Low Dark Current and Performance Enhanced Perovskite Photodetector by Graphene Oxide as an Interfacial Layer

Organic–inorganic hybrid perovskite photodetectors are gaining much interest recently for their high performance in photodetection, due to excellent light absorption, low cost, and ease of fabrication. Lower defect density and large grain size are always favorable for efficient and stable devices. Herein, we applied the interface engineering technique for hybrid trilayer (TiO2/graphene oxide/perovskite) photodetector to attain better crystallinity and defect passivation. The graphene oxide (GO) sandwich layer has been introduced in the perovskite photodetector for improved crystallization, better charge extraction, low dark current, and enhanced carrier lifetime. Moreover, the trilayer photodetector exhibits improved device performance with a high on/off ratio of 1.3 × 104, high responsivity of 3.38 AW−1, and low dark current of 1.55 × 10−11 A. The insertion of the GO layer also suppressed the perovskite degradation process and consequently improved the device stability. The current study focuses on the significance of interface engineering to boost device performance by improving interfacial defect passivation and better carrier transport.

Optimization of Highly Efficient and Eco-Friendly EA-Substituted Tin Based Perovskite Solar Cell with Different Hole Transport Material

The perovskite absorber layer are considered highly efficient solar cell for low-cost electricity production. In this research work, an EA-substituted tin based perovskite solar cell with different hole transport material (PEDOT: PSS, Cu2O, CuI, CZTSe) have been investigated using device simulation software. The effects of absorber thickness, defect density, operating temperature, J-V characteristics, and Quantum efficiency have been considered to enhance the performance of solar cell. To confirm the feasibility and validate the study, all the simulation results were compared with reported experiment data. According to the experimental work based on (FA0.9EA0.1)0.98EDA0.01SnI3 absorber layer, maximum of 13% efficiency is achieved with PEDOT: PSS as the HTM. Whereas we have further optimized performance parameters and found the superior response (Voc=0.8851 V, Jsc=27.24 mA/cm2, FF=77.91%, and PCE=18.78%) while opted Cu2O as the hole transport material. This device structure FTO/Cu2O/(FA0.9EA0.1)0.98EDA0.01SnI3/IDL/PCBM/C60/Au provides the more efficient, reliable solution for replacing the lead-based perovskite solar cell. This study will aid researcher in a reasonable choice of materials and to predict the behavior of high performance solar cell before undergoing the fabrication process.

LaTiO2N nanopowders (NPs) with low surface defect density via nitridation of flame made NPs retaining simple perovskite structure

LaTiO2N NP synthesized from flame made LaTiO3 NP exhibits less absorption background above the optical absorption edge than that synthesized from flame made La2Ti2O7 NP, suggesting a low surface defect density.

A Game Theoretic Approach for Quality Assurance in Software Systems Using Antifragility-Based Learning Hooks

The use of software in mission critical applications poses greater quality needs. Quality assurance activities are aimed at ensuring such quality requirements of the software system. Antifragility is a property of software that increases its quality as a result of errors, faults, and attacks. Such antifragile software systems proactively accepts the errors and learns from these errors and relies on test-driven development methodology. In this article, an innovative approach is proposed which uses a fault injection methodology to perform the task of quality assurance. Such a fault injection mechanism makes the software antifragile and it gets better with the increase in the intensity of such errors up to a point. A software quality game is designed as a two-player game model with stressor and backer entities. The stressor is an error model which injects errors into the software system. The software system acts as a backer, and tries to recover from the errors. The backer uses a cheating mechanism by implementing software Learning Hooks (SLH) which learn from the injected errors. This makes the software antifragile and leads to improvement of the code. Moreover, the SLH uses a Q-Learning reinforcement algorithm with a hybrid reward function to learn from the incoming defects. The game is played for a maximum of K errors. This approach is introduced to incorporate the anti-fragility aspects into the software system within the existing framework of object-oriented development. The game is run at the end of every increment during the construction of object-oriented systems. A detailed report of the injected errors and the actions taken is output at the end of each increment so that necessary actions are incorporated into the actual software during the next iteration. This ensures at the end of all the iterations, the software is immune to majority of the so-called Black Swans. The experiment is conducted with an open source Java sample and the results are studied selected two categories of evaluation parameters. The defect related performance parameters considered are the defect density, defect distribution over different iterations, and number of hooks inserted. These parameters show much reduction in adopting the proposed approach. The quality parameters such as abstraction, inheritance, and coupling are studied for various iterations and this approach ensures considerable increases in these parameters.

