https://www.selleckchem.com/products/GDC-0449.html In this work, we propose and demonstrate a near-unity light absorber in the ultra-violet to near-infrared range (300-1100 nm) with the average efficiency up to 97.7%, suggesting the achievement of black absorber. The absorber consists of a wavy surface geometry, which is formed by the triple-layer of ITO (indium tin oxide)-Ge (germanium)-Cu (copper) films. Moreover, the minimal absorption is even above 90% in the wide wavelength range from 300 nm to 1015 nm, suggesting an ultra-broadband near-perfect absorption window covering the main operation range for the conventional semiconductors. Strong plasmonic resonances and the near-field coupling effects located in the spatially geometrical structure are the key contributions for the broadband absorption. The absorption properties can be well maintained during the tuning of the polarization and incident angles, indicating the high tolerance in complex electromagnetic surroundings. These findings pave new ways for achieving high-performance optoelectronic devices based on the light absorption over the full-spectrum energy gap range.The use of photo-activated fluorescent molecules to create long sequences of low emitter-density diffraction-limited images enables high-precision emitter localization, but at the cost of low temporal resolution. We suggest combining SPARCOM, a recent high-performing classical method, with model-based deep learning, using the algorithm unfolding approach, to design a compact neural network incorporating domain knowledge. Our results show that we can obtain super-resolution imaging from a small number of high emitter density frames without knowledge of the optical system and across different test sets using the proposed learned SPARCOM (LSPARCOM) network. We believe LSPARCOM can pave the way to interpretable, efficient live-cell imaging in many settings, and find broad use in single molecule localization microscopy of biological structures.Lase