Two-step Sequential Method Research Articles

Enhanced Efficiency and Intrinsic Stability of Wide-Bandgap Perovskite Solar Cells Through Dimethylamine-Based Cation Engineering.

High efficiency and stable wide-bandgap (WBG) perovskite solar cells (PSCs) are crucial for the development of perovskite-based tandem solar cells. However, the efficiency and stability of WBG PSCs are compromised by significant phase segregation and surface defects. In this study, we introduce a cation engineering strategy for WBG perovskite, employing a two-step sequential method that incorporates dimethylamine hydroiodide (DMAI) into the lead halide complex during the initial step. The addition of DMAI modifies the crystal structure and grain growth of the perovskite film, leading to improved crystal quality, reduced photo-induced halide segregation, decreased defect density, and enhanced charge carrier mobility. Consequently, we achieved a champion power conversion efficiency (PCE) of 21.9 % for 1.68 eV WBG PSCs. Furthermore, the stability of PSCs based on DMA-doped perovskite was significantly improved. After 1500 hours of exposure to ambient air, the unencapsulated device retained an impressive 80.6 % of its initial efficiency. This result highlights the substantial potential for stable and efficient WBG PSCs.

Extending Shelf-Life of Two-Step Method Precursor Solutions through Targeted Regulation for Highly Efficient and Reproducible Perovskite Solar Cells.

Precursor solution aging process can cause significant influence on the photovoltaic performance of perovskite solar cells (PVSCs). Notably, we first observe that the aging phenomenon is more severe in the precursor of two-step sequential method compared to that in one-step method due to that the protic solvent isopropanol facilitates amine-cation side reaction and iodide ions oxidation. Herein, we report a novel approach for selectively stabilizing both organic amine salt and lead iodide (PbI2) precursor solutions in two-step method. The introduction of benzene-1,3-dithiol into organic amine salt solution can mitigate amine-cation side reactions due to the formation of an acidic and reducing environment. Simultaneously, decamethylferrocene (FcMe10/FcMe+ 10) pair can act as a redox shuttle in PbI2 solution to concurrently oxidize Pb0 and reduce I2 in cyclic manner. Consequently, the PVSCs device fabricated from ameliorative precursor solutions demonstrates superior power conversion efficiency of 25.31 %, retaining 95 % of its efficiency after 21 days of solution aging. Moreover, the unencapsulated devices maintain 85 % of primitive efficiency for 1500 h at maximum power point tracking under continuous illumination. This work establishes a fundamental guidance and scientific direction for the stabilization of two-step perovskite precursor solutions.

Enhanced stability of FA-based perovskite: Rare-earth metal compound EuBr2 doping

It is highly desirable to enhance the long-term stability of perovskite solar cells (PSCs) so that this class of photovoltaic cells can be effectively used for the commercialization purposes. In this contribution, attempts have been made to use the two-step sequential method to dope EuBr2 into FAMAPbI3 perovskite to promote the stability. It is shown that the device durability at 85 °C in air with RH of 20%–40% is improved substantially, and simultaneously the champion device efficiency of 23.04% is achieved. The enhancement in stability is attributed to two points: (i) EuBr2 doping effectively inhibits the decomposition and α–δ phase transition of perovskite under ambient environment, and (ii) EuBr2 aggregates in the oxidized format of Eu(BrO3)3 at perovskite grain boundaries and surface, hampering humidity erosion and mitigates degradation through coordination with H2O.

RbPbI3 Seed Embedding in PbI2 Substrate Tailors the Facet Orientation and Crystallization Kinetics of Perovskites.

High power conversion efficiencies (PCEs) in perovskite solar cells (PSCs) have always been awe-inspiring, but perovskite films scalability is an exacting precondition for PSCs commercial deployment, generally unachievable through the antisolvent technique. On the contrary, in the two-step sequential method, the perovskite's uncontrolled crystallization and unnecessary PbI2 residue impede the device's performance. These two issues motivated to empower the PbI2 substrate with orthorhombic RbPbI3 crystal seeds, which act as grown nuclei and develop orientated perovskites lattice stacks, improving the perovskite films morphologically and reducing the PbI2 content in eventual perovskite films. Thence, achieving a PCE of 24.17% with suppressed voltage losses and an impressive life span of 1140h in the open air.

