Search

In the field of molecular magnetism, the lanthanide-radical (Ln-Rad) method has become one of the most appealing tactics for introducing strong magnetic interactions and has spurred on the booming development of heterospin single-molecule magnets (SMMs). The article is a timely retrospect on the research progress of Ln-Rad heterospin systems and special attention is invested on low dimensional Ln-Rad compounds with SMM behavior, primarily concerning with nitrogen-based radicals, semiquinone and nitroxide radicals. Rational design, molecular structures, magnetic behaviors and magneto-structural correlations are highlighted. Meanwhile, particular attention is focused on the influence of exchange couplings on the dynamic magnetic properties, with the purpose of helping to guide the design of prospective radical-based Ln-SMMs.

Hydrogels are known to be soft and wet smart materials that respond to external stimuli. Numerous hydrogel actuators have been developed that perform volume and shape changes, and reversible motions. Heterogeneity, periodicity, and integration of hydrogels with different properties are needed to realize biomimetic motions. In order to achieve specific actuations with predictable pathways, it is critical to fabricate hydrogel structures in a well-controlled manner. This review article summarizes recent progress in representative methods to fabricate hydrogel actuators by different methods, ranging from sequential synthesis, macroscopic supramolecular assembling, field-induced alignment, photolithography, ionoprinting, 3D printing, and gradient structuring. The advantages and limits of these methods are compared and analyzed. Finally, a brief perspective and conclusion are presented to point out some important issues for further studies.

A biradical with ferromagnetic intramolecular interaction was found to improve the SCM behavior of a nitronyl nitroxide-Cu-Dy chain, resulting in an energy barrier for magnetization reversal of 40 K.

In this work, we design and synthesize a new near-infrared (NIR) ratiometric fluorescent probe FD-H2S for the highly sensitive (DL 68.2 nM) detection of H2S with fast response (15 s), large emission shift (220 nm) and excellent enhancement (168-fold in ratiometric value). The probe could be applied for monitoring and imaging of exogenous or endogenous H2S in live MCF-7 cells and in live mice with the fastest response.

Atomic-scale gold clusters were intercalated at the inter-facet corner sites of Pt-shell Ru-core nanocatalysts with near-monolayer shell thickness. We demonstrated that these unique clusters could serve as a drain of valence electrons in the kink region of the core-shell heterojunction. As jointly revealed by density functional theory calculations and valence band spectra, these Au clusters extract core-level electrons to the valence band. They prevent corrosion due to protonation and enhance the tolerance of CO by increasing the electronegativity at the outermost surface of the NCs during the methanol oxidation reaction (MOR). In these circumstances, the retained current density of Pt-shell Ru-core NCs is doubled in a long-term (2 hours) MOR at a fixed voltage (0.5 V vs. SCE) by intercalating these sub-nanometer gold clusters. Such novel structural confinement provides a possible strategy for developing direct-methanol fuel cell (DMFC) modules with high power and stability.

A novel series of 1-(pyrrolidin-1-ylmethyl)-2-[(3-oxo-indan)-formyl]-1,2,3,4-tetrahydroisoquinoline derivatives maj-3a-maj-3u were synthesized and evaluated in vitro for their binding affinity at κ-opioid receptors. Maj-3c displayed the highest affinity for κ-opioid receptors (Ki = 0.033 nM) among all the compounds evaluated. Furthermore, all four stereoisomers of compound 3c were prepared, and (1S,18S)-3c was identified as the most potent (Ki = 0.0059 nM) κ-opioid receptor agonist among the four stereoisomers. Maj-3c produced significant antinociception (ED50 = 0.000406 mg kg(-1)) compared to U-50,488H and original BRL 52580 in the acetic acid writhing assay, but its strong sedative effect (ED50 = 0.000568 mg kg(-1)) observed in the mouse rotation test reduced its druggability. To minimize the central nervous system side effects, a series of hydroxyl-containing analogs of maj-3c were synthesized, and maj-11a was found to be a potent κ-opioid receptor agonist (Ki = 35.13 nM). More importantly, the dose for the sedative effect (ED50 = 9.29 mg kg(-1)) of maj-11a was significantly higher than its analgesic dose (ED50 = 0.392 mg kg(-1)), which made it a promising peripheral analgesic candidate compound with weak sedative side effects.

Classical calibration and inverse calibration are two kinds of multivariate calibration in chemical modeling. They use strategies of modeling in component spectral space and in measured variable space, respectively. However, the intrinsic difference between these two calibration models is not fully investigated. Besides, in the case of complex analytical systems, the net analyte signal (NAS) cannot be well defined in inverse calibration due to the existence of uninformative and/or interfering variables. Therefore, application of the NAS cannot improve the predictive performance for this kind of calibration, since it is essentially a technique based on the full-spectrum. From our perspective, variable selection can significantly improve the predictive performance through removing uninformative and/or interfering variables. Although the need for variable selection in the inverse calibration model has already been experimentally demonstrated, it has not aroused so much attention. In this study, we first clarify the intrinsic difference between these two calibration models and then use a new perspective to intrinsically prove the importance of variable selection in the inverse calibration model for complex analytical systems. In addition, we have experimentally validated our viewpoint through the use of one UV dataset and two generated near infrared (NIR) datasets.

