For the hydroformylation of alkenes, developing ligand-free heterogeneous catalysts is a research focus because of both fundamental research interests and potential commercial applications. However, the leaching of active metals (e.g., Co, Rh) during the hydroformylation reaction seriously hinders the development of heterogeneous catalysts. Suppressing metal leaching with the use of an effective protectant is a possible solution to this problem. In this work, Co leaching was suppressed by the addition of suitable protectants (e.g., citric acid, oxalic acid, and formic acid), thus providing a stable heterogeneous catalytic system. In situ Fourier transform infrared proved that toluene and 1-hexene can promote the solvation of Co carbonyls because of the solvent effect. In contrast, formic acid can be converted to formate species (HCOO) on the surface of Co and effectively suppress the formation of Co carbonyls. Theoretical calculations further proved that high CO coverage on the surface of Co leads to the formation of Co carbonyl species. Furthermore, carboxyl radicals resulting from the dissociation of the O–H bonds of organic acids exhibit strong adsorption on the surface of Co. The intrinsic role of the protectants in suppressing metal leaching is attributed to the decreased CO coverage on the surface of Co by competitive adsorption.

Electronic skin (E‐skin) is an emerging and promising human‐machine interface. Besides skin‐like functions of tactile perception and stretchability, skin‐like comfortabilities, including breathability, moisture permeability, softness, and thermoregulating ability are, also crucial factors for E‐skins. Thermoregulation is one of the most important roles of human skin. People can feel comfortable when their skins are regulated at a certain range of temperature. Moreover, it is a dynamic process according to the surrounding temperature. Current E‐skins do not have the function of dynamically regulating their temperature. Here, a thermoregulating E‐skin (TE‐skin) based on liquid metal as a phase change material with its melting point in the comfortable temperature range of human skin is reported. Compared with conventional E‐skins, the TE‐skin can dynamically termoregulate according to the surrounding temperature through a phase change. Combining with the principle of triboelectric nanogenerator, the TE‐skin is also able to act as a self‐powered sensor. Based on the self‐powered TE‐skin, an intelligent dialing communications system is further developed, which can be used to call a cellphone on human skin. For the first time, this study introduces the dynamic thermoregulating concept to E‐skins and could open up new opportunities for E‐skin developments.

No abstract

No abstract

Multi-label classification, or the same example can belong to more than one class label, happens in many applications. To name a few, image and video annotation, functional genomics, social network annotation and text categorization are some typical applications. Existing methods have limited performance in both efficiency and accuracy. In this paper, we propose an extension over decision tree ensembles that can handle both challenges. We formally analyze the learning risk of Random Decision Tree (RDT) and derive that the upper bound of risk is stable and lower bound decreases as the number of trees increases. Importantly, we demonstrate that the training complexity is independent from the number of class labels, a significant overhead for many state-of-the-art multi-label methods. This is particularly important for problems with large number of multi-class labels. Based on these characteristics, we adopt and improve RDT for multi-label classification. Experiment results have demonstrated that the computation time of the proposed approaches is 1-3 orders of magnitude less than other methods when handling datasets with large number of instances and labels, as well as improvement up to more than 10% in accuracy as compared to a number of state-of-the-art methods in some datasets for multi-label learning. Considering efficiency and effectiveness together, Multi-label RDT is the top rank algorithm in this domain. Even compared with the HOMER algorithm proposed to solve the problem of large number of labels, Multi-label RDT runs 2-3 orders of magnitude faster in training process and achieves some improvement on accuracy. Software and datasets are available from the authors.

No abstract

A cheap protic ionic liquid (PIL), 3-(Dimethylamino)-1-propylamine acetate (abbreviated as [DMAPAH][Ac]), is investigated in this work as the activator of N-methyldiethanolamine (MDEA) for fast capture of CO 2 . The PIL-activated MDEA solutions show excellent performance in absorption rate and capacity (!2.5 molÁkg 21 ). A novel absorption mechanism is proposed to account for the phenomenon, where the shuttling role of the PIL is described in detail. Additionally, the enthalpy change DH SOL (245 to 252 kJÁmol 21 ), the turnover number of the PIL and the regeneration efficiency (>92%) are also measured. All these data show that the PIL-mediated MDEA solutions may be used as a kind of promising absorbents for fast capture of CO 2 . Figure 2. Absolute absorption capacity (a) and relative CO 2 loading (moles of CO 2 absorbed by per mole of PIL) (b) of PIL aqueous solutions as a function of the PIL concentration (ٗ: 60 wt %; ᭺: 70 wt %; ᭝: 80 wt %; ᭞: 90 wt %) at T 5 308.15 K.

No abstract