Search Results (36)

Proton exchange membrane fuel cells (PEMFCs) are considered promising solutions to address global energy and environmental challenges. This is largely due to their high efficiency in energy transformation, low emission of pollutants, quick responsiveness, and suitable operating conditions. However, their widespread application is limited by high cost, limited durability and system complexity. To maintain system reliability and optimize cost-effectiveness, it is essential to predict the remaining operational lifespan of PEMFC systems with precision. This study introduces a prediction framework integrating a dual-layer bidirectional LSTM architecture enhanced by an attention mechanism for accurately predicting the RUL of PEMFCs. Raw data is preprocessed, and important features are selected by the smoothing technique and random forest method to reduce manual intervention. To enhance model adaptability and predictive accuracy, the Optuna optimization framework is employed to automatically fine-tune hyperparameters. The proposed prediction model is benchmarked against several existing approaches using aging datasets from two separate PEMFC stacks. Experimental findings indicate that the proposed two-layer BiLSTM with attention mechanism surpasses other baseline models in performance. Notably, the designed prediction model demonstrates strong performance on both benchmark datasets and real-world data acquired through a custom-built experimental fuel cell platform. This research offers meaningful guidance for prolonging the service life of PEMFCs and enhancing the efficiency of maintenance planning. Full article

Proton exchange membrane fuel cell (PEMFC) fault diagnosis faces two critical limitations: conventional offline methods lack real-time predictive capability, while existing prediction approaches are confined to single fault types. To address these gaps, this study proposes an online multi-fault prediction framework integrating three novel contributions: (1) a sensor fusion strategy leveraging existing thermal/electrochemical measurements (voltage, current, temperature, humidity, and pressure) without requiring embedded stack sensors; (2) a real-time sliding window mechanism enabling dynamic prediction updates every 1 s under variable load conditions; and (3) a modified CNN-based Bi-LSTM parallel model with attention mechanism (ConvBLSTM-PMwA) architecture featuring multi-input multi-output (MIMO) capability for simultaneous flooding/air-starvation detection. Through comparative analysis of different neural architectures using experimental datasets, the optimized ConvBLSTM-PMwA achieved 96.49% accuracy in predicting dual faults 64.63 s pre-occurrence, outperforming conventional LSTM models in both temporal resolution and long-term forecast reliability. Full article

To expand the applications of proton exchange membrane fuel cell (PEMFC) stacks, it is essential to address the issue of their short lifetime. Various studies are being conducted to improve this limitation, and efficient methods for verifying durability in a short period of time are required. This study presents a novel dynamic load cycling protocol designed to emulate the real-world driving conditions of commercial vehicles. This protocol was employed as an accelerated degradation test for PEMFC stacks under two elevated temperatures (65 and 80 °C), each conducted for 1000 h. A bi-exponential model, incorporating Arrhenius principles, was fitted to the degradation data. During this process, a mixed effects modeling approach was employed to distinguish between fixed and random effects within the model parameters. The activation energy was consistent across all cells and was thus designated as a fixed effect. Activation energy, which predominantly affects the long-term durability of PEMFC stacks, was estimated as 0.808 eV. By applying this estimated value to the Arrhenius equation, we calculated the acceleration factors for the degradation of fuel cell performance. Specifically, the rate of voltage degradation was found to be approximately 1.516 times faster at 65 °C and 4.923 times faster at 80 °C, compared to the standard operating temperature of 60 °C. Additionally, Monte Carlo simulations were conducted to predict the failure-time distribution under normal use conditions, estimating a median lifetime of 3884 h, which corresponds to 155,360 km of driving. This methodology offers a reliable and time-efficient framework for assessing PEMFC durability, with significant implications for reducing testing costs and accelerating the development of hydrogen fuel cell technology. Full article

