Transition metal doped colloidal nanomaterials (TMDCNMs) have recently attracted attention as promising nano‐emitters due to dopant‐induced properties. However, despite ample investigations on the steady‐state and dynamic spectroscopy of TMDCNMs, experimental understandings of their performance in stimulated emission regimes are still elusive. Here, the optical gain properties of copper‐doped CdSe colloidal quantum wells (CQWs) are systemically studied with a wide range of dopant concentration for the first time. This work demonstrates that the amplified spontaneous emission (ASE) threshold in copper‐doped CQWs is a competing result between the biexciton formation, which is preferred to achieve population inversion, and the hole trapping which stymies the population inversion. An optimum amount of copper dopants enables the lowest ASE threshold of ≈7 µJ cm−2, about 8‐fold reduction from that in undoped CQWs (≈58 µJ cm−2) under sub‐nanosecond pulse excitation. Finally, a copper‐doped CQW film embedded in a vertical cavity surface‐emitting laser (VCSEL) structure yields an ultralow lasing threshold of 4.1 µJ cm−2. Exploiting optical gain from TMDCNMs may help to further boost the performance of colloidal‐based lasers.

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Purpose The purpose of this paper is to propose an adaptive neural-sliding mode-based observer for the estimation and reconstruction of unknown faults and disturbances for time-varying nonlinear systems such as aircraft, to ensure preciseness in the diagnosis of fault magnitude as well as the shape without enhancement of system complexity and cost. Fault-tolerant control (FTC) strategy based on adaptive neural-sliding mode is also proposed in the existence of faults for ensuring the stability of the faulty system. Design/methodology/approach In this paper, three strategies are presented: adaptive radial basis functions neural network (ARBFNN), conventional radial basis functions neural network (CRBFNN) and integral-chain differentiator. For the purpose of enhancement of fault diagnosis and isolation, a new sliding mode-based concept is introduced for the weight updating parameters of radial basis functions neural network (RBFNN).The main objective of updating the weight parameters adaptively is to enhance the effectiveness of fault diagnosis and isolation without increasing the computational complexities of the system. Results depict the effectiveness of the proposed ARBFNN approach in fault detection (FD) and approximation compared to CRBFNN, integral-chain differentiator and schemes existing in literature. In the second step, the FTC strategy is presented separately for each observer in the presence of unknown faults and failures for ensuring the stability of the system, which is validated on Boeing 747 100/200 aircraft. Findings The proposed adaptive neural-sliding mode approach is investigated, which depicts more effectiveness in numerous situations such as faults, disturbances and uncertainties compared to algorithms used in literature. In this paper, both the fault approximation and isolation and the fault tolerance approaches are studied. Practical implications For the enhancement of safety level as well as for avoiding any kind of damage, timely FD and fault tolerance have always had a significant role; therefore, the algorithms proposed in this research ensure the tolerance of faults and failures, which plays a vital role in practical life for avoiding any kind of damage. Originality/value In this study, a new neural-sliding mode concept is adopted for the adaptive faults approximation and reconstruction, and then the FTC algorithms are studied for each observer separately, whereas in previous studies, only the fault detection and isolation (FDI) or the fault tolerance problems were studied. Results demonstrate the effectiveness of the proposed strategy compared to the approaches given in the literature.

Carbon fabric‐reinforced polymer (CFRP) composites were incorporated with novel, environment‐friendly and, low‐cost inorganic flame‐retardant (FR) materials. Synergistic properties of potash alum (KA) and magnesium hydroxide (MH) were obtained by infusing these materials in the carbon woven fabric with diglycidial ether of bisphenol‐A epoxy (EP) matrix through resin infusion technology. Composite samples EP80%MH20% and EP80%MH15%KA5% showed Underwriter Laboratories 94 (UL94) V‐2 rating, EP80%MH10%KA10% and EP80%MH5%KA15% showed UL94 V‐1 rating, and EP80%KA20% showed UL94 V‐0 rating. Flame retardancy of EP was improved with increasing concentration of KA up to UL94 V‐0 while a high concentration of MH improved flame retardancy only up to UL94 V‐2 rating, which is not suitable for the material. Flexural modulus and flexural strength of EP were decreased with increasing concentration of KA and MH because both FR created the distance between the EP chains and hence imparted ductility. Thermogravimetric analysis showed an increase in char residue with increasing KA concentration. The char residue of EP100% (without FR materials) was 16.5 wt%, which increased to 71.2 wt% for EP80%KA20%. CFR samples were prepared with the given FR materials and it was seen that reduction in mechanical properties was compensated with the help of CFRP.