RESUMEN
Halide perovskite-based resistive switching memory (memristor) has potential in an artificial synapse. However, an abrupt switch behavior observed for a formamidinium lead triiodide (FAPbI3)-based memristor is undesirable for an artificial synapse. Here, we report on the δ-FAPbI3/atomic-layer-deposited (ALD)-SnO2 bilayer memristor for gradual analogue resistive switching. In comparison to a single-layer δ-FAPbI3 memristor, the heterojunction δ-FAPbI3/ALD-SnO2 bilayer effectively reduces the current level in the high-resistance state. The analog resistive switching characteristics of δ-FAPbI3/ALD-SnO2 demonstrate exceptional linearity and potentiation/depression performance, resembling an artificial synapse for neuromorphic computing. The nonlinearity of long-term potentiation and long-term depression is notably decreased from 12.26 to 0.60 and from -8.79 to -3.47, respectively. Moreover, the δ-FAPbI3/ALD-SnO2 bilayer achieves a recognition rate of ≤94.04% based on the modified National Institute of Standards and Technology database (MNIST), establishing its potential in an efficient artificial synapse.
RESUMEN
The importance of light management for perovskite solar cells (PSCs) has recently been emphasized because their power conversion efficiency approaches their theoretical thermodynamic limits. Among optical strategies, anti-reflection (AR) coating is the most widely used method to reduce reflectance loss and thus increase light-harvesting efficiency. Monolayer MgF2is a well-known AR material because of its optimal refractive index, simple fabrication process, and physical and chemical durabilities. Nevertheless, quantitative estimates of the improvement achieved by the MgF2AR layer are lacking. In this study, we conducted theoretical and experimental evaluations to assess the AR effect of MgF2on the performance of formamidinium lead-triiodide PSCs. A sinusoidal tendency to enhance the short-circuit current density (JSC) was observed depending on the thickness, which was attributed to the interference of the incident light. A transfer matrix method-based simulation was conducted to calculate the optical losses, demonstrating the critical impact of reflectance loss on theJSCimprovement. The predictedJSCs values, depending on the perovskite thickness and the incident angle, are also presented. The combined use of experimental and theoretical approaches offers notable advantages, including accurate interpretation of photocurrent generation, detailed optical analysis of the experimental results, and device performance predictions under unexplored conditions.
RESUMEN
Cations with suitable sizes to occupy an interstitial site of perovskite crystals have been widely used to inhibit ion migration and promote the performance and stability of perovskite optoelectronics. However, such interstitial doping inevitably leads to lattice microstrain that impairs the long-range ordering and stability of the crystals, causing a sacrificial trade-off. Here, we unravel the evident influence of the valence states of the interstitial cations on their efficacy to suppress the ion migration. Incorporation of a trivalent neodymium cation (Nd3+) effectively mitigates the ion migration in the perovskite lattice with a reduced dosage (0.08%) compared to a widely used monovalent cation dopant (Na+, 0.45%). The photovoltaic performances and operational stability of the prototypical perovskite solar cells are enhanced with a trace amount of Nd3+ doping while minimizing the sacrificial trade-off.
RESUMEN
Metal halide perovskite solar cells (PSCs) are infamous for their batch-to-batch and lab-to-lab irreproducibility in terms of stability and performance. Reproducible fabrication of PSCs is a critical requirement for market viability and practical commercialization. PSC irreproducibility plagues all levels of the community; from institutional research laboratories, start-up companies, to large established corporations. In this work, the critical function of atmospheric humidity to regulate the crystallization and stabilization of formamidinium lead triiodide (FAPbI3) perovskites is unraveled. It is demonstrated that the humidity content during processing induces profound variations in perovskite stoichiometry, thermodynamic stability, and optoelectronic quality. Almost counterintuitively, it is shown that the presence of humidity is perhaps indispensable to reproduce phase-stable and efficient FAPbI3-based PSCs.
RESUMEN
Chemical bath deposition (CBD) is widely used to deposit tin oxide (SnOx ) as an electron-transport layer in perovskite solar cells (PSCs). The conventional recipe uses thioglycolic acid (TGA) to facilitate attachments of SnOx particles onto the substrate. However, nonvolatile TGA is reported to harm the operational stability of PSCs. In this work, a volatile oxalic acid (OA) is introduced as an alternative to TGA. OA, a dicarboxylic acid, functions as a chemical linker for the nucleation and attachment of particles to the substrate in the chemical bath. Moreover, OA can be readily removed through thermal annealing followed by a mild H2 O2 treatment, as shown by FTIR measurements. Synergistically, the mild H2 O2 treatment selectively oxidizes the surface of the SnOx layer, minimizing nonradiative interface carrier recombination. EELS (electron-energy-loss spectroscopy) confirms that the SnOx surface is dominated by Sn4+ , while the bulk is a mixture of Sn2+ and Sn4+ . This rational design of a CBD SnOx layer leads to devices with T85 ≈1500 h, a significant improvement over the TGA-based device with T80 ≈250 h. The champion device reached a power conversion efficiency of 24.6%. This work offers a rationale for optimizing the complex parameter space of CBD SnOx to achieve efficient and stable PSCs.
RESUMEN
Sn-based perovskite light-emitting diodes (PeLEDs) have emerged as promising alternatives to Pb-based PeLEDs with their rapid increase in performance owing to the various research studies on inhibiting Sn oxidation. However, the absence of defect passivation strategies for Sn-based perovskite LEDs necessitates further research in this field. We performed systematic studies to investigate the design rules for defect passivation agents for Sn-based perovskites by incorporating alkali/multivalent metal salts with various cations and anions. From the computational and experimental analyses, sodium trifluoromethanesulfonate (NaTFMS) was found to be the most effective passivation agent for PEA2SnI4 films among the explored candidate agents owing to favorable reaction energetics to passivate iodide Frenkel defects. Consequently, the incorporation of NaTFMS facilitates the formation of uniform films with relatively large crystals and reduced Sn4+. The NaTFMS-containing PEA2SnI4 PeLEDs demonstrate an improved luminance of 138.9 cd/m2 and external quantum efficiency (EQE) of 0.39% with an improved half-lifetime of more than threefold. This work provides important insight into the design of defect passivation agents for Sn-based perovskites.
RESUMEN
With recent advances in biotechnology and sequencing technology, the microbial community has been intensively studied and discovered to be associated with many chronic as well as acute diseases. Even though a tremendous number of studies describing the association between microbes and diseases have been published, text mining methods that focus on such associations have been rarely studied. We propose a framework that combines machine learning and natural language processing methods to analyze the association between microbes and diseases. A hierarchical long short-term memory network was used to detect sentences that describe the association. For the sentences determined, two different parse tree-based search methods were combined to find the relation-describing word. The ensemble model of constituency parsing for structural pattern matching and dependency-based relation extraction improved the prediction accuracy. By combining deep learning and parse tree-based extractions, our proposed framework could extract the microbe-disease association with higher accuracy. The evaluation results showed that our system achieved an F-score of 0.8764 and 0.8524 in binary decisions and extracting relation words, respectively. As a case study, we performed a large-scale analysis of the association between microbes and diseases. Additionally, a set of common microbes shared by multiple diseases were also identified in this study. This study could provide valuable information for the major microbes that were studied for a specific disease. The code and data are available at https://github.com/DMnBI/mdi_predictor .