Higher Spacetime Research Articles

Single-cell enzymatic cascade synthesis of testolactone enabled by engineering of polycyclic ketone monooxygenase and multi-gene expression fine-tuning

The synthesis of steroids is challenging through multistep steroidal core modifications with high site-selectivity and productivity. In this work, a novel enzymatic cascade system was constructed for synthesis of testolactone by specific C17 lactonization/Δ1-dehydrogenation from inexpensive androstenedione using an engineered polycyclic ketone monooxygenase (PockeMO) and an appropriate 3-ketosteroid-Δ1-dehydrogenase (ReKstD). The focused saturation mutagenesis in the substrate binding pocket was implemented for evolution of PockeMO to eliminate the bottleneck effect. A best mutant MU3 (I225L/L226V/L532Y) was obtained with 20-fold higher specific activity compared to PockeMO. The catalytic efficiency (kcat/Km) of MU3 was 171-fold higher and the substrate scope shifted to polycyclic ketones. Molecular dynamic simulations suggested that the activity was improved by stabilization of the pre-lactonization state and generation of productive orientation of 4-AD mediated by distal L532Y mutation. Based on that, the three genes, MU3, ReKstD and a ketoreductase for NADPH regeneration, were rationally integrated in one cell via expression fine-tuning to form the efficient single cell catalyst E. coli S9. The single whole-cell biocatalytic process was scaled up and could generate 9.0 g/L testolactone with the high space time yield of 1 g/L/h without steroidal by-product, indicating the potential for site-specific and one-pot synthesis of steroid.

High space–time bandwidth product imaging in low coherence quantitative phase microscopy

Current low coherence quantitative phase microscopy (LC-QPM) systems suffer from either reduced field of view (FoV) or reduced temporal resolution due to the short temporal coherence (TC) length of the light source. Here, we propose a hybrid, experimental and numerical approach to address this core problem associated with LC-QPM. We demonstrate high spatial resolution and high phase sensitivity in LC-QPM at high temporal resolution. High space–time bandwidth product is achieved by employing incoherent light source for sample illumination in QPM to increase the spatial resolution and single-shot Hilbert spiral transform (HST) based phase recovery algorithm to enhance the temporal resolution without sacrificing spatial resolution during the reconstruction steps. The high spatial phase sensitivity comes by default due to the use of incoherent light source in QPM which has low temporal coherence length and does not generate speckle noise and coherent noise. The spatial resolution achieved by the HST is slightly inferior to the temporal phase-shifting (TPS) method when tested on a specimen but surpasses that of the single-shot Fourier transform (FT) based phase recovery method. Contrary to HST method, FT method requires high density fringes for lossless phase recovery, which is difficult to achieve in LC-QPM over entire FoV. Consequently, integration of HST algorithm with LC-QPM system makes an attractive route. Here, we demonstrate scalable FoV and resolution in single-shot LC-QPM and experimentally corroborate it on a test object and on both live and fixed biological specimen such as MEF, U2OS and human red blood cells (RBCs). LC-QPM system with HST reconstruction offer high-speed single-shot QPM imaging at high phase sensitivity and high spatial resolution enabling us to study sub-cellular dynamic inside U2OS for extended duration (3 h) and observe high-speed (50 fps) dynamics of human RBCs. The experimental results validate the effectiveness of the present approach and will open new avenues in the domain of biomedical imaging in the future.

Estimating spatiotemporal pumping amounts using multiple signal decomposition methods

Effective water resource management requires accurate estimation of the volume of water withdrawn through anthropogenic groundwater pumping. However, insufficient pumping information makes accurate estimation difficult. To address this issue, this study proposes a novel method for estimating pumping volumes that does not rely on pumping information. The proposed method uses high-resolution groundwater level (GWL) data from densely monitored wells and integrates Hilbert–Huang transform and empirical orthogonal function analysis to extract pump-associated signals, which can then be used to estimate total pumping. Our method was validated by analyzing GWL data from monitoring stations on the Choshui River alluvial fan in Taiwan. The results indicate the estimated pumping amount of 1.71×109 – 2.05×109 m3/year consistent with previous estimates obtained using survey and physics-based methods ranging from 9×108 m3/year to 2.2×109 m3/year. In the areas with abundant groundwater head observations, the proposed method can efficiently estimate pumping rates with a high space–time resolution, and may serve as a useful reference for groundwater management.

