A self-penetrating and chemically stable zinc (ii)-organic framework as multi-responsive chemo-sensor to detect pesticide and antibiotics in water

Guided by the self-penetrating features can improve the stability of metal organic frameworks (MOFs), an unprecedented 3D self-penetrated framework, {[Zn (tptc)_0.5(bimb)]·H₂O}_n ( NUC-6 , here NUC corresponding to North University of China), with 3D (4,4)-c {8⁶} net, was designed. Benefit from the high chemical stability and excellent luminescent property, NUC-6 can be act as an efficient multi-response chemo-sensor in detecting dichloronitroaniline pesticide and nitrofuran antibiotics in water with the detection limits are 116 ppb for DCN pesticide, 16 ppb for NFT antibiotic, and 12 ppb for NTZ antibiotic. Besides, the mechanisms of luminescence quenching were revealed from the viewpoint of internal filter effect (IFE) and photo-induced electron transfer (PET), implied by the optical spectroscopy and quantum chemical calculation. This work provides a promising strategy to design stable MOFs by improving the self-penetrating features and to expand their practical applications in the detection of organic pollutants in aqueous medium.     

Identifying aspirin polymorphs from combined DFT-based crystal structure prediction and solid-state NMR

A combined experimental and computational approach was used to distinguish between different polymorphs of the pharmaceutical drug aspirin. This method involves the use of ab initio random structure searching (AIRSS), a density functional theory (DFT)-based crystal structure prediction method for the high-accuracy prediction of polymorphic structures, with DFT calculations of nuclear magnetic resonance (NMR) parameters and solid-state NMR experiments at natural abundance. AIRSS was used to predict the crystal structures of form-I and form-II of aspirin. The root-mean-square deviation between experimental and calculated ¹H chemical shifts was used to identify form-I as the polymorph present in the experimental sample, the selection being successful despite the large similarities between the molecular environments in the crystals of the two polymorphs.     

Simultispin: A versatile graphical user interface for the simulation of solid-state continuous wave EPR spectra

Solid-state continuous wave (cw) electronic paramagnetic resonance (EPR) spectroscopy is particularly suitable for metal complex analysis. Extracting magnetic parameters by simulation is often necessary to describe the electronic structure of the studied molecular compounds that can have various electronic spin states and characterized by different parameters like g-values, hyperfine coupling or zero field splitting values. Easyspin toolbox on MATLAB is a powerful tool, but for the user, it requires spending time with coding and could discourage nonexperts. Facing this context, we have developed a graphical user interface called Simultispin, dedicated to solid-state cw-EPR spectra simulation. Some examples of experimental spectra of metal complexes (mixture of low spin and high spin Fe^III complexes, dynamic disorder of a Cu^II complex, photogeneration of a Mn^III complex), highlighting specific solid-state functions, are described and analyzed based on simulations performed with Simultispin. We hope that its ergonomy and the ease to set up a complete set of parameters to get reliable simulations could help a large EPR community to improve the efficiency of their interpretations.     

Zn–Co Sulfide Microflowers Anchored on Three-Dimensional Graphene: A High-Capacitance and Long-Cycle-Life Electrode for Asymmetric Supercapacitors

Zinc–cobalt double-metal sulfides (ZCS) as Faradic electrode materials with high energy density have great potential for supercapacitors, but their poor transfer efficiency for electrons and ions hinders their electrochemical response. Herein, ZnCo₂(CO₃)_1.5(OH)₃@ZCS microflower hybrid arrays consisting of thin nanolayer petals were anchored on three-dimensional graphene (ZnCo₂(CO₃)_1.5(OH)₃@ZCS/3DG) by a simple hydrothermal method and additional ion-exchange process. A ZnCo₂(CO₃)_1.5(OH)₃@ZCS/3DG electrode delivered high capacitance (2228 F g⁻¹ at 1 A g⁻¹) and long cycling life (85.7 % retention after 17 000 cycles), which are ascribed to the multicomponent structural design. The 3DG conductive substrate improves the electron-transfer dynamics of the electrode material. Meanwhile, the microflowers consisting of thin nanolayer petals could not only provide many active sites for ions to improve the capacitance, but also alleviate the volume expansion to ensure the structural stability. Furthermore, an all-solid-state asymmetric supercapacitor based on a ZnCo₂(CO₃)_1.5(OH)₃@ZCS/3DG electrode achieved a high energy density of 27 W h kg⁻¹ at 528.3 W kg⁻¹ and exhibits exceptional cyclic stability for 23 000 cycles. Its ability to light a blue LED for 9 min verified the feasibility of its application for energy storage devices.     

Nonmetal Doping as a Robust Route for Boosting the Hydrogen Evolution of Metal-Based Electrocatalysts

Recently, nonmetal doping has exhibited its great potential for boosting the hydrogen evolution reaction (HER) of transition-metal (TM)-based electrocatalysts. To this end, this work overviews the recent achievements made on the design and development of the nonmetal-doped TM-based electrocatalysts and their performance for the HER. It is also shown that by rationally doping nonmetal elements, the electronic structures of TM-based electrocatalysts can be effectively tuned and in turn the Gibbs free energy of the TM for adsorption of H* intermediates (ΔG_H*) optimized, consequently enhancing the intrinsic activity of TM-based electrocatalysts. Notably, we highlight that concurrently doping two nonmetal elements can continuously and precisely regulate the electronic structures of the TM, thereby maximizing the activity for HER. Moreover, nonmetal doping also accounts for enhancing the physical properties of the TM (i.e. surface area). Therefore, nonmetal doping is a robust strategy for simultaneous regulation of the chemical and physical features of the TM.     

Nanoweb Surface-Mounted Metal–Organic Framework Films with Tunable Amounts of Acid Sites as Tailored Catalysts

Metal–organic frameworks (MOFs) are a promising class of materials for many applications, due to their high chemical tunability and superb porosity. By growing MOFs as (thin-)films, additional properties and potential applications become available. Here, copper (II) 1,3,5-benzenetricarboxylate (Cu-BTC) metal–organic framework (MOF) thin-films are reported, which were synthesized by spin-coating, resulting in “nanowebs”, that is, fiber-like structures. These surface-mounted MOFs (SURMOFs) were studied by using photoinduced force microscopy (PiFM) and time-of-flight secondary ion mass spectrometry (ToF-SIMS). The optimal concentration of precursors (10 mm ) was determined that resulted in chemically homogeneous, pure nanowebs. Furthermore, the morphology and (un)coordinated Cu sites in the web were tuned by varying the rotation speed of the spin-coating process. X-ray diffraction (XRD) analysis showed that rotation speeds ≥2000 rpm (with precursors in a water/ethanol solution) generate the catena-triaqua-μ-(1,3,5-benzenetricarboxylate)-copper(II), or Cu(BTC)(H₂O)₃ coordination polymer. X-ray photoelectron spectroscopy (XPS) highlighted the strong decrease in number of (defective) Cu⁺ sites, as the nanowebs mainly consist of coordinated Cu²⁺ Lewis acid sites (LAS) and organic linker–linker, for example, hydrogen-bonding, interactions. Finally, the Lewis-acidic character of the Cu sites is illustrated by testing the films as catalysts in the isomerization of α-pinene oxide. The higher number of LAS (≥3000 rpm), result in higher campholenic aldehyde selectivity reaching up to 87.7 %. Furthermore, the strength of a combined micro- and spectroscopic approach in understanding the nature of MOF thin-films in a spatially resolved manner is highlighted.     

FeOOH@Metal–Organic Framework Core–Satellite Nanocomposites for the Serum Metabolic Fingerprinting of Gynecological Cancers

High-throughput metabolic analysis is of significance in diagnostics, while tedious sample pretreatment has largely hindered its clinic application. Herein, we designed FeOOH@ZIF-8 composites with enhanced ionization efficiency and size-exclusion effect for laser desorption/ionization mass spectrometry (LDI-MS)-based metabolic diagnosis of gynecological cancers. The FeOOH@ZIF-8-assisted LDI-MS achieved rapid, sensitive, and selective metabolic fingerprints of the native serum without any enrichment or purification. Further analysis of extracted serum metabolic fingerprints successfully discriminated patients with gynecological cancers (GCs) from healthy controls and also differentiated three major subtypes of GCs. Given the low cost, high-throughput, and easy operation, our approach brings a new dimension to disease analysis and classification.     

Canonical Lorentzian spin structure and twistor spinors on the Fefferman space of a contact Riemannian manifold

The Fefferman space of a contact Riemannian manifold carries a Lorentzian spin structure canonically. On the Lorentzian spin manifold, we investigate the Dirac operator and the twistor operator closely. In particular, we show that, if the contact Riemannian manifold is integrable, then there exist non-zero global solutions of the twistor equation.     

A benchmark of optimally folded protein structures using integer programming and the 3D-HP-SC model

The Protein Structure Prediction (PSP) problem comprises, among other issues, forecasting the three-dimensional native structure of proteins using only their primary structure information. Most computational studies in this area use synthetic data instead of real biological data. However, the closer to the real-world, the more the impact of results and their applicability. This work presents 17 real protein sequences extracted from the Protein Data Bank for a benchmark to the PSP problem using the tri-dimensional Hydrophobic-Polar with Side-Chains model (3D-HP-SC). The native structure of these proteins was found by maximizing the number of hydrophobic contacts between the side-chains of amino acids. The problem was treated as an optimization problem and solved by means of an Integer Programming approach. Although the method optimally solves the problem, the processing time has an exponential trend. Therefore, due to computational limitations, the method is a proof-of-concept and it is not applicable to large sequences. For unknown sequences, an upper bound of the number of hydrophobic contacts (using this model) can be found, due to a linear relationship with the number of hydrophobic residues. The comparison between the predicted and the biological structures showed that the highest similarity between them was found with distance thresholds around 5.2–8.2 Å. Both the dataset and the programs developed will be freely available to foster further research in the area.