Graphitic carbon nitride (g‐C3N4) has been widely explored as a photocatalyst for water splitting. The anodic water oxidation reaction (WOR) remains a major obstacle for such processes, with issues such as low surface area of g‐C3N4, poor light absorption, and low charge‐transfer efficiency. In this work, such longtime concerns have been partially addressed with band gap and surface engineering of nanostructured graphitic carbon nitride (g‐C3N4). Specifically, surface area and charge‐transfer efficiency are significantly enhanced through architecting g‐C3N4 on nanorod TiO2 to avoid aggregation of layered g‐C3N4. Moreover, a simple phosphide gas treatment of TiO2/g‐C3N4 configuration not only narrows the band gap of g‐C3N4 by 0.57 eV shifting it into visible range but also generates in situ a metal phosphide (M=Fe, Cu) water oxidation cocatalyst. This TiO2/g‐C3N4/FeP configuration significantly improves charge separation and transfer capability. As a result, our non‐noble‐metal photoelectrochemical system yields outstanding visible light (>420 nm) photocurrent: approximately 0.3 mA cm−2 at 1.23 V and 1.1 mA cm−2 at 2.0 V versus RHE, which is the highest for a g‐C3N4‐based photoanode. It is expected that the TiO2/g‐C3N4/FeP configuration synthesized by a simple phosphide gas treatment will provide new insight for producing robust g‐C3N4 for water oxidation.

Aiming at the problems of worse reconstructed image quality and larger time complexity of the fast iterative shrinkage-thresholding algorithm in compressed sensing, this paper presents adaptive regularized fast iterative shrinkage-thresholding algorithm. This algorithm brings in the idea of adaptively selecting regularization parameter on the basis of using gradient method and threshold shrinkage to minimize the objective function. During the iteration process regularization parameter is adaptively selected from the whole value in order to adjust the proportion of the former part and the latter part of the objective function value. Simulation results show that the proposed algorithm, compared with the traditional algorithms, obtains the better reconstructed image quality and lower time complexity.