Exploring efficient materials for capturing radioactive iodine in nuclear waste is of great significance for the progress of nuclear energy as well as the protection of ecological *** organic frameworks(COFs)have emer...
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Exploring efficient materials for capturing radioactive iodine in nuclear waste is of great significance for the progress of nuclear energy as well as the protection of ecological *** organic frameworks(COFs)have emerged as promising adsorbents because of their predesignable and functionalizable skeleton ***,it remains a grand challenge to achieve large scale preparation of *** this work,we developed a mild and efficient microwave irradiation method instead of the traditional solvothermal method to prepare copper phthalocyanine-based covalent organic frameworks(Cu_(x)Pc-COFs)within only 15 *** nitrogen-rich 1,2,4,5-tetracarbonitrilebenzene(TCNB)was selected as the solely organic ligand to construct copper phthalocyanine-based 2D conjugated *** resultant Cu_(x)Pc-COFs exhibited excellent iodine enrichment with 2.99 g/g for volatile iodine and 492.27 mg/g for iodine-cyclohexane solution,respectively,outperforming that of many porous *** indicated by spectroscopic analysis and DFT calculations,this impressive adsorption performance can be attributed to the charge transfer arising from nitrogen-rich phthalocyanine structures and electron-richπ-conjugated systems with iodine ***,the strong electrostatic interaction between Cu(Ⅱ)on chelate centers and polyiodide anions(I_(x)^(-))also play an important role in the firmly trapping radioactive ***,this study provides a facile and intelligent approach to implement metal-based COFs for the remediation of toxic radioactive iodine.
Decision-making architectures in the process industries require scheduling problems to explicitly account for control considerations in their optimisation. The literature proposes two traditional ways to solve this in...
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Decision-making architectures in the process industries require scheduling problems to explicitly account for control considerations in their optimisation. The literature proposes two traditional ways to solve this integrated problem: hierarchical and monolithic approaches. The monolithic approach ignores the control level's objective and incorporates it as a constraint into the upper level at the cost of suboptimality. The hierarchical approach requires solving a mathematically complex bilevel problem with the scheduling acting as the leader/upper-level and control as the follower/lower level. The linking variables between both levels belong to a small subset of scheduling and control decision variables. For this subset of variables, data-driven surrogate models have been used to learn follower responses to different leader decisions. In this work, we propose to use ReLU neural networks for the control level. Consequently, the bilevel problem is collapsed into a single-level MILP that is still able to account for the control level's objective. This single-level MILP reformulation is compared with the monolithic approach and benchmarked against embedding a nonlinear expression of the neural networks into the optimisation. Moreover, a neural network is used to predict control level feasibility. The case studies involve batch reactor and sequential batch process scheduling problems. The proposed methodology finds optimal solutions while largely outperforming both approaches in terms of computational time. Additionally, due to well-developed MILP solvers, adding ReLU neural networks in a MILP form marginally impacts the computational time. The solution's error due to prediction accuracy is correlated with the neural network training error and can lead to suboptimal solutions. Overall, we expose how - by using an existing big-M reformulation and being careful about integrating machine learning and optimisation pipelines - we can more efficiently solve the bilevel schedu
Environmental impacts of the extant linear carbon economy and aspects of conservation of resources demand a transformation to a circular carbon economy (CCE). In view of this transformation, carbon-containing plastic ...
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Predictive thermodynamic models are crucial for the early stages of product and process design. In this paper the performance of Graph Neural Networks (GNNs) embedded into a relatively simple excess Gibbs energy model...
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Transparent Ce:lutetium aluminum garnet(Ce:Lu_(3)A_(l5)O_(12),Ce:LuAG)ceramics have been regarded as potential scintillator materials due to their relatively high density and atomic number(Zeff).However,the current Ce...
