Macromolecular Engineered Multifunctional Room-Temperature Phosphorescent Polymers through Reversible Deactivation Radical Polymerization.

Despite the intensive research in room-temperature phosphorescent (RTP) polymers, the synthesis of RTP polymers with well-defined macromolecular structures and multiple functions remains a challenge. Herein, reversible deactivation radical polymerization was demonstrated to offer a gradient copolymer (GCP) architecture with controlled heterogeneities, which combines hard segment and flexible segment. The GCPs would self-assemble into a multiphase nanostructure, featuring tunable stretchability, excellent RTP performance, and intrinsic healability without compromising light emission under stretching. The mechanical performance is tunable on demand with elongation at break ranging from 5.0% to 221.7% and Young's modulus ranging from 0.5 to 225.0 MPa.

Mechanoluminescent Materials Enable Mechanochemically Controlled Atom Transfer Radical Polymerization and Polymer Mechanotransduction.

Organic mechanophores have been widely adopted for polymer mechanotransduction. However, most examples of polymer mechanotransduction inevitably experience macromolecular chain rupture, and few of them mimic mussel's mechanochemical regeneration, a mechanically mediated process from functional units to functional materials in a controlled manner. In this paper, inorganic mechanoluminescent (ML) materials composed of CaZnOS-ZnS-SrZnOS: Mn2+ were used as a mechanotransducer since it features both piezoelectricity and mechanolunimescence. The utilization of ML materials in polymerization enables both mechanochemically controlled radical polymerization and the synthesis of ML polymer composites. This procedure features a mechanochemically controlled manner for the design and synthesis of diverse mechanoresponsive polymer composites.

Tribochemically Controlled Atom Transfer Radical Polymerization Enabled by Contact Electrification.

Traditional mechanochemically controlled reversible-deactivation radical polymerization (RDRP) utilizes ultrasound or ball milling to regenerate activators, which induce side reactions because of the high-energy and high-frequency stimuli. Here, we propose a facile approach for tribochemically controlled atom transfer radical polymerization (tribo-ATRP) that relies on contact-electro-catalysis (CEC) between titanium oxide (TiO2 ) particles and CuBr2 /tris(2-pyridylmethylamine (TPMA), without any high-energy input. Under the friction induced by stirring, the TiO2 particles are electrified, continuously reducing CuBr2 /TPMA into CuBr/TPMA, thereby conversing alkyl halides into active radicals to start ATRP. In addition, the effect of friction on the reaction was elucidated by theoretical simulation. The results indicated that increasing the frequency could reduce the energy barrier for the electron transfer from TiO2 particles to CuBr2 /TPMA. In this study, the design of tribo-ATRP was successfully achieved, enabling CEC (ca. 10 Hz) access to a variety of polymers with predetermined molecular weights, low dispersity, and high chain-end fidelity.

A new damage estimation method for carbon fiber reinforced polymer based on electrical impedance tomography.

Damage estimation is vital for monitoring the remaining life of carbon fiber reinforced plastic/polymer (CFRP). As a non-invasive, non-radiative, and low-cost method, electrical impedance tomography (EIT) is increasingly investigated for the purpose of structural health monitoring of CFRP. The commonly used EIT method is limited by the image accuracy since it estimates the damage just through a reconstructed image. In this paper, a damage estimation method (DEM) is proposed to quantify the damage location and area. First, each damage is fitted into a two-dimensional Gaussian function through edge fitting. Then, the parameters of the Gaussian function are optimized with the two-norm regularization method. Finally, the damage location and area are estimated with the parameters of the Gaussian function. The accuracy of the DEM is directly evaluated in terms of location error and area error. Both simulation and experimental results demonstrated the potential of the proposed method in providing damage estimation information.

Modified sparse regularization for electrical impedance tomography.

Electrical impedance tomography (EIT) aims to estimate the electrical properties at the interior of an object from current-voltage measurements on its boundary. It has been widely investigated due to its advantages of low cost, non-radiation, non-invasiveness, and high speed. Image reconstruction of EIT is a nonlinear and ill-posed inverse problem. Therefore, regularization techniques like Tikhonov regularization are used to solve the inverse problem. A sparse regularization based on L1 norm exhibits superiority in preserving boundary information at sharp changes or discontinuous areas in the image. However, the limitation of sparse regularization lies in the time consumption for solving the problem. In order to further improve the calculation speed of sparse regularization, a modified method based on separable approximation algorithm is proposed by using adaptive step-size and preconditioning technique. Both simulation and experimental results show the effectiveness of the proposed method in improving the image quality and real-time performance in the presence of different noise intensities and conductivity contrasts.

[A hybrid reconstruction method in electrical impedance tomography based on GMRES and Tikhonov regularization].

Electrical impedance tomography (EIT) is a technique for reconstructing the conductivity distribution of measured field owing to its characteristics of being non-homogeneous, of injecting current at the boundary of the measured subject,of measuring the.corresponding changes in voltage, and of reconstructing the image of the subject consequently. However, the limited measurement data of EIT, and the serious nonlinearity of the field result in ill-posed problem, and the resolution of reconstructed image is poor. To solve the problem, a new hybrid algorithm is herein proposed. The method combines the characteristics of Krylov subspace and Tikhonov regularization, which can improve the real time performance, the quality and robustness of reconstructed image.