Mesoporous α-Al2O3-supported PdCu bimetallic nanoparticle catalyst for the selective semi-hydrogenation of alkynes.

The selective hydrogenation of alkynes to alkenes is widely applied in the chemical industry; nevertheless, achieving highly selective hydrogenation with high catalytic activity is considerably challenging. Herein, ultrafine PdCu bimetallic nanoparticles encapsulated by high-surface-area mesoporous α-Al2O3 were prepared by high-temperature calcination-reduction using a porous organic framework (POF) as the template. As-obtained PdCu@α-Al2O3 exhibited a high selectivity of 95% for the semi-hydrogenation of phenylacetylene as a probe reaction under mild reaction conditions. The separation of continuous Pd atoms and modification of the Pd electronic state by Cu atoms suppressed ß-hydride formation and alkene adsorption, contributing to high selectivity for the catalytic hydrogenation of alkynes. The catalytic activity was maintained after 7 cycles due to the strong interaction between the PdCu bimetallic nanoparticles and α-Al2O3 as well as the encapsulation effect of mesoporous α-Al2O3. Thus, the current work provides a facile strategy for fabricating high-surface-area mesoporous α-Al2O3-supported catalysts for industrial catalysis applications.

Systematic investigation of simultaneous copper biosorption and nitrogen removal from wastewater by an aerobic denitrifying bacterium of auto-aggregation.

Finding effective methods for simultaneous removal of eutrophic nutrients and heavy metals has attracted increasing concerns for the environmental remediation. Herein, a novel auto-aggregating aerobic denitrifying strain (Aeromonas veronii YL-41) was isolated with capacities for copper tolerance and biosorption. The denitrification efficiency and nitrogen removal pathway of the strain were investigated by nitrogen balance analysis and amplification of key denitrification functional genes. Moreover, the changes in the auto-aggregation properties of the strain caused by extracellular polymeric substances (EPS) production were focused on. The biosorption capacity and mechanisms of copper tolerance during denitrification were further explored by measuring changes in copper tolerance and adsorption indices, as well as by variations in extracellular functional groups. The strain showed extremely strong total nitrogen removal ability, with 67.5%, 82.08% and 78.48% of total nitrogen removal when NH4+-N, NO2--N, and NO3--N were used as the only initial nitrogen source, respectively. The successful amplification of napA, nirK, norR, and nosZ genes further demonstrated that the strain accomplished nitrate removal through a complete aerobic denitrification pathway. The production of protein-rich EPS of up to 23.31 mg/g and an auto-aggregation index of up to 76.42% may confer a strong biofilm-forming potential to the strain. Under the stress of 20 mg/L copper ions, the removal of nitrate-nitrogen was still as high as 71.4%. In addition, the strain could achieve an efficient removal of 96.9% of copper ions at an initial concentration of 80 mg/L. Scanning electron microscopy and deconvolution analysis of characteristic peaks confirmed that the strains encapsulate heavy metals by secreting EPS and, meanwhile, form strong hydrogen bonding structures to enhance intermolecular forces to resist copper ion stress. This study provides an innovative and effective biological approach for the synergistic bioaugmentation removal of eutrophic substances and heavy metals from aquatic environments.

Acceleration of the semi-hydrogenation of alkynes over an N-doped porous carbon sphere-confined ultrafine PdCu bimetallic nanoparticle catalyst.

Selective hydrogenation of alkynes to obtain alkenes is a key reaction in petrochemical and fine chemical industries. However, the development of stable and highly selective catalysts with uniformly dispersed active sites is still immensely challenging for the semi-hydrogenation of alkynes. In this study, N-doped porous carbon nanospheres (NPCNs) were synthesized by the nanoemulsion self-assembly and subsequently carbonization method. Ultrafine PdCu bimetallic nanoparticles (NPs) were uniformly dispersed and immobilized on NPCNs. The obtained PdCu/NPCNs catalyst exhibited an open framework and abundant active sites originating from ultrafine PdCu NPs. In the semi-hydrogenation of alkynes, the PdCu/NPCNs catalyst exhibited a remarkable performance and stability, outperforming most of the classical catalysts. The excellent performance was related to the introduction of a secondary metal Cu, which can regulate the electronic state of Pd active sites to further enhance the hydrogenation activity and selectivity. Hence, the facile approach reported herein may be useful for constructing highly dispersed bimetallic NP-based catalysts for selective hydrogenation of alkynes in the petrochemical industry.

