for parameters selection.The authors also prove that ADRC with RESO performs better than that with ESO both in disturbance estimation and tracking error.The simulation results on CNC machine show the effectiveness and feasibility of our control approaches.

Selection and Characterization for Corrosion Control

In a recent study by the National Association of Corrosion Engineers （NACE）, the global cost of corrosion was estimated to be US$ 2.5 trillion, equivalent to 3.4% of the global Gross Domestic Product [htto：//imtacact.nace.org/]. The prevention practices could save between study found that implementing the best corrosion 15%-35% of the cost of damage. One of the important measures to reduce the corrosion damage is using an appropriate material. Selection of a suitable material according to corrosivity of the service environment is essentially important in battle against industrial corrosion. Corrosion detection, damage characterization and surface analysis are critical approaches to fundamental understanding of the root cause and detailed mechanism of corrosion. They lay a foundation for prevention and mitigation of material corrosion in service environments. A technical breakthrough in these fields may result in significantly widened applications of traditional and emerging materials.

Measuring air traffic complexity based on small samples

Air traffic complexity is an objective metric for evaluating the operational condition of the airspace. It has several applications, such as airspace design and traffic flow management.Therefore, identifying a reliable method to accurately measure traffic complexity is important. Considering that many factors correlate with traffic complexity in complicated nonlinear ways,researchers have proposed several complexity evaluation methods based on machine learning models which were trained with large samples. However, the high cost of sample collection usually results in limited training set. In this paper, an ensemble learning model is proposed for measuring air traffic complexity within a sector based on small samples. To exploit the classification information within each factor, multiple diverse factor subsets（FSSs） are generated under guidance from factor noise and independence analysis. Then, a base complexity evaluator is built corresponding to each FSS. The final complexity evaluation result is obtained by integrating all results from the base evaluators. Experimental studies using real-world air traffic operation data demonstrate the advantages of our model for small-sample-based traffic complexity evaluation over other stateof-the-art methods.