Large magnetoresistances and non-Ohmic conductivity in EuWO[1+x]N[2-x]

The magnetic field and voltage dependent electronic transport properties of EuWO[1+x]N[2-x] ceramics are reported. Large negative magnetoresistances are observed at low temperatures, up to 70% in the least doped (x=0.09) material. Non-Ohmic conduction emerges below the 12 K Curie transition. This is attributed to a microstructure of ferromagnetic conducting and antiferromagnetic insulating regions resulting from small spatial fluctuations in the chemical doping

Data for: Effect of tectonic evolution on hydrocarbon charging time: a case study from Lower Shihezi Formation (Guadalupian), Hangjinqi area, northern Ordos, China

Data for: Effect of tectonic evolution on hydrocarbon charging time: a case study from Lower Shihezi Formation (Guadalupian), Hangjinqi area, northern Ordos, Chin

A Network of Conformational Transitions Revealed by Molecular Dynamics Simulations of the Binary Complex of <i>Escherichia coli</i> 6‑Hydroxymethyl-7,8-dihydropterin Pyrophosphokinase with MgATP

6-Hydroxymethyl-7,8-dihydropterin pyrophosphokinase (HPPK) catalyzes the first reaction in the folate biosynthetic pathway. Comparison of its X-ray and nuclear magnetic resonance structures suggests that the enzyme undergoes significant conformational change upon binding to its substrates, especially in three catalytic loops. Experimental research has shown that, in its binary form, even bound by analogues of MgATP, loops 2 and 3 remain rather flexible; this raises questions about the putative large-scale induced-fit conformational change of the HPPK–MgATP binary complex. In this work, long-time all-atomic molecular dynamics simulations were conducted to investigate the loop dynamics in this complex. Our simulations show that, with loop 3 closed, multiple conformations of loop 2, including the open, semiopen, and closed forms, are all accessible to the binary complex. These results provide valuable structural insights into the details of conformational changes upon 6-hydroxymethyl-7,8-dihydropterin (HP) binding and biological activities of HPPK. Conformational network analysis and principal component analysis related to the loops are also discussed

Multivalent Duplexed-Aptamer Networks Regulated a CRISPR-Cas12a System for Circulating Tumor Cell Detection

Although circulating tumor cells (CTCs) have great potential to act as the mini-invasive liquid biopsy cancer biomarker, a rapid and sensitive CTC detection method remains lacking. CRISPR-Cas12a has recently emerged as a promising tool in biosensing applications with the characteristic of fast detection, easy operation, and high sensitivity. Herein, we reported a CRISPR-Cas12a-based CTC detection sensor that is regulated by the multivalent duplexed-aptamer networks (MDANs). MDANs were synthesized on a magnetic bead surface by rolling circle amplification (RCA), which contain multiple duplexed-aptamer units that allow structure switching induced by cell-binding events. The presence of target cells can trigger the release of free “activator DNA” from the MDANs structure to activate the downstream CRISPR-Cas12a for signal amplification. Furthermore, the 3D DNA network formed by RCA products also provided significantly higher sensitivity than the monovalent aptamer. As a proof-of-concept study, we chose the most widely used sgc8 aptamer that specifically recognizes CCRF-CEM cells to validate the proposed approach. The MDANs-Cas12a system could afford a simple and fast CTC detection workflow with a detection limit of 26 cells mL–1. We also demonstrated that the MDANs-Cas12a could directly detect the CTCs in human blood samples, indicating a great potential of the MDANs-Cas12a in clinical CTC-based liquid biopsy

Programmed Synthesis of Sn₃N₄ Nanoparticles via a Soft Chemistry Approach with Urea: Application for Ethanol Vapor Sensing

Metal nitrides are a significant class of multifunctional materials that have attracted a huge and ever-increasing interest for their new structural and redox chemical, as well as physical, characteristics. In this work, we present a designed synthesis of Sn₃N₄ nanoparticles through a soft urea route for the first time. The strategy includes the synthesis of gel-like tin–urea precursor and subsequent transformation to Sn₃N₄ nanoparticles via thermal treatment of the as-prepared precursor under NH₃ flow. Various techniques were employed to characterize the structure and morphology of the as-prepared Sn₃N₄ samples. When innovatively utilized as sensing material for a gas sensor, Sn₃N₄ nanoparticles exhibited high sensitivity, excellent cyclability, and long-term stability to ethanol at the operating temperature of 120 °C, which is lower than those of metal oxide-based ethanol sensors. This research work provides an efficient method for preparing Sn₃N₄nanoparticles that are promising sensing materials for ethanol gas sensors

Capping Gold Nanoparticles to Achieve a Protein-like Surface for Loop-Mediated Isothermal Amplification Acceleration and Ultrasensitive DNA Detection

Loop-mediated isothermal amplification (LAMP) is a popular DNA amplification method. Gold nanoparticles (AuNPs) were reported to enhance the efficiency of LAMP, although the underlying mechanism remained elusive. Since AuNPs strongly adsorb a range of ligands, preadsorbed ligands cannot be easily displaced. In this work, we systematically investigated the effect of surface-modified AuNPs on LAMP by varying the order of mixing of AuNPs with each reagent in the LAMP system (Mg2+, template DNA, dNTPs, primers, and polymerase). Mixing the AuNPs with the primers delayed the LAMP based on SYBR green I fluorescence. While other orders of mixing had little effect, all accelerated the reaction. We then tested other common ligands including polymers (polyethylene glycol and polyvinylpyrrolidone), inorganic ions (Br–), proteins, glutathione (GSH), and DNA (A15) on AuNP-LAMP. The boosted AuNP performance on LAMP was most obvious when the AuNPs formed a protein-like surface. Finally, using GSH-capped AuNPs, a detection limit of around 100 copies/μL–1 of target DNA was achieved. This work has identified a ligand-capped AuNP strategy to boost LAMP and yielded a higher sensitivity in DNA sensing, which also deepens our understanding of AuNP-assisted LAMP

Mesoporous Ti_0.5Cr_0.5N Supported PdAg Nanoalloy as Highly Active and Stable Catalysts for the Electro-oxidation of Formic Acid and Methanol

We report a robust noncarbon Ti_0.5Cr_0.5N support synthesized by an efficient solid–solid phase separation method. This ternary nitride exhibits highly porous, sintered, and random network structure with a crystallite size of 20–40 nm, resulting in a high specific surface area. It is not only kinetically stable in both acid and alkaline media, but also electrochemically stable in the potential range of fuel cell operation. Two typical anode reactions, formic acid oxidation in acid media and methanol oxidation in alkaline media, are employed to investigate the possibility of Ti_0.5Cr_0.5N as an alternative to carbon. Bimetallic PdAg nanoparticles (∼4 nm) act as anode catalysts for the two anode reactions. PdAg/Ti_0.5Cr_0.5N exhibits much higher mass activity and durability for the two reactions than PdAg/C and Pd/C catalyst, suggesting that mesoporous Ti_0.5Cr_0.5N is a very promising support in both acid and alkaline media

Electrochemical Detection of Circulating Tumor Cells Based on DNA Generated Electrochemical Current and Rolling Circle Amplification

Circulating tumor cells (CTCs) are important indicators for tumor diagnosis and tumor metastasis. However, the extremely low levels of CTCs in peripheral blood challenges the precise detection of CTCs. Herein, we report DNA generated electrochemical current combined with rolling circle amplification (RCA) as well as magnetic nanospheres for highly efficient magnetic capture and ultrasensitive detection of CTCs. The antiepithelial cell adhesion molecule (EpCAM) antibody-modified magnetic nanospheres were used to capture and enrich CTCs. The following binding of an aptamer onto the CTC surface and the subsequent RCA assembled a significant amount of DNA molecules onto the electrode. The reaction of the DNA molecules with molybdate can then form redox molybdophosphate and produce an electrochemical current. Using the breast cancer cell MCF-7 as a model, the sensor displays good performances toward detection of MCF-7 that was spiked into peripheral blood. The signal amplification strategy integrated with a magnetic nanosphere platform exhibits good performance in the efficient capture and detection of CTCs, which may find wide potential in cancer diagnostics and therapeutics

Structure-Based Reaction Descriptors for Predicting Rate Constants by Machine Learning: Application to Hydrogen Abstraction from Alkanes by CH₃/H/O Radicals

Accurate determination of the thermal rate constants for combustion reactions is a highly challenging task, both experimentally and theoretically. Machine learning has been proven to be a powerful tool to predict reaction rate constants in recent years. In this work, three supervised machine learning algorithms, including XGB, FNN, and XGB-FNN, are used to develop quantitative structure–property relationship models for the estimation of the rate constants of hydrogen abstraction reactions from alkanes by the free radicals CH3, H, and O. The molecular similarity based on Morgan molecular fingerprints combined with the topological indices are proposed to represent chemical reactions in the machine learning models. Using the newly constructed descriptors, the hybrid XGB-FNN algorithm yields average deviations of 65.4%, 12.1%, and 64.5% on the prediction sets of alkanes + CH3, H, and O, respectively, whose performance is comparable and even superior to the corresponding one using the activation energy as a descriptor. The use of activation energy as a descriptor has previously been shown to significantly improve prediction accuracy (Fuel 2022, 322, 124150) but typically requires cumbersome ab initio calculations. In addition, the XGB-FNN models could reasonably predict reaction rate constants of hydrogen abstractions from different sites of alkanes and their isomers, indicating a good generalization ability. It is expected that the reaction descriptors proposed in this work can be applied to build machine learning models for other reactions

Convenient Size Analysis of Nanoplastics on a Microelectrode

Chemical recycling is a promising approach to reduce plastic pollution. Timely and accurate size analysis of produced nanoplastics is necessary to monitor the process and assess the quality of chemical recycling. In this work, a sandwich-type microelectrode sensor was developed for the size assessment of nanoplastics. β-Mercaptoethylamine was modified on the microelectrode to enhance its surface positive charge density. Polystyrene (PS) nanoplastics were captured on the sensor through electrostatic interactions. Ferrocene was used as an electrochemical beacon and attached to PS via hydrophobic interactions. The results show a nonlinear dependence of the sensor’s current response on the PS particle size. The size resolving ability of the microelectrode is mainly attributed to the small size of the electrode and the resulting attenuation of the electric field strength. For mixed samples with different particle sizes, this method can provide accurate average particle sizes. Through an effective pretreatment process, the method can be applied to PS nanoplastics with different surface properties, ensuring its application in evaluating different chemical recycling methods