Controllable morphology of Bi2S3 nanostructures formed via hydrothermal vulcanization of Bi2O3 thin-film layer and their photoelectrocatalytic performances

Bi2S3 nanostructures with various morphologies were synthesized through hydrothermal vulcanization at different sulfur precursor (thiourea) concentrations. A 100 nm thick sputter-deposited Bi2O3 thin-film layer on a fluorine-doped tin oxide glass substrate was used as a sacrificial template layer. The etching of the Bi2O3 sacrificial template layer and the regrowth of Bi2S3 crystallites during hydrothermal vulcanization produced the different Bi2S3 nanostructure morphologies. The lowest sulfur precursor concentration (0.01 M) induced the formation of Bi2S3 nanosheets, whereas the Bi2S3 nanoribbons and nanowires were formed with increased sulfur precursor concentrations of 0.03 and 0.1 M, respectively. These results indicate that sputter-deposited Bi2O3 thin-film layers can be effectively used to form low-dimensional Bi2S3 crystals with controllable morphologies. Among the various Bi2S3 samples, the Bi2S3 nanosheets exhibited superior photoactive ability. The higher active surface area, surface defect density, light absorption capacity, and photo-induced charge separation ability of Bi2S3 nanosheets explain their superior photoelectrocatalytic degradation ability of rhodamine B dyes.

Quantitative Evidence for the Dependence of Highly Crystalline Single Wall Carbon Nanotube Synthesis on the Growth Method

We present a study quantitatively demonstrating that the method of synthesis (gas phase, fixed bed, non-fixed bed) represents a determining factor in the level of crystallinity in growing single wall carbon nanotubes (SWCNTs). Using far infrared spectroscopy, the “effective length” (associated with the level of crystallinity) was estimated for CNTs grown using various synthetic methods (lab-produced and supplemented by commercially purchased SWCNTs) as a metric for crystallinity (i.e., defect density). Analysis of the observed “effective lengths” showed that the SWCNTs fell into two general groups: long and short (high and low crystallinity) synthesized by gas-phase methods and all other supported catalyst methods, respectively. Importantly, the “long” group exhibited effective lengths in the range of 700–2200 nm, which was greater than double that of the typical values representing the “short” group (110–490 nm). These results highlight the significant difference in crystallinity. We interpret that the difference in the crystallinity stemmed from stress concentration at the nanotube-catalyst interface during the growth process, which originated from various sources of mismatch in growth rates (e.g., vertically aligned array) as well as impact stress from contact with other substrates during fluidization or rotation. These results are consistent with well-accepted belief, but now are demonstrated quantitatively.

Theoretical study of highly efficient CH3NH3SnI3 based Perovskite solar cell with CuInS2 quantum dot

Simulation studies have been carried out for n-i-p perovskite solar cell (PSC) structure i.e. ITO/SnO2/CH3NH3PbI3/CuInS2/Au. We have considered this cell as our primary structure and is simulated using Solar Cell Capacitance Simulator (SCAPS-1D) software. Here, the CuInS2 quantum dot acts as an inorganic hole transporting layer. Further, the use of the CuInS2 quantum dot in PSCs has been explored by simulating twenty different cell structures. These perovskite solar cells are based on recently used absorber layers, i.e., MASnI3, FAPbI3, and (FAPbI3)0.97(MAPbBr1.5Cl1.5)0.03, and electron transporting layers, i.e., SnO2, TiO2, ZnO, C60, and IGZO. The performance of all structures has been optimized by varying the thickness of the absorber layers and ETLs. The cell structure, ITO/SnO2/CH3NH3SnI3/CuInS2/Au, has been found to exhibit highest power conversion efficiency of 21.79% as compared to other cells. Investigations have also been carried out to analyze the effect of defect density in the absorber layer and the interface of the cell structure. In addition, the cell performance has been ascertained by examining the impact of operating temperature, metal contact work function and that of resistance in series as well as in parallel. The simulation results of our primary cell structure are found to be in good agreement with the recent experimental study.

A Novel Artificial Neuron-Like Gas Sensor Constructed from CuS Quantum Dots/Bi2S3 Nanosheets

Real-time rapid detection of toxic gases at room temperature is particularly important for public health and environmental monitoring. Gas sensors based on conventional bulk materials often suffer from their poor surface-sensitive sites, leading to a very low gas adsorption ability. Moreover, the charge transportation efficiency is usually inhibited by the low defect density of surface-sensitive area than that in the interior. In this work, a gas sensing structure model based on CuS quantum dots/Bi2S3 nanosheets (CuS QDs/Bi2S3 NSs) inspired by artificial neuron network is constructed. Simulation analysis by density functional calculation revealed that CuS QDs and Bi2S3 NSs can be used as the main adsorption sites and charge transport pathways, respectively. Thus, the high-sensitivity sensing of NO2 can be realized by designing the artificial neuron-like sensor. The experimental results showed that the CuS QDs with a size of about 8 nm are highly adsorbable, which can enhance the NO2 sensitivity due to the rich sensitive sites and quantum size effect. The Bi2S3 NSs can be used as a charge transfer network channel to achieve efficient charge collection and transmission. The neuron-like sensor that simulates biological smell shows a significantly enhanced response value (3.4), excellent responsiveness (18 s) and recovery rate (338 s), low theoretical detection limit of 78 ppb, and excellent selectivity for NO2. Furthermore, the developed wearable device can also realize the visual detection of NO2 through real-time signal changes.