α- or 4-Substituted curcumin analogues are scarce. We describe herein the syntheses and crystal structures of the first α-halogenated curcumin derivatives: (1E,6E)-1,7-bis (4-hydroxy-3-methoxyphenyl)-4-bromo-5-hydroxy-1,3,6-heptatriene-3-one or (4-bromocurcumin) (1) and (1E,6E)-1,7-bis (4-hydroxy-3-methoxyphenyl)-4-chloro-5-hydroxy-1,3,6-heptatriene-3-one or (4-chlorocurcumin) (2). We note that the key step in the successful synthesis of the bromo-analog is the use of slightly acidic media to favor the diketo form of curcumin prior to carrying out the reaction. Both newly prepared compounds assume the keto–enol form in the solid state and crystallize in the monoclinic space group P2₁/c with four molecules in the unit cell each with slightly different dimensions. Inter- and intra- molecular hydrogen bonds were observed in the two structures. Most significant observed features were the inter-molecular O…O distances of 2.842 and 2.840 Å and intra-molecular O…O distances of 2.460 and 2.451 Å for bromo-or (1) and chloro- or (2) derivatives, respectively. No close halogen…halogen contacts were observed in either of the two structures. Both molecules are nearly planar with torsion angles of 0.54 and 1.16 °C between the planes of two terminal phenyl groups for (1) and (2), respectively. π-Stacks were observed in both structures with interplanar distances of 3.367 and 3.454 Å for the bromo- and chloro- compounds, respectively. Hirshfeld surface analysis confirms quantitively a picture of the inter- and intra-molecular interactions in both compounds compared with polymorph I (the most common form) of curcumin. UV–Vis absorption spectra are shifted to higher wavelengths with l_max of 475 and 477 nm for compounds 1 and 2, respectively, compared with 442 nm in dichloromethane solutions. The newly synthesized molecules will open the door for numerous possible synthetic modifications of the α-carbon to prepare valuable analogues of curcumin with more favorable solubility profiles. Full article

A packaged transmit array (TA) antenna is designed for automotive radar applications operating at 77 GHz. The compact dimensions of the proposed configuration make it compatible with standard quad flat no-lead package (QFN) technology. The TA placed inside the package cover is used to focus the field radiated by a feed placed in the same package. The unit cell of the array is composed of two pairs of stacked patches separated by a central ground plane. A planar patch antenna surrounded by a mushroom-type EBG (Electromagnetic Band Gap) structure is used as the primary feed. An analytical approach is employed to evaluate the primary parameters of the suggested TA, including its directivity, gain and spillover efficiency. The final design has been refined using comprehensive full-wave simulations. The simulated gain is 14.2 dBi at 77 GHz, with a half-power beamwidth of 22°. This proposed setup is a strong contender for highly integrated mid-gain applications in the automotive sector. Full article

We propose a double-cell cholesteric liquid crystal (CLC) device composed of a left-handed (LH) CLC cell with a pair of sheet electrodes and a right-handed (RH) CLC cell with a tri-electrode configuration characterized by a sheet electrode on the top and an interdigitated electrode on the bottom substrates. Bi-reflected color tuning and hyper-reflective color switching are revealed from this cell stack via the electrothermal control of the central wavelengths of the LH- and RH-bandgaps by voltage-induced pseudo-dielectric heating. The two CLCs are thermally sensitive and exhibit overlapped bandgaps in the field-off state with nearly identical temperature dependence, resulting in a hyper-reflective color at 720 nm at 23.4 °C and 380 nm at 29.8 °C. Upon the application of 4 V_rms at 2 MHz across the stacked device to induce pseudo-dielectric heating, two reflective colors can be resolved due to asymmetrical temperature elevations. Accordingly, the difference in wavelength between the two colors increases with increasing voltage through a series cell connection, while maintaining approximately constant via a parallel connection. This study provides a feasible pathway to developing a multifunctional device with electrothermally tunable bi-reflected and hyper-reflective states based on two conventional cell geometries, which is promising for lasers and color-related display applications. Full article

