The synthesis of polyoxymethylene dimethyl ethers (PODEn), being green diesel fuel additives, from dimethoxymethane (DMM) and paraformaldehyde over three different cation resins, namely NKC‐9, D001‐CC, and D72, in a stirred‐autoclave reactor was investigated. The pore size distribution and surface area were measured with nitrogen adsorption‐desorption. NKC‐9 had a larger exchange capacity, better developed porosity structure, and higher reaction activity than D001‐CC and D72. The effects of reaction temperature, DMM/CH2O molar ratio, reaction time, and catalyst loading were studied with NKC‐9. A possible mechanism was explored to describe the bond reorganizations during the reactions.

Organic thermoelectric (TE) materials create a brand new perspective to search for high-efficiency TE materials, due to their small thermal conductivity. The overlap of pz orbitals, commonly existing in organic π-stacking semiconductors, can potentially result in high electronic mobility comparable to inorganic electronics. Here we propose a strategy to utilize the overlap of pz orbitals to increase the TE efficiency of layered polymeric carbon nitride (PCN). Through first-principles calculations and classical molecular dynamics simulations, we find that A-A stacked PCN has unexpectedly high cross-plane ZT up to 0.52 at 300 K, which can contribute to n-type TE groups. The high ZT originates from its one-dimensional charge transport and small thermal conductivity. The thermal contribution of the overlap of pz orbitals is investigated, which noticeably enhances the thermal transport when compared with the thermal conductivity without considering the overlap effect. For a better understanding of its TE advantages, we find that the low-dimensional charge transport results from strong pz-overlap interactions and the in-plane electronic confinement, by comparing π-stacking carbon nitride derivatives and graphite. This study can provide a guidance to search for high cross-plane TE performance in layered materials.

Flexible metal electrodes are essential for flexible electronics, where the main challenge is to obtain mask-free patterned metals directly on substrates such as poly­(dimethylsiloxane) (PDMS) at low cost. This work highlights a feasible strategy named femtosecond laser-activated metal deposition for electroless deposition of metals (Cu, Ni, Ag, and Au) on PDMS, which is suitable for maskless and low-cost fabrication of metal layers on PDMS and even on other materials of different natures including polyethylene terephthalate, paper, Si, and glass. The electrical conductivity of the PDMS/Cu electrode is comparable to that of bulk Cu. Moreover, robust bonding at the PDMS/Cu interface is evidenced by a scotch tape test and bending test of more than 20,000 cycles. Compared with previous studies using a nanosecond laser, the restriction on absorbing sensitizers could be alleviated, and catalysts could originate from precursors without polymer substrates under a femtosecond laser, which may be attributed to nonlinear absorption and ultrashort heating time with the femtosecond laser. Implementing a human–machine interface task is demonstrated by recognizing hand gestures via a multichannel electrode array with high fidelity to control a robot hand.

Nuclear magnetic resonance (NMR) spectroscopy is highly unbiased and reproducible, which provides us a powerful tool to analyze mixtures consisting of small molecules. However, the compound identification in NMR spectra of mixtures is highly challenging because of chemical shift variations of the same compound in different mixtures and peak overlapping among molecules. Here, we present a pseudo-Siamese convolutional neural network method (pSCNN) to identify compounds in mixtures for NMR spectroscopy. A data augmentation method was implemented for the superposition of several NMR spectra sampled from a spectral database with random noises. The augmented dataset was split and used to train, validate and test the pSCNN model. Two experimental NMR datasets (flavor mixtures and additional flavor mixture) were acquired to benchmark its performance in real applications. The results show that the proposed method can achieve good performances in the augmented test set (ACC = 99.80%, TPR = 99.70% and FPR = 0.10%), the flavor mixtures dataset (ACC = 97.62%, TPR = 96.44% and FPR = 2.29%) and the additional flavor mixture dataset (ACC = 91.67%, TPR = 100.00% and FPR = 10.53%). We have demonstrated that the translational invariance of convolutional neural networks can solve the chemical shift variation problem in NMR spectra. In summary, pSCNN is an off-the-shelf method to identify compounds in mixtures for NMR spectroscopy because of its accuracy in compound identification and robustness to chemical shift variation.

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Heat in phononic crystals (PnCs) are carried by phonons, which can behave coherently (wave-like) or incoherently (particle-like) depending on the modes, temperature, and length scales. By comparing the measured thermal conductivity of

Origami offers a promising alternative for designing innovative soft robotic actuators. While features of origami, such as bi-directional motion and structural anisotropy, haven't been extensively explored in the past, this letter presents a novel design inspired by origami tubes for a bi-directional actuator. This actuator is capable of moving in two orthogonal directions and has separate channels throughout its body to control each movement. We introduce a bottom-up design methodology that can also be adapted for other complex movements. The actuator was manufactured using popular 3D printing techniques. To enhance its durability, we experimented with different 3D printing technologies and materials. The actuator's strength was further improved using silicon spin coating, and we compared the performance of coated, uncoated, and silicononly specimens. The material model was empirically derived by testing specimens on a universal testing machine (UTM). Lastly, we suggest potential applications for these actuators, such as in quadruped robots.