Ultrafine PdCo bimetallic nanoclusters confined in N-doped porous carbon for the efficient semi-hydrogenation of alkynes.

Semi-hydrogenation of alkynes to prepare alkenes is an important reaction in the petrochemical and fine chemical industries. The use of conventional Pd nanoparticle-based catalysts is limited by alkyne over-hydrogenation and low Pd utilization. In this study, a nitrogen-doped mesoporous carbon material (m-NC), which was rich in defect sites after Zn volatilization, was fabricated by the carbonization of ZIF-8. Ultrafine PdCo bimetallic nanoclusters with Co atom-modified Pd active site electronic and compositional structure were highly dispersed and confined in m-NC. As-obtained Pd0.43Co1/m-NC was used for the semi-hydrogenation of alkynes and it exhibited high selectivity with high conversion under mild reaction conditions. Pd0.43Co1/m-NC also exhibited excellent stability in leaching tests and maintained its catalytic activity for at least nine reaction cycles. The highly dispersed active sites in Pd0.43Co1/m-NC served as the active sites for the catalytic semi-hydrogenation of alkynes; as a regulator, the second metal Co effectively improved selectivity, and m-NC endowed the catalyst with excellent stability. The research work presented here may provide a foundation for the design of highly active, selective, and stable Pd-based bimetallic catalysts for selective hydrogenation.

Development and Validation of a Deep Learning Algorithm to Automatic Detection of Pituitary Microadenoma From MRI.

Background: It is often difficult to diagnose pituitary microadenoma (PM) by MRI alone, due to its relatively small size, variable anatomical structure, complex clinical symptoms, and signs among individuals. We develop and validate a deep learning -based system to diagnose PM from MRI. Methods: A total of 11,935 infertility participants were initially recruited for this project. After applying the exclusion criteria, 1,520 participants (556 PM patients and 964 controls subjects) were included for further stratified into 3 non-overlapping cohorts. The data used for the training set were derived from a retrospective study, and in the validation dataset, prospective temporal and geographical validation set were adopted. A total of 780 participants were used for training, 195 participants for testing, and 545 participants were used to validate the diagnosis performance. The PM-computer-aided diagnosis (PM-CAD) system consists of two parts: pituitary region detection and PM diagnosis. The diagnosis performance of the PM-CAD system was measured using the receiver operating characteristics (ROC) curve and area under the ROC curve (AUC), calibration curve, accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and F1-score. Results: Pituitary microadenoma-computer-aided diagnosis system showed 94.36% diagnostic accuracy and 98.13% AUC score in the testing dataset. We confirm the robustness and generalization of our PM-CAD system, the diagnostic accuracy in the internal dataset was 96.50% and in the external dataset was 92.26 and 92.36%, the AUC was 95.5, 94.7, and 93.7%, respectively. In human-computer competition, the diagnosis performance of our PM-CAD system was comparable to radiologists with >10 years of professional expertise (diagnosis accuracy of 94.0% vs. 95.0%, AUC of 95.6% vs. 95.0%). For the misdiagnosis cases from radiologists, our system showed a 100% accurate diagnosis. A browser-based software was designed to assist the PM diagnosis. Conclusions: This is the first report showing that the PM-CAD system is a viable tool for detecting PM. Our results suggest that the PM-CAD system is applicable to radiology departments, especially in primary health care institutions.

Efficacy of inorganic nitrogen removal by a salt-tolerant aerobic denitrifying bacterium, Pseudomonas sihuiensis LK-618.