The market for electric vehicles (EVs) has been growing in popularity, and by 2027, it is predicted that the market valuation will reach $869 billion. To support the growth of EVs in public road safety, advances in battery safety research for EV application should achieve low-cost, lightweight, and high safety protection. In this research, the development of a lightweight, crashworthy battery protection system using an excellent energy absorption capability is carried out. The lightweight structure was developed by using metastructure constructions with an arrangement of repeated lattice cellular structures. Three metastructure configurations (bi-stable, star-shaped, double-U) with their geometrical variables (thickness, inner spacing, cell stack) and material types (stainless steel, aluminium, and carbon steel) were evaluated until the maximum Specific Energy Absorptions (SEA) value was attained. The Finite Element Method (FEM) is utilised to simulate the mechanics of impact and calculate the optimum SEA of the various designs using machine learning methodology. Latin Hypercube Sampling (LHS) was used to derive the design variation by dividing the variables into 100 samples. The machine learning optimisation method utilises the Artificial Neural Networks (ANN) and Non-dominated Sorting Genetic Algorithm-II (NSGA-II) to forecast the design that produces maximum SEA. The optimum control variables are star-shaped cells consisting of one vertical unit cell using aluminium material with a cross-section thickness of 2.9 mm. The optimum design increased the SEA by 5577% compared to the baseline design. The accuracy of the machine learning prediction is also verified using numerical simulation with a 2.83% error. Four different sandwich structure configurations are then constructed using the optimal geometry for prismatic battery protection subjected to ground impact loading conditions. An optimum configuration of $6\times 4\times 1$ core cells arrangement results in a maximum displacement of 7.33 mm for the prismatic battery in the ground impact simulation, which is still less than the deformation threshold for prismatic battery safety of 10.423 mm. It is shown that the lightweight metastructure is very efficient for prismatic battery protection subjected to ground impact loading conditions. Full article

The syntheses of novel 2,4-bis[(substituted-aminomethyl)phenyl]phenylquinazolines 12 and 2,4-bis[(substituted-aminomethyl)phenyl]phenylquinolines 13 are reported here in six steps starting from various halogeno-quinazoline-2,4-(1H,3H)-diones or substituted anilines. The antiproliferative activities of the products were determined in vitro against a panel of breast (MCF-7 and MDA-MB-231), human adherent cervical (HeLa and SiHa), and ovarian (A2780) cell lines. Disubstituted 6- and 7-phenyl-bis(3-dimethylaminopropyl)aminomethylphenyl-quinazolines 12b, 12f, and 12i displayed the most interesting antiproliferative activities against six human cancer cell lines. In the series of quinoline derivatives, 6-phenyl-bis(3-dimethylaminopropyl)aminomethylphenylquinoline 13a proved to be the most active. G-quadruplexes (G4) stacked non-canonical nucleic acid structures found in specific G-rich DNA, or RNA sequences in the human genome are considered as potential targets for the development of anticancer agents. Then, as small aza-organic heterocyclic derivatives are well known to target and stabilize G4 structures, their ability to bind G4 structures have been determined through FRET melting, circular dichroism, and native mass spectrometry assays. Finally, telomerase inhibition ability has been also assessed using the MCF-7 cell line. Full article

The membrane electrode assembly (MEA) encompassing the polymer electrolyte membrane (PEM) and catalyst layers are the key components in Polymer Electrolyte Membrane Fuel Cells (PEMFCs). The cost of the PEMFC stacks has been limiting its commercialization due to the inflated price of conventional platinum (Pt)-based catalysts. As a consequence, the authors of this paper focus on developing novel bi-metallic (Pt-Co) nano-alloy-catalyzed MEAs using the non-equilibrium impregnation–reduction (NEIR) approach with an aim to reduce the Pt content, and hence, the cost. Herein, the MEAs are fabricated on a Nafion^® membrane with a 0.4 mg_Ptcm⁻² Pt:Co electrocatalyst loading at three atomic ratios, viz., 90:10, 70:30, and 50:50. The High Resolution-Scanning Electron Microscopic (HR-SEM) characterization of the MEAs show a favