A Thienothiophene-Based Cation Treatment Allows Semitransparent Perovskite Solar Cells with Improved Efficiency and Stability

Perovskite surface treatment with additives has been reported to improve charge extraction, stability, and/or surface passivation. In this study, treatment of a 3D perovskite ((FAPbI₃)_1−x(MAPbBr₃)_x) layer with a thienothiophene-based organic cation (TTMAI), synthesized in this work, is investigated. Detailed analyses reveal that a 2D (n = 1) or quasi-2D layer does not form on the PbI₂-rich surface 3D perovskite. TTMAI-treated 3D perovskite solar cells (PSCs) fabricated in this study show improved fill factors, providing an increase in their power conversion efficiencies (PCEs) from 17% to over 20%. It is demonstrated that the enhancement is due to better hole extraction by drift-diffusion simulations. Furthermore, thanks to the hydrophobic nature of the TTMAI, PSC maintains 82% of its initial PCE under 15% humidity for over 380 h (the reference retains 38%). Additionally, semitransparent cells are demonstrated reaching 17.9% PCE with treated 3D perovskite, which is one of the highest reported efficiencies for double cationic 3D perovskites. Moreover, the semitransparent 3D PSC (TTMAI-treated) maintains 87% of its initial efficiency for six weeks (>1000 h) when kept in the dark at room temperature. These results clearly show that this study fills a critical void in perovskite research where highly efficient and stable semitransparent perovskite solar cells are scarce.     

An adaptive density-weighted contrast enhancement filter for mammographic breast mass detection

Presents a novel approach for segmentation of suspicious mass regions in digitized mammograms using a new adaptive density-weighted contrast enhancement (DWCE) filter in conjunction with Laplacian-Gaussian (LG) edge detection. The DWCE enhances structures within the digitized mammogram so that a simple edge detection algorithm can be used to define the boundaries of the objects. Once the object boundaries are known, morphological features are extracted and used by a classification algorithm to differentiate regions within the image. This paper introduces the DWCE algorithm and presents results of a preliminary study based on 25 digitized mammograms with biopsy proven masses. It also compares morphological feature classification based on sequential thresholding, linear discriminant analysis, and neural network classifiers for reduction of false-positive detections.     

A novel design procedure for tractor clutch fingers by using optimization and response surface methods

This paper presents a methodology for re-designing a failed tractor transmission component subjected to cyclic loading. Unlike other vehicles, tractors cope with tough working conditions. Thus, it is necessary to re-design components by using modern optimization techniques. To extend their service life, we present a design methodology for a failed tractor clutch power take-off finger. The finger was completely re-designed using topology and shape optimization approach. Stress-life based fatigue analyses were performed. Shape optimization and response surface methodology were conducted to obtain optimum dimensions of the finger. Two design parameters were selected for the design of experiment method and 15 cases were analyzed. By using design of the experiment method, three responses were obtained: Maximum stresses, mass, and displacement depending on the selected the design parameters. After solving the optimization problem, we achieved a maximum stress and mass reduction of 14% and 6%, respectively. The stiffness was improved up to 31.6% compared to the initial design.     

Comparison of analytical,finite element and neural network methods to study magnetic shielding

Three types of methods are studied to calculate the shielding efficiency of a cylindrical ferromagnetic shield in Fe–Si steel, i.e. an analytical method, a finite element method and a neural network method. The methods take into account nonlinear hysteretic behaviour in the shield. All of the methods are compared with each other and with measurements: the shielding factor is computed and measured for several shield radii, thicknesses, field amplitudes and frequencies. The mean absolute error rates are calculated for AM, FEM and NN to be 9.16%, 6.45% and 5.54%, respectively. The analytical model is very fast and accurate for materials with mild nonlinearity. In case of highly nonlinear material and a strongly non-uniform field distribution in the shield, the modeling of the nonlinear behavior in the analytical model is not accurate. The FEM is very accurate for all considered shielding configurations if the mesh density is well chosen, but its evaluation time is high. The neural network has the disadvantage that it should be trained. It is, however, a fast method with the additional advantage that it can be used without any knowledge of the shield geometry and material properties if the training is based on measurements.     

Tunable fluorescent and antimicrobial properties of poly(vinyl amine) affected by the acidic or basic hydrolysis of poly(N-vinylformamide)

Synthesis of poly(N-vinylformamide) (PNVF) and its subsequent hydrolysis to convert it to poly(vinyl amine) (PVAm) were performed. Kinetics of acidic and basic hydrolysis of poly(N-vinylformamide) (PNVF), and products of hydrolysis were investigated by using Fourier transform infrared, size exclusion chromatography, ¹H NMR, and ¹³C NMR spectroscopies, and thermogravimetric analysis. It was observed that amide groups did not completely transform into amine groups by acidic hydrolysis of PNVF while the conversion of amides into amine groups via basic hydrolysis of PNVF was complete in 12 h, as confirmed by spectroscopic measurements. Results of extensive characterization revealed significant structural and conformational differences between acidic and basic hydrolysis products. Fluorescence spectroscopy was used for the first time to follow the conversion of amide groups into amine groups. The fluorescence intensity of PVAm obtained from basic hydrolysis of PVNF showed significant increase with amide/amine conversion. Finally, PVAm obtained from acidic hydrolysis of PNVF demonstrated potent antimicrobial activity, 10–20 times more, against common pathogens for example, C. albicans as fungal strain and E. coli, S. aureus, B. subtilis, and P. aeruginosa as bacterial strains as compared to PVAm obtained from basic hydrolysis.     

Design and analysis of an integrated concentrated solar and wind energy system with storage

This study analyzes a renewable energy‐driven innovative multigeneration system, in which wind and solar energy sources are utilized in an efficient way to generate several useful commodities such as hydrogen, oxygen, desalted water, space cooling, and space heating along with electricity. A 1‐km² heliostat field is considered to concentrate the solar light onto a spectrum splitter, where the light spectrum is separated into two portions as reflected and transmitted to be used as the energy source in the concentrated solar power (CSP) and concentrated photovoltaics (CPV) receivers, respectively. As such, CSP and CPV systems are integrated. Wind energy is proposed for generating electricity (146 MW) or thermal energy (138 MW) to compensate the energy need of the multigeneration system when there is insufficient solar energy. In addition, multiple commodities, 46 MW of electricity, 12 m³/h of desalted water, and 69 MW of cooling, are generated using the Rankine cycle and the rejected heat from its condenser. Further, the heat generated on CPV cells is recovered for efficient photovoltaic conversion and utilized in the space heating (34 MW) and proton exchange membrane (PEM) electrolyzer (239 kg/h) for hydrogen production. The energy and exergy efficiencies of the overall system are calculated as 61.3% and 47.8%, respectively. The exergy destruction rates of the main components are presented to identify the potential improvements of the system. Finally, parametric studies are performed to analyze the effect of changing parameters on the exergy destruction rates, production rates, and efficiencies.     

Catalytic activity of metal-free amine-modified dextran microgels in hydrogen release through methanolysis of NaBH4

Polymeric microgels were prepared from dextran (Dex) by crosslinking linear natural polymer dextran with divinyl sulfone (DVS) with a surfactant-free emulsion technique resulting in high gravimetric yield of 78.5 ± 5.3% with wide size distribution. Dex microgels were chemically modified, and then used as catalyst in the methanolysis of NaBH₄ to produce H₂. The chemical modification of Dex microgel was done on epichlorohydrin (ECH)-reacted Dex microgels with ethylenediamine (EDA), diethylenetriamine (DETA), and triethylenetetraamine (TETA) in dimethylformamide (DMF) at 90°C for 12 hours. The modified dextran-TETA microgels were protonated using treatment with hydrochloric acid (HCl) and m-Dex microgels-TETA-HCl was found to be a very efficient catalyst for methanolysis of NaBH₄ to produce H₂. The effects of reaction temperature and NaBH₄ concentration on H₂ generation rates were investigated and m-Dex microgels-TETA-HCl catalyst possessed excellent catalytic performances with 100% conversion and 80% activity at end of 10 consecutive uses and was highly re-generatable with simple HCl treatment. Interestingly, m-Dex microgels-TETA-HCl catalyst can catalyze NaBH₄ methanolysis reaction in a mild temperature range 0 to 35°C with E_a value of 30.72 kJ/mol and in subzero temperature range, −20 to 0°C with E_a value of 32.87 kJ/mol, which is comparable with many catalysts reported in the literature.     

Automatic detection of epileptiform events in EEG by a three-stage procedure based on artificial neural networks

This paper introduces a three-stage procedure based on artificial neural networks for the automatic detection of epileptiform events (EVs) in a multichannel electroencephalogram (EEG) signal. In the first stage, two discrete perceptrons fed by six features are used to classify EEG peaks into three subgroups: 1) definite epileptiform transients (ETs); 2) definite non-ETs; and 3) possible ETs and possible non-ETs. The pre-classification done in the first stage not only reduces the computation time but also increases the overall detection performance of the procedure. In the second stage, the peaks falling into the third group are aimed to be separated from each other by a nonlinear artificial neural network that would function as a postclassifier whose input is a vector of 41 consecutive sample values obtained from each peak. Different networks, i.e., a backpropagation multilayer perceptron and two radial basis function networks trained by a hybrid method and a support vector method, respectively, are constructed as the postclassifier and then compared in terms of their classification performances. In the third stage, multichannel information is integrated into the system for contributing to the process of identifying an EV by the electroencephalographers (EEGers). After the integration of multichannel information, the overall performance of the system is determined with respect to EVs. Visual evaluation, by two EEGers, of 19 channel EEG records of 10 epileptic patients showed that the best performance is obtained with a radial basis support vector machine providing an average sensitivity of 89.1%, an average selectivity of 85.9%, and a false detection rate (per hour) of 7.5.     

Preparation and Characterization of Bi-metallic and Tri-metallic Metal Organic Frameworks Based on Trimesic Acid and Co(II), Ni(II), and Cu(II) Ions

Trimesic acid-M1(II):M2(II) (M1,2(II)=M(II)=Co(II), Ni(II) and Cu(II)) bi-metallic or tri-metallic organic frameworks (MOFs) were synthesized by the reaction of trimesic acid (H3BTC) ligand with the corresponding MCl₂nH₂O aqueous solutions. Here, bi- and tri-metallic MOF preparations were demonstrated by using H3BTC as an organic linker, with dual metal ion mixtures at different mole ratios such as Co(II):Ni(II), Ni(II):Cu(II), and Cu(II):Co(II) as metal ion sources in the synthesis of bi-metallic MOFs, and the triple metal ion mixture of Co(II):Ni(II):Cu(II) as the metal ion source in the synthesis of tri-metallic MOFs. The bi- or tri-metallic MOFs were characterized via the Brunauer–Emmett–Teller method, thermogravimetric analyzer (TGA), and magnetic susceptibility measurements with the Gouy method, FT-IR spectroscopy, and electronic spectral studies. The results revealed that the H3BTC MOFs have octahedral and distorted octahedral arrangement around the metal ions, and the d–d transition was not observed in the complex. It was further found that all the prepared MOFs contain water molecules confirmed by Fourier transform infrared (FT-IR) and TGA analyses. The FT-IR spectra of the MOF complexes were characterized by the appearance of a broad band in the region of 3454–3300 cm^?1 due to the ν(-OH) of the coordinated water; therefore, the location of the two water molecules was assumed to be inside the complex structure. Remarkably, the synthesized bi-metallic MOFs had unique and distinct colors depending on the amounts of metal ions used in the feed, implying that these bi-metallic MOFs with tunable M1(II) and M2(II) ratios offer great potential in the design of color-coded materials for use as sensors.