Lignin carbon-initiated Ni/K/Mo2C catalyst for efficient synthesis of higher alcohols from syngas

Bamboo lignin, the principal constituent of non-wood pulping black liquor, is ecologically and environmentally harmful due to its complex molecular structure and stability. Additionally, the syngas-based synthesis of higher alcohols is a desired industrial application, alleviating the dependency on petroleum resources. However, developing highly efficient catalysts with high space–time yield and selectivity towards higher alcohols remains challenging. Herein, for the first time, taking into account the permeable structure of bamboo lignin in paper-making black liquid resulting from cellulose and hemicellulose extraction, porous nano molybdenum carbide was manufactured by designed pretreatment and activation processes of the lignin, which was further doped by the K and Ni additive to create a highly efficient catalyst (Ni/K/Mo2C@LC) for the high alcohols synthesis reaction, yielding a surprising space–time yield of 0.455 g/gcat/h) to alcohol products under the optimal reaction conditions. Control experiments and characterizations were conducted over relevant catalysts, revealing that Ni and K could be integrated to regulate the adsorption property, acidity/basicity, and carbon chain growth ability of the catalysts. Also, the open mesoporous lignin carbon provided larger metal particles, enhancing the hydrogenation ability and better balancing the non-dissociative and dissociative active sites for the high alcohol synthesis reaction. These results provide insights into the structure design of carbide catalysts toward efficiently utilizing industrial lignin and other biomass-derived feedstocks, and the development of an efficient Mo-based catalyst for syngas conversion.

CMEMS-LSCE: a global, 0.25°, monthly reconstruction of the surface ocean carbonate system

Abstract. Observation-based data reconstructions of global surface ocean carbonate system variables play an essential role in monitoring the recent status of ocean carbon uptake and ocean acidification, as well as their impacts on marine organisms and ecosystems. So far, ongoing efforts are directed towards exploring new approaches to describe the complete marine carbonate system and to better recover its fine-scale features. In this respect, our research activities within the Copernicus Marine Environment Monitoring Service (CMEMS) aim to develop a sustainable production chain of observation-derived global ocean carbonate system datasets at high space–time resolutions. As the start of the long-term objective, this study introduces a new global 0.25∘ monthly reconstruction, namely CMEMS-LSCE (Laboratoire des Sciences du Climat et de l'Environnement) for the period 1985–2021. The CMEMS-LSCE reconstruction derives datasets of six carbonate system variables, including surface ocean partial pressure of CO2 (pCO2), total alkalinity (AT), total dissolved inorganic carbon (CT), surface ocean pH, and saturation states with respect to aragonite (Ωar) and calcite (Ωca). Reconstructing pCO2 relies on an ensemble of neural network models mapping gridded observation-based data provided by the Surface Ocean CO2 ATlas (SOCAT). Surface ocean AT is estimated with a multiple-linear-regression approach, and the remaining carbonate variables are resolved by CO2 system speciation given the reconstructed pCO2 and AT; 1σ uncertainty associated with these estimates is also provided. Here, σ stands for either the ensemble standard deviation of pCO2 estimates or the total uncertainty for each of the five other variables propagated through the processing chain with input data uncertainty. We demonstrate that the 0.25∘ resolution pCO2 product outperforms a coarser spatial resolution (1∘) thanks to higher data coverage nearshore and a better description of horizontal and temporal variations in pCO2 across diverse ocean basins, particularly in the coastal–open-ocean continuum. Product qualification with observation-based data confirms reliable reconstructions with root-mean-square deviation from observations of less than 8 %, 4 %, and 1 % relative to the global mean of pCO2, AT (CT), and pH. The global average 1σ uncertainty is below 5 % and 8 % for pCO2 and Ωar (Ωca), 2 % for AT and CT, and 0.4 % for pH relative to their global mean values. Both model–observation misfit and model uncertainty indicate that coastal data reproduction still needs further improvement, wherein high temporal and horizontal gradients of carbonate variables and representative uncertainty from data sampling would be taken into account as a priority. This study also presents a potential use case of the CMEMS-LSCE carbonate data product in tracking the recent state of ocean acidification. The data associated with this study are available at https://doi.org/10.14768/a2f0891b-763a-49e9-af1b-78ed78b16982 (Chau et al., 2023).