An aerobic denitrifying bacterium, stain LK-618, was isolated from lake sediment surface and the efficacy of inorganic nitrogen removal was tested. Stain LK-618 identified as Pseudomonas sihuiensis by 16S rRNA sequencing analysis. Trisodium citrate was found to be the ideal carbon source for this strain. When an initial nitrogen sources of approximately 50 mg/L nitrate, ammonium, or nitrite was solely selected as the nitrogen source, the nitrogen removal efficiencies were 91.4% (3.86 mg/L/h), 95.07% (2.47 mg/L/h) and 97.7% (2.41 mg/L/h), respectively. Nitrogen balance analysis revealed that 55.12% NO3--N was removed as N2. Response surface methodology (RSM) analysis demonstrated that the optimal Total Nitrogen (TN) removal ratio for strain LK-618 was under C/N ratio of 12.63, shaking speed of 52.06 rpm, temperature of 28.5 °C and pH of 6.86. In addition, strain LK-618 could tolerate NaCl concentrations up to 20 g/L, and its most efficient denitrification capacity was presented at NaCl concentrations of 0-10 g/L. Therefore, strain LK-618 has potential application on the removal of inorganic nitrogen from saline wastewater under aerobic conditions.

Persicariajucunda var. rotunda (Polygonaceae, Persicarieae), a distinct distylous taxa raised to specific rank.

Persicariajucunda(Meisn.)Migovar.rotunda (Z.Z.Zhou & Q.Y.Sun) Bo Li was originally published in the genus Polygonum L. and treated as a variety of P.rotundum Meisn. [≡Persicariajucunda (Meisn.) Migo]. After carefully comparing the macro- and micro-morphological characteristics of the achenes, leaf epidermis and tepals and the habitat between the variety and its typical variety, we confirmed that P.jucundavar.rotunda is clearly different from P.jucunda and should not be treated as a variety, but be raised to a specific rank as P.rotunda (Z.Z.Zhou & Q.Y.Sun) Bo Li. The species is distylous and could be easily distinguished from all other Persicaria taxa by a combination of morphological characters, such as completely decumbent leafless basal branches, almost sessile leaves, linear-lanceolate with rounded leaf bases, spicate, short and dense inflorescences, slender pedicels longer than bracts and dimorphic flowers and achenes. P.rotunda is endemic to several large wetlands of eastern China and usually occurs as one of the dominant species in some plant communities.

Detecting and Monitoring the Flavor of Tomato (Solanum lycopersicum) under the Impact of Postharvest Handlings by Physicochemical Parameters and Electronic Nose.

The objective of this study was to detect and monitor the flavor of tomatoes, as impacted by different postharvest handlings, including chilling storage (CS) and blanching treatment (BT). CS tomatoes were stored in a refrigerator at 5 °C and tested at storage day 0, 3, and 7. BT tomatoes were dipped in 50 or 100 °C water for 1 min, and tested immediately. The taste, mouth feel, and aroma of tomatoes were evaluated by testing the total soluble solid content (TSS), titratable acidity (TA), ratio of TSS and TA (TSS/TA), firmness, and electronic nose (E-nose) response to tomatoes. The experimental results showed that the CS can prevent taste and firmness loss to a certain extent, but the sensory results indicated that CS accelerated flavor loss due to the TSS/TA of CS tomatoes increasing slower than control. The taste and firmness of tomatoes were impacted slightly by 50 °C BT, and were significantly impacted by 100 °C BT. Based on physicochemical parameters, different postharvest handling treatments for tomatoes could not be classified except for the 100 °C BT treated tomatoes, which were significantly impacted in terms of taste and mouth feel. The E-nose is an efficient way to detect differences in postharvest handling treatments for tomatoes, and indicated significant aroma changes for CS and BT treated tomato fruit. The classification of tomatoes after different postharvest handling treatments, based on comprehensive flavor (physicochemical parameters and E-nose combined data), is better than that based on single physicochemical parameters or E-nose, and the comprehensive flavor of 100 °C BT tomatoes changed the most. Even so, the tomato flavor change during postharvest handlings is suggested to be detected and monitored by single E-nose data. The E-nose has also been proved as a feasible way to predict the TSS and firmness of tomato fruit rather than TA or TSS/TA, during the postharvest handing process.