Formamidinium Lead Iodide Perovskite Thin Films Formed by Two-Step Sequential Method: Solvent-Morphology Relationship.

Thin films made of formamidinium lead iodide (FAPbI3) perovskites prepared by a two-step sequential deposition method using various solvents for formamidinium iodide (FAI) - isopropanol, n-butanol and tert-butanol, were studied with the aim of finding a correlation between morphology and solvent properties to improve film quality. They were characterized by X-ray diffraction (XRD) and scanning electron microscopy (SEM) and their photophysical properties were studied by means of absorption and photoluminescence (PL) spectroscopies. XRD patterns, absorption and PL spectra proved α-phase formation for all selected solvents. An excessive amount of PbI2 found in perovskite films prepared with n-butanol indicates incomplete conversion. Thin film morphology, such as grain and crystallite size, depended on the solvent. Using tert-butanol, thin films with a very large grain size of up to several micrometers and with preferred crystallite orientation were fabricated. The grain size increased as follows: 0.2-0.5, 0.2-1 and 2-5 µm for isopropanol, n-butanol and tert-butanol, respectively. A correlation between the grain size and viscosity, electric permittivity and polarizability of the solvent could be considered. Our results, including fabrication of perovskite films with large grains and fewer grain boundaries, are important and of interest for many optoelectronic applications.

Anchoring Vertical Dipole to Enable Efficient Charge Extraction for High-Performance Perovskite Solar Cells.

Perovskite solar cells (PSCs) via two‐step sequential method have received great attention in recent years due to their high reproducibility and low processing costs. However, the relatively high trap‐state density and poor charge carrier extraction efficiency pose challenges. Herein, highly efficient and stable PSCs via a two‐step sequential method are fabricated using organic—inorganic (OI) complexes as multifunctional interlayers. In addition to reduce the under‐coordinated Pb2+ ions related trap states by forming interactions with the functional groups, the complexes interlayer tends to form dipole moment which can enhance the built‐in electric field, thus facilitating charge carrier extraction. Consequently, with rational molecular design, the resulting devices with a vertical dipole moment that parallels with the built‐in electric field yield a champion efficiency of 23.55% with negligible hysteresis. More importantly, the hydrophobicity of the (OI) complexes contributes to an excellent ambient stability of the resulting device with 91% of initial efficiency maintained after 3000 h storage.

Synergistic effect of MACl and DMF towards efficient perovskite solar cells

Abstract The performance of perovskite solar cells (PSCs) is extremely dependent on morphology and crystallinity of perovskite film. One of the most effective methods to achieve high performance perovskite solar cells has been to introduce additives that serve as dopants, crystallization or passivation agents. Herein a facile strategy by introducing methylammonium chloride (MACl) and polar solvent N,N-Dimethylformamide (DMF) as co-additives in two-step sequential method is proposed to realize high quality perovskite film. It is demonstrated that DMF facilitates methylammonium iodide (MAI) penetrating easily into PbI2 layer to form highly crystalized perovskite film with uniform morphology which is essential to achieve high VOC. While MACl induces MAPbI3 to crystallize in a pure α-phase and suppress non-photovoltaic phase, which guarantees high FF. Pure α-phase perovskite film with uniform morphology can be achieved by adopting MACl and DMF together and the corresponding solar cell illustrates a power conversion efficiency (PCE) of 19.02% with substantially promoted durability. Moreover, A VOC as high as 1.181 V is succeeded for MAPbI3 based solar cell benefiting from the synergistic effect.

Dynamic auto-weighted multi-view co-clustering

To exploit the complementary information of multi-view data, many weighted multi-view clustering methods have been proposed and have demonstrated impressive performance. However, most of these methods learn the view weights by introducing additional parameters, which can not be easily obtained in practice. Moreover, they all simply apply the learned weights on the original feature representation of each view, which may deteriorate the clustering performance in the case of high-dimensional data with redundancy and noise. In this paper, we extend information bottleneck co-clustering into a multi-view framework and propose a novel dynamic auto-weighted multi-view co-clustering algorithm to learn a group of weights for views with no need for extra weight parameters. By defining the new concept of the discrimination-compression rate, we quantify the importance of each view by evaluating the discriminativeness of the compact features (i.e., feature-wise clusters) of the views. Unlike existing weighted methods that impose weights on the original feature representations of multiple views, we apply the learned weights on the discriminative ones, which can reduce the negative impact of noisy features in high-dimensional data. To solve the optimization problem, a new two-step sequential method is designed. Experimental results on several datasets show the advantages of the proposed algorithm. To our knowledge, this is the first work incorporating weighting scheme into multi-view co-clustering framework.

An evaluation of machine-learning for predicting phenotype: studies in yeast, rice, and wheat

In phenotype prediction the physical characteristics of an organism are predicted from knowledge of its genotype and environment. Such studies, often called genome-wide association studies, are of the highest societal importance, as they are of central importance to medicine, crop-breeding, etc. We investigated three phenotype prediction problems: one simple and clean (yeast), and the other two complex and real-world (rice and wheat). We compared standard machine learning methods; elastic net, ridge regression, lasso regression, random forest, gradient boosting machines (GBM), and support vector machines (SVM), with two state-of-the-art classical statistical genetics methods; genomic BLUP and a two-step sequential method based on linear regression. Additionally, using the clean yeast data, we investigated how performance varied with the complexity of the biological mechanism, the amount of observational noise, the number of examples, the amount of missing data, and the use of different data representations. We found that for almost all the phenotypes considered, standard machine learning methods outperformed the methods from classical statistical genetics. On the yeast problem, the most successful method was GBM, followed by lasso regression, and the two statistical genetics methods; with greater mechanistic complexity GBM was best, while in simpler cases lasso was superior. In the wheat and rice studies the best two methods were SVM and BLUP. The most robust method in the presence of noise, missing data, etc. was random forests. The classical statistical genetics method of genomic BLUP was found to perform well on problems where there was population structure. This suggests that standard machine learning methods need to be refined to include population structure information when this is present. We conclude that the application of machine learning methods to phenotype prediction problems holds great promise, but that determining which methods is likely to perform well on any given problem is elusive and non-trivial.

A two-step sequential method for device-free localization using wireless sensor networks

The device-free localization that locates an uncooperative target by analyzing its shadowing effect on wireless links in low-cost wireless sensor networks has become an attractive technique. Due to its ability of realizing localization without the need of equipping the target with any wireless device, device-free localization has lots of potential applications ranging from emergency rescue to patient monitoring. However, since the change of the received signal strength on links easily becomes unpredictable due to all kinds of dynamical factors, device-free localization is still a challenging problem. In this article, we propose a novel two-step sequential device-free localization method to enhance device-free localization performance. The sequential device-free localization first utilizes the geometric-based method to quickly obtain the coarse positioning result with low storage and computational requirements, and then exploits the location information of selected wireless nodes to improve the accuracy of the geometric-based localization result based on the optimization-based method, which ensures its robust performance. Experimental results in indoor and outdoor scenarios demonstrate the effectiveness and robustness of the proposed scheme.

Machines group and load balancing: an industrial case

Abstract This paper presents a problem from the textile industry wich consists of designing the layout of a knitting workshop. Machine groups are created to balance the operators’ workload and the layout is designed to create group cells. New models are proposed in this paper with two approaches. The first is an aggregated model and the second one proposes a two-step sequential method. The two models were implemented using CPLEX-ILOG software and compared to provide preliminary results. Future directions for research are outlined to further address this issue.

Impact of water-based TiO2 nanofluid on heat transfer under transition flow

This paper reports the heat transfer behavior of TiO2/H2O nanofluids with varying concentrations have been investigated. The nanofluids have remarkable potential for realistic application than the currently prevailing methods of heat transfer enhancement. The major focus of this paper is to the heat transfer behavior of TiO2/H2O nanofluids by varying the volume concentration using a two-step sequential method. The results indicate that the nusselt number is augmented with an increase in concentration of nanoparticles. Also, it has been found that the nusselt number enhancement was 16.3%. There was greater enhancement in Nusselt number was observed than in friction factor.

Enhanced Crystallinity and Performance of CH3NH3PbI3 Thin Film Prepared by Controlling Hot CH3NH3I Solution Onto Evaporated PbI2 Nanocrystal

In this paper, a facile, efficient, and reduplicative modified two-step sequential method for the fabrication of CH 3NH3PbI3 thin film was reported. First, a uniform, homogeneous, and small-sized PbI 2 nanocrystal was deposited via vapor evaporation. Then, a hot CH3NH3I solution with a different temperature was spin coated onto the prepared PbI2 thin film to form perovskite thin films in a short time. The results demonstrated that homogeneous, compact, complete surface coverage, high crystallinity, and pinhole-free CH3NH3PbI3 thin film was prepared by CH3NH3I solution with 80 °C spin coating on the evaporated PbI2 nanocrystal.

Cooling-water system optimisation with a novel two-step sequential method

In cooling water systems, conventional cooler networks usually operate in a parallel configuration and traditional pump networks consist of pumps which are installed only on the main supply pipeline. However, a series-parallel configuration of coolers can substantially increase the cooling-water return temperature and reduce its flow-rate as well as the efficiency of the cooling tower, but the pressure drop along pipes would increase correspondingly. This paper presents a novel two-step sequential methodology for the optimisation of cooling-water system (CWS). The first step is to use a thermodynamic model to obtain the optimal cooler network. In the second step, the hydraulic model is established to obtain the optimal pump network with auxiliary pumps installed in parallel branch pipes. The proposed model can identify the optimal distribution of cooling water within the network and the optimal installation locations and pressure head of pumps required for CWS. A case study is presented to demonstrate the effectiveness of the proposed methodology. It can save up to 23.3% of the cooler network cost and additional 11% of the pump network cost after optimisation.

Demand and Roughness Estimation in Water Distribution Systems

To provide more accurate estimates and account for associated uncertainties, a parameter estimation methodology for water distribution systems (WDSs) that combines demand and parameter estimation processes is proposed. A two-step sequential method for dual estimation of demand and roughness coefficient is presented based on a weighted least-squares scheme using field measurements of pipe flow rates and nodal pressure heads under multiple demand conditions. The uncertainties in the estimated variables and resulting nodal pressure predictions are quantified in terms of confidence limits using the first-order second moment method. The algorithm is applied to two network systems including a midsized real WDS. The two-step sequential model provides accurate and precise estimates while joint estimation provides poor estimates.

Laser control of singlet–triplet transition in molecules

Abstract A two-step sequential method with the π pulse and the stimulated Raman adiabatic passage (STIRAP) pulse sequence is applied to the laser control of the singlet–triplet transition in Cl 2 molecule. We find four reaction pathways from the singlet ground state to the triplet excited state by investigating the spin–orbit coupling and the transition dipole moment calculated with gamess software package, and we realize the highly effective singlet–triplet transition by STIRAP method with the spin-mixed intermediate state on picosecond time scale.

A two-stage enzymatic method for determination of uric acid and hypoxanthine/xanthine

A method is described by which plasma or serum concentrations of uric acid and hypoxanthine/xanthine may be determined in a two-step sequential method. The principle of the method is that hydrogen peroxide, liberated by the action of uricase and xanthine oxidase on their substrates, is coupled to the production of a coloured product.