Construction of covalent organic framework functionalized with carbene dual active sites for enhancing CO2 carboxylation conversion

It is challenging to drive the integral insertion of linear CO2 with thermodynamic stability and kinetic inertness as carboxylation reagents into C-H, O-H, and other chemical bonds. The known strategies mainly focus on unilaterally activating substrates as nucleophilic reagents, and the CO2 insertion process has to overcome high reaction energy barriers. Herein, this work reports copper carbene NHC-CuCl and CO2 adduct NHC-CO2 as dual active sites are introduced into the porous crystal covalent organic framework to effectively drive the simultaneous activation of substrate and CO2. The catalyst COF@NHC-CuCl/CO2 exhibited high space–time yield (STY = 71.9 h−1 at 40 °C and 36.7 h−1 at 25 °C, ambient pressure) in the carboxylative coupling of propargylic alcohols, which is superior to most of the cutting-edge heterogeneous catalysts. Experimental data and density functional theory calculation revealed that the carboxylation reaction involving NHC-CO2 and ordinary CO2 source follows different reaction mechanisms. The free energy barrier of the CO2 insertion process (TS:35.9 kcal/mol → 17.4 kcal/mol) is effectively reduced by NHC modulation of the CO2 bond energy parameters to facilitate the reaction. Due to the unique structural design, COF@NHC-CuCl/CO2 exhibited outstanding diffusion mass transfer, catalytic diversity and reusability. This work proposes a novel construction strategy of heterogeneous dual activation catalyst to provide a sustainable solution for efficiently producing value-added chemicals from CO2 under mild conditions.

Improving Precipitation Nowcasting for High-Intensity Events Using Deep Generative Models with Balanced Loss and Temperature Data: A Case Study in the Netherlands

Abstract Precipitation nowcasting is essential for weather-dependent decision-making, but it remains a challenging problem despite active research. The combination of radar data and deep learning methods has opened a new avenue for research. Radar data are well suited for precipitation nowcasting due to the high space–time resolution of the precipitation field. On the other hand, deep learning methods allow the exploitation of possible nonlinearities in the precipitation process. Thus far, deep learning approaches have demonstrated equal or better performance than optical flow methods for low-intensity precipitation, but nowcasting high-intensity events remains a challenge. In this study, we have built a deep generative model with various extensions to improve nowcasting of heavy precipitation intensities. Specifically, we consider different loss functions and how the incorporation of temperature data as an additional feature affects the model’s performance. Using radar data from KNMI and 5–90-min lead times, we demonstrate that the deep generative model with the proposed loss function and temperature feature outperforms other state-of-the-art models and benchmarks. Our model, with both loss function and feature extensions, is skillful at nowcasting precipitation for high rainfall intensities, up to 60-min lead time.

Dehydrogenation of cycloalkanes over Pt/SBA-15 for endothermic cooling

Dehydrogenation of cyclohexane, methylcyclohexane, and decalin was studied on Pt/SBA-15 between 773 and 873 K for application to engine cooling in hypersonic aircraft. While the reactions were unstable at 1 bar, the reactions showed no evidence for deactivation at pressures of 20 and 60 bar for at least 4 h. Selectivities for dehydrogenation of cyclohexane to benzene and methylcyclohexane to toluene were greater than 90% in all cases. The reaction of decalin proceeded sequentially, forming primarily tetralin at low reactor space times and naphthalene at higher space times. The reaction of 50% mixtures of n-dodecane and decalin at 773 K showed negligible conversion of n-dodecane but the reaction of decalin was similar to what was observed for a pure decalin feed. Heat measurements gave endothermicities that were in good agreement with standard reaction enthalpies.

Boosting CO2 hydrogenation of Fe-based monolithic catalysts via 3D printing technology-induced heat/mass-transfer enhancements

The direct transformation of CO2 into valuable chemicals with the aid of sustainable energy-generated H2 has attracted enormous interest owing to the integrated functions of carbon elimination and non-fossil fuel-derived products supply. As one of the most promising catalysts for CO2 hydrogenation, the chemical properties of Fe-based catalysts, such as electronic structure and coordination environment, have been widely studied. However, the mass/heat transfer effects in Fe-based catalysts are also crucial to the targeted product selectivity and catalytic stability, but have been rarely investigated due to the lack of facile fabrication protocols. Herein, we precisely fabricate and tailor the architecture of the Fe-based monolithic catalysts by a direct ink writing (DIW)-type three-dimensional (3D) printing technology under the guidance of the computationally controlled printing procedure. The Fe-based monolithic catalyst with spiral-type architecture delivers extremely high light olefins selectivity (52.6%) and space time yield (STY, 451.8 gCH2 kgcat−1 h−1) from CO2 hydrogenation. Based on the mass-transfer simulation, the spiral-structured channels in the Fe-based monolithic catalyst lower the coverage of intermediates and products on the catalytic interface due to the optimal mass-transfer effect, thus maximizing the utilization of active sites and timely terminating the carbon-chain growth. More in-depth, the density functional theoretical (DFT) simulations verify that the relatively electron-rich catalytic interface with low intermediate coverage could facilitate the desorption of olefins and decelerate the C-C coupling step, which synergistically guarantee the enhanced light olefins synthesis performance. Furthermore, the enhanced heat-transfer effect endowed by the 3D architecture prolongs the life-time of catalyst by avoiding undesirable active site aggregation and carbon deposition. The powerful strategy for catalyst fabrication not only provides a new concept of regulating the CO2 hydrogenation performance of Fe-based catalysts but also holds a great promise to spread into other catalytic systems for targeted synthesis.

Xylene Synthesis Through Tandem CO2 Hydrogenation and Toluene Methylation Over a Composite ZnZrO Zeolite Catalyst.

Selective synthesis of specific value-added aromatics from CO2 hydrogenation is of paramount interest for mitigating energy and climate problems caused by CO2 emission. Herein, we report a highly active composite catalyst of ZnZrO and HZSM-5 (ZZO/Z5-SG) for xylene synthesis from CO2 hydrogenation via a coupling reaction in the presence of toluene, achieving a xylene selectivity of 86.5 % with CO2 conversion of 10.5 %. A remarkably high space time yield of xylene could reach 215 mg gcat -1 h-1 , surpassing most reported catalysts for CO2 hydrogenation. The enhanced performance of ZZO/Z5-SG could be due to high dispersion and abundant oxygen vacancies of the ZZO component for CO2 adsorption, more feasible hydrogen activation and transfer due to the close interaction between the two components, and enhanced stability of the formate intermediate. The consumption of methoxy and methanol from the deep hydrogenation of formate by introduced toluene also propels an oriented conversion of CO2 .

Xylene Synthesis Through Tandem CO2 Hydrogenation and Toluene Methylation Over a Composite ZnZrO Zeolite Catalyst

AbstractSelective synthesis of specific value‐added aromatics from CO2 hydrogenation is of paramount interest for mitigating energy and climate problems caused by CO2 emission. Herein, we report a highly active composite catalyst of ZnZrO and HZSM‐5 (ZZO/Z5‐SG) for xylene synthesis from CO2 hydrogenation via a coupling reaction in the presence of toluene, achieving a xylene selectivity of 86.5 % with CO2 conversion of 10.5 %. A remarkably high space time yield of xylene could reach 215 mg gcat−1 h−1, surpassing most reported catalysts for CO2 hydrogenation. The enhanced performance of ZZO/Z5‐SG could be due to high dispersion and abundant oxygen vacancies of the ZZO component for CO2 adsorption, more feasible hydrogen activation and transfer due to the close interaction between the two components, and enhanced stability of the formate intermediate. The consumption of methoxy and methanol from the deep hydrogenation of formate by introduced toluene also propels an oriented conversion of CO2.

Combined hydrogenation of CO2 and CO to methanol using Aerosol-Assisted Metal-Organic Framework-Derived hybrid catalysts

In this study, a spray-drying approach is presented to develop (1) Cu-based metal–organic framework (Cu-MOF) supported on aluminum oxide nanoparticle cluster and (2) its derivedCu-ZnO/Al2O3 hybrid catalyst for combined (CO + CO2) hydrogenation to methanol. The results show superior high space time yield, 23.14 mmol gcat-1h−1, and selectivity to methanol (85%) were achievable under a moderate-pressure (30 bar) and relatively low-temperature (220 °C) operation. Incorporation of ZnO and Al2O3 (i.e., in the form of nanoparticle cluster) enhanced the dispersion and the redox ability of Cu in the MOF-derived catalyst, both of which were critical factors to the catalytic activity toward the combined hydrogenation of CO2 and CO to methanol. An optimal performance, in terms of the methanol yield, was achieved by using the catalyst with a Cu/Zn molar ratio of 2 and partially reduction by H2 at a relatively lower temperature (300 °C). The work demonstrates a new route for the design of MOF-derived catalyst by using an aerosol-assisted synthesis approach, showing promise for the catalysis of a variety of chemical reactions in the field of CO2 capture and utilization.

Defective UiO-66-NH2 monoliths for optimizing CO2 capture performance

Zirconium-based metal–organic frameworks (Zr-MOFs) have been considered promising sorbents for guest adsorption owing to their high porosity, structural and chemical versatility, and exceptional stability. Nevertheless, issues with the powder and the modest CO2 adsorption remain as major challenges for their use in industrial processes. Herein, a series of defective monolithic UiO-66-NH2 xerogels (G66-NH2-X) are prepared via adjusting the modulator concentration, featuring tunable porous structure, simple and scalable preparation process and high space–time yield. With a change in the modulator concentration, the missing linker deficiency varies from 0.57 to 1.80 per secondary building unit. The porosity and CO2 uptake capacity of the adsorbents are strongly affected by the defect concentration. At 25 ℃ and 1 bar, the defective G66-NH2-12 exhibits optimal CO2 adsorption capacity of ∼ 2.50 mmol/g and CO2/N2 ideal adsorbed solution theory selectivity of ∼ 33, exceeding those of many other CO2 benchmark adsorbents. Moreover, the G66-NH2-X adsorbents can be easily regenerated with obtained lower isosteric heat of adsorption (Qst) and show excellent sustainability of up to ∼ 99% even after five adsorption–desorption cycles. This provides a solid foundation for the development of defective granular G66-NH2-X adsorbents as future CO2 adsorption and separation media.

A versatile microfluidic strategy using air–liquid segmented flow for continuous and efficient synthesis of metal–organic frameworks

Microfluidic synthesis is one of the most state-of-the-art methods for preparing metal–organic frameworks (MOFs) attributed to its high mass/heat transfer efficiency and controllable reaction process. However, the post-processing of the products in most of the currently reported liquid–liquid microfluidic synthesis methods is complicated and can burden the environment due to the using of oil phase. Herein, we report a simple, efficient and versatile method for the preparation of MOFs through an air–liquid segmented flow reaction. Six classical MOFs (HKUST-1, Co-MOF-74, Cu-BDC, MOF-801, UiO-66 and MOF-808) with homogeneous morphologies and different topologies are successfully synthesized by the air–liquid segmented flow method. The prepared A-MOFs exhibited high crystallinity, high space–time yield (STY), homogeneous morphology, smaller particle size, higher surface area compared to the samples synthesized by the solvothermal method, which will benefit the practical applications of MOFs in many fields, such as gas adsorption, catalysis and so on.