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Transparent Ce:lutetium aluminum garnet(Ce:Lu_(3)A_(l5)O_(12),Ce:LuAG)ceramics have been regarded as potential scintillator materials due to their relatively high density and atomic number(Zeff).However,the current Ce:LuAG ceramics exhibit a light yield much lower than the expected theoretical value due to the inevitable presence of LuAl antisite defects at high sintering *** work demonstrates a low-temperature(1100℃)synthetic strategy for elaborating transparent LuAG–Al_(2)O_(3) nanoceramics through the crystallization of 72 mol%Al_(2)O_(3)–28 mol%Lu_(2)O_(3)(ALu28)bulk *** biphasic nanostructure composed of LuAG and Al_(2)O_(3) nanocrystals makes up the whole ceramic *** of Al_(2)O_(3) is distributed among LuAG grains,and the rest is present inside the LuAG *** dense biphasic LuAG–Al_(2)O_(3) nanoceramics are highly transparent from the visible region to mid-infrared(MIR)region,and particularly the transmittance reaches 82%at 780 ***,LuAl antisite defect-related centers are completely undetectable in X-ray excited luminescence(XEL)spectra of Ce:LuAG–Al_(2)O_(3) nanoceramics with 0.3–1.0 at%*** light yield of 0.3 at%Ce:LuAG–Al_(2)O_(3) nanoceramics is estimated to be 20,000 ph/MeV with short 1μs shaping time,which is far superior to that of commercial Bi_(4)Ge_(3)O_(12)(BGO)single *** results show that a low-temperature glass crystallization route provides an alternative approach for eliminating the antisite defects in LuAG-based ceramics,and is promising to produce garnet-based ceramic materials with excellent properties,thereby meeting the demands of advanced scintillation applications.
Thermal variation is assumed to be one of the main source of uncertainty in dimensional measurements and must be controlled in order to analyse and quantify its possible effects. Therefore, as the analysis of temperat...
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Fully resolved numerical simulations of a micron-sized spherical particle residing on a surface with large-scale roughness are performed by using the Lattice-Boltzmann *** aim is to investigate the influence of surfac...
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Fully resolved numerical simulations of a micron-sized spherical particle residing on a surface with large-scale roughness are performed by using the Lattice-Boltzmann *** aim is to investigate the influence of surface roughness on the detachment of fine drug particles from larger carrier particles for transporting fine drug particles in a DPI(dry powder inhaler).Often the carrier surface is modified by mechanical treatments for modifying the surface roughness in order to reduce the adhesion force of drug ***,drug particle removal from the carrier surface is equivalent to the detachment of a sphere from a rough plane *** a sphere with a diameter of 5μm at a particle Reynolds number of 1.0,3.5 and 10 are *** surface roughness is described as regularly spaced semi-cylindrical asperities(with the axes oriented normal to the flow direction)on a smooth *** influence of asperity distance and size ratio(*** radius of the semi-cylinder to the particle radius,Rc/Rd)on particle adhesion and detachment are *** asperity distance is varied in the range 1.2
Small-molecule drugs are essential for maintaining human health. The objective of this study is to identify a molecule that can inhibit the Factor Xa protein and be easily procured. An optimization-based de novo drug ...
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Small-molecule drugs are essential for maintaining human health. The objective of this study is to identify a molecule that can inhibit the Factor Xa protein and be easily procured. An optimization-based de novo drug design framework, Drug CAMD, that integrates a deep learning model with a mixed-integer nonlinear programming model is used for designing drug candidates. Within this framework, a virtual chemical library is specifically tailored to inhibit Factor Xa. To further filter and narrow down the lead compounds from the designed compounds, comprehensive approaches involving molecular docking,binding pose metadynamics(BPMD), binding free energy calculations, and enzyme activity inhibition analysis are utilized. To maximize efficiency in terms of time and resources, molecules for in vitro activity testing are initially selected from commercially available portions of customized virtual chemical libraries. In vitro studies assessing inhibitor activities have confirmed that the compound EN300-331859shows potential Factor Xa inhibition, with an IC_(50)value of 34.57 μmol·L^(-1). Through in silico molecular docking and BPMD, the most plausible binding pose for the EN300-331859-Factor Xa complex are identified. The estimated binding free energy values correlate well with the results obtained from biological assays. Consequently, EN300-331859 is identified as a novel and effective sub-micromolar inhibitor of Factor Xa.
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