Random distributed feedback fiber laser of ultimate efficiency

We demonstrate a random fiber laser of ultimate efficiency. More than 2 Watts are generated from 0.5W of pump excess over the generation threshold. At higher power, an optical efficiency corresponds to the quantum limit.

The effect of slow passage in the pulse-pumped quantum dot laser

In recent years, quantum-dot (QD) semiconductor lasers attract significant interest in many practical applications due to their advantages such as high-power pulse generation because to the high gain efficiency. In this work, the pulse shape of an electrically pumped QD-laser under high current is analyzed. We find that the slow rise time of the pulsed pump may significantly affect the high intensity output pulse. It results in sharp power dropouts and deformation of the pulse profile. We address the effect to dynamical change of the phase-amplitude coupling in the proximity of the excited state (ES) threshold. Under 30ns pulse pumping, the output pulse shape strongly depends on pumping amplitude. At lower currents, which correspond to lasing in the ground state (GS), the pulse shape mimics that of the pump pulse. However, at higher currents the pulse shape becomes progressively unstable. The instability is greatest when in proximity to the secondary threshold which corresponds to the beginning of the ES lasing. After the slow rise stage, the output power sharply drops out. It is followed by a long-time power-off stage and large-scale amplitude fluctuations. We explain these observations by the dynamical change of the alpha-factor in the QD-laser and reveal the role of the slowly rising pumping processes in the pulse shaping and power dropouts at higher currents. The modeling is in very good agreement with the experimental observations. © 2014 SPIE.

Simultaneous optimization of colloidal stability and interfacial charge transfer efficiency in photocatalytic Pt/CdS nanocrystals

Colloidal stability and efficient interfacial charge transfer in semiconductor nanocrystals are of great importance for photocatalytic applications in aqueous solution since they provide long-term functionality and high photocatalytic activity, respectively. However, colloidal stability and interfacial charge transfer efficiency are difficult to optimize simultaneously since the ligand layer often acts as both a shell stabilizing the nanocrystals in colloidal suspension and a barrier reducing the efficiency of interfacial charge transfer. Here, we show that, for cysteine-coated, Pt-decorated CdS nanocrystals and Na2SO3 as hole scavenger, triethanolamine (TEOA) replaces the original cysteine ligands in situ and prolongs the highly efficient and steady H2 evolution period by more than a factor of 10. It is shown that Na2SO3 is consumed during H2 generation while TEOA makes no significant contribution to the H2 generation. An apparent quantum yield of 31.5%, a turnover frequency of 0.11 H2/Pt/s, and an interfacial charge transfer rate faster than 0.3 ps were achieved in the TEOA stabilized system. The short length, branched structure and weak binding of TEOA to CdS as well as sufficient free TEOA in the solution are the keys to enhancing colloidal stability and maintaining efficient interfacial charge transfer at the same time. Additionally, TEOA is commercially available and cheap, and we anticipate that this approach can be widely applied in many photocatalytic applications involving colloidal nanocrystals.

Quantum kernels for unattributed graphs using discrete-time quantum walks

In this paper, we develop a new family of graph kernels where the graph structure is probed by means of a discrete-time quantum walk. Given a pair of graphs, we let a quantum walk evolve on each graph and compute a density matrix with each walk. With the density matrices for the pair of graphs to hand, the kernel between the graphs is defined as the negative exponential of the quantum Jensen–Shannon divergence between their density matrices. In order to cope with large graph structures, we propose to construct a sparser version of the original graphs using the simplification method introduced in Qiu and Hancock (2007). To this end, we compute the minimum spanning tree over the commute time matrix of a graph. This spanning tree representation minimizes the number of edges of the original graph while preserving most of its structural information. The kernel between two graphs is then computed on their respective minimum spanning trees. We evaluate the performance of the proposed kernels on several standard graph datasets and we demonstrate their effectiveness and efficiency.