Fast low-rank-matrix-based connection center evolution for large hyperspectral image clustering

Clustering is a common data processing technique and has been increasingly applied in remote sensing processing. In recent years, a novel clustering algorithm called connection center evolution (CCE) has been proposed. To a certain extent, CCE solves the parameter sensitivity problem, which generally exists in traditional clustering algorithms. However, CCE is not feasible for handling large hyperspectral images (HSI) due to its computational bottlenecks. That is, when the size of HSI to be processed is large, the construction of the similarity matrix and the calculation of its power have very high space complexity and time complexity, respectively. To solve this problem, we propose a novel approach for large HSI datasets, named fast low-rank-matrix-based connectivity center evolution (LMCCE). The proposed LMCCE algorithm first introduces the KNN graph into CCE to construct a sparse similarity matrix, effectively reducing its space complexity. Then, symmetric non-negative matrix factorization (SymNMF) is used for the similarity matrix, which can maintain most of the information while reducing the redundancy and noise in similarity matrix. Furthermore, by performing SymNMF on the similarity matrix, the problem of calculating the power of the similarity matrix is transformed into the problem of calculating the power of a small matrix, which significantly reduces the time complexity. Experimental results on several synthetic and real HSI datasets demonstrate that the proposed LMCCE algorithm is faster than CCE, and can handle large HSI while achieving superior performance compared to state-of-the-art clustering methods.

Maximizing the Interface of Dual Active Sites to Enhance Higher Oxygenate Synthesis from Syngas with High Activity

Selective synthesis of higher oxygenates from syngas provides a promising route for the conversion of nonpetroleum carbon resources into valuable chemicals. However, it remains a grand challenge to design highly efficient and stable dual-sites structures to promote the production of higher oxygenates. Herein, we reported an effective method to maximize the interface of dual active sites via designing the structure of alloy carbide derived from the FeCo layered double hydroxide precursor. Cobalt atoms were well-distributed and doped into Fe2C to form (FexCoy)2C alloy carbide. The atomic-scale contact Fe–Co interfacial sites could achieve a >35% oxygenate selectivity at a CO conversion of >80% during 200 h of running, and a high space–time yield of 183.9 mg/gcat./h for oxygenates with 95.6% being the C2+OH fraction was obtained. The kinetic study confirmed that the apparent activation energy of (FexCoy)2C alloy carbide was lower than that of separated Fe2C-Co2C dual sites. This work provides a strategy for the design of an effective catalyst for selective synthesis of higher oxygenates from syngas by tuning the interface of dual active sites at an atomic level.

Revealing and Regulating the Complex Reaction Mechanism of CO2 Hydrogenation to Higher Alcohols on Multifunctional Tandem Catalysts

Revealing and regulating the intricate reaction mechanism of direct CO2 hydrogenation to higher alcohols (C2+OH), especially for the crucial C–C coupling step, is still a great challenge. Herein, the specific reaction network on Co2C and CuZnAl multifunctional tandem catalysts is elucidated by designing subtly surface adsorption–desorption reactions, in situ chemical transient kinetics, and theory calculations. The key C–C coupling step for the formation of C2+OH over the sole Co2C catalyst was the insertion of CO into R-CHx, while the reaction mechanism can be modulated to the coupling of R-CH2 and CHO with a lower energy barrier on the tandem catalyst (Co2C||CuZnAl). R-CH2 was derived from the hydrogenation dissociation of olefins and coupled with the CHO from formate hydrogenation at the Cu/ZnAl2O4 interface. Such multifunctional tandem catalysts exhibited a high space–time yield of C2+OH of 2.2 mmol g–1 h–1. This work provides an effective strategy for studying complex mechanisms, contributing to the precise design of highly efficient catalysts and the optimization of reaction pathways.

Visible-light-driven direct decarboxylative carbonylation of carboxylic acids using acridine photocatalysis in oxygen-liquid flow

Photocatalytic direct decarboxylative carbonylation of ubiquitous carboxylic acids is a promising strategy to obtain highly versatile ketone building blocks, but metal-free and efficient conversions are still lacking. Here, density functional theory calculations are used to guide the experimental screening of catalysts, highlighting the potential influence of frontier molecular orbital energy levels on the catalytic process and revealing an acridine photocatalyst capable of realizing value-added conversion of carboxylic acid at room temperature under visible light with excellent performance. The possible mechanism for this conversion was elucidated in depth by combining study of the excited state behavior of the photosensitizer and the results of detection experiments for radicals. Theoretical calculations and mechanistic studies further disclose the kinetically rate-limiting role of oxygen, including its participation in the formation of superoxide radicals for carbonylation and as an electron trapping agent for catalyst cycling. Based on these findings, a flow microreactor platform was ingeniously built to achieve previously inaccessible photocatalytic decarboxylative carbonylation promoted by acridine in concert with oxygen. Interestingly, the gas–liquid segmented microflow is found to enable a significant acceleration (from 14 to 2 h) and higher yield compared to the batch counterpart, resulting in a high space–time yield of 137.4 g/L d-1 for this transformation, which far exceeds the reported values. This study underscores the feasibility of broadening the competence of acridine photocatalysis through the involvement of kinetically adapted oxygen, thus enabling substitution of metal-dependent photocatalysis and even more efficient and high-yield decarboxylative carbonylation of carboxylic acids.

Degradation of Agro-Industrial Wastewater Model Compound by UV-A-Fenton Process: Batch vs. Continuous Mode.

The degradation of a model agro-industrial wastewater phenolic compound (caffeic acid, CA) by a UV-A-Fenton system was investigated in this work. Experiments were carried out in order to compare batch and continuous mode. Initially, batch experiments showed that UV-A-Fenton at pH 3.0 (pH of CA solution) achieved a higher generation of HO•, leading to high CA degradation (>99.5%). The influence of different operational conditions, such as H2O2 and Fe2+ concentrations, were evaluated. The results fit a pseudo first-order (PFO) kinetic model, and a high kinetic rate of CA removal was observed, with a [CA] = 5.5 × 10-4 mol/L, [H2O2] = 2.2 × 10-3 mol/L and [Fe2+] = 1.1 × 10-4 mol/L (kCA = 0.694 min-1), with an electric energy per order (EEO) of 7.23 kWh m-3 order-1. Under the same operational conditions, experiments in continuous mode were performed under different flow rates. The results showed that CA achieved a steady state with higher space-times (θ = 0.04) in comparison to dissolved organic carbon (DOC) removal (θ = 0-0.020). The results showed that by increasing the flow rate (F) from 1 to 4 mL min-1, the CA and DOC removal rate increased significantly (kCA = 0.468 min-1; kDOC = 0.00896 min-1). It is concluded that continuous modes are advantageous systems that can be adapted to wastewater treatment plants for the treatment of real agro-industrial wastewaters.

Vapor-assisted synthesis of the MOF-74 metal-organic framework family from zinc, cobalt, and magnesium oxides.

In this work, we investigate the vapor-assisted synthesis of the metal-organic framework MOF-74 starting from three metal oxides (ZnO, CoO, and MgO). Depending on the nature of the added vapor (H2O, DMF, DMSO), the metal oxide, and the temperature, the outcome of the reaction can be directed towards the desired porous phase. Ex situ and in situ XRD measurements reveal the formation of an intermediate phase during the reaction of MgO with H4dobdc, while the MOF-74 phase forms directly for ZnO and CoO. The reduced CO2 uptake of the resulting materials compared to solvothermally prepared MOFs might be offset by the convenience of the presented route and the promise of a high space time yield.