By means of the first-principles calculations, we predict a new metallic two-dimensional carbon allotrope named net W with Cmmm (D(2h)(19)) symmetry. This new carbon phase consists of squares C(4), hexagons C(6), and octagons C(8), its dynamical stability is validated based on phonon-mode analysis and it is energetically more favored over previously proposed two-dimensional carbon forms such as net C, planar C(4), biphenylene, graphyne, and the recently prepared graphdiyne. On the other hand, we find that net W possesses strong metallicity due to its rather large density of states across the Fermi level contributed by the carbon p(z) orbital. Through first-principles molecular dynamics simulations, we theoretically demonstrate that selective dehydrogenation of the parallel-laid narrowest angular polycyclic aromatic hydrocarbons (4-AGNRs) would lead to a spontaneous interconversion to such a net W carbon phase, the possible synthetic routes are also addressed. Of particular interest, semiconductivity could be introduced when a net W carbon sheet is cut into ribbons of certain widths. Our work shows that the net W carbon sheet and its nanoribbons have great potential for future nanoelectronics.

By means of first-principles calculations, we theoretically studied the structural stabilities and electronic properties of a pure-carbon 2D covalent metal named planar C(4) in P4/mmm (D(1)(4h)) symmetry. Planar C(4) is confirmed to be dynamically stable in the ground state based on phonon-mode analysis, and it is more stable than graphyne and the recently prepared graphdiyne. Moreover, it has a higher density of states (DOS) at the Fermi level than any plausible metallic carbon nanotube. Of particular interest, there exist two distinct types of planar C(4) nanoribbons (NRs): type I is predicted to be uniformly metallic regardless of the width change, while type II exhibits remarkable odd-even metal-semiconductor oscillating behavior depending on the width. The edge structure of type II NRs is revealed to be energetically more favored since its formation energy is about 0.45 eV per edge atom lower than that of type I NRs. Our work shows that planar C(4) carbon sheet and its NRs could serve as potential materials for future functional nanodevices.

The intriguing electronic and magnetic properties of one-side semihydrogenated silicene and germanene are investigated by means of first-principles calculations. Both one-side semihydrogenated silicene and germanene are confirmed as dynamically stable in the ground state based on phonon-mode analysis. Moreover, we find that semihydrogenation from only one side causes localized and unpaired 3p (4p) electrons in the unhydrogenated Si (Ge) sites and then introduces ferromagnetism to silicene (germanene) sheet with no need for doping, cutting, or etching. One-side semihydrogenated silicene and germanene are both identified as semiconductors with direct energy gaps: their gap values obtained from the HSE06 functional are estimated to be 1.74 eV and 1.32 eV, much greater than the PBE-GGA results of 0.94 eV and 0.41 eV, respectively. From pristine to one-side semihydrogenated and then to fully hydrogenated systems, silicene and germanene change from metallic to magnetic semiconducting and then to nonmagnetic semiconducting. The hydrogenation process provides a novel method to tune the properties of silicene and germanene with unprecedented potentials for future nanoelectronics.

Selecting a small subset of informative genes plays an important role in accurate prediction of clinical tumor samples. Based on model population analysis, a novel variable selection method, called noise incorporated subwindow permutation analysis (NISPA), is proposed in this study to work with support vector machines (SVMs). The essence of NISPA lies in the point that one noise variable is added into each sampled sub-dataset and then the distribution of variable importance of the added noise could be computed and serves as the common reference to evaluate the experimental variables. Further, by using the non-parametric Mann-Whitney U test, a P value can be assigned to each variable which describes to what extent the distributions of the gene variable and the noise variable are different. According to the computed P values, all the variables could be ranked and then a small subset of informative variables could be determined to build the model. Moreover, by NISPA, we are the first to distinguish the variables into a more detailed classification as informative, uninformative (noise) and interfering variables in comparison with other methods. In this study, two microarray datasets are employed to evaluate the performance of NISPA. The results show that the prediction errors of SVM classifiers could be significantly reduced by variable selection using NISPA. It is concluded that NISPA is a good alternative of variable selection algorithm., (© The Royal Society of Chemistry 2011)

Large amounts of data from high-throughput metabolomics experiments have become commonly more and more complex, which brings a number of challenges to existing statistical modeling. Thus there is a need to develop a statistically efficient approach for mining the underlying metabolite information contained by metabolomics data under investigation. In this work, we provide a new strategy based on Monte Carlo cross validation coupled with the classification tree algorithm, which is termed as the MCTree approach. The MCTree approach inherently provides a feasible way to uncover the predictive structure of metabolomics data by the establishment of many cross-predictive models. With the help of the sample proximity matrix such obtained, it seems to be able to give some interesting insights into metabolomics data. Simultaneously, informative metabolites or potential biomarkers can be successfully discovered by means of variable importance ranking in the MCTree approach. Two real metabolomics datasets are finally used to demonstrate the performance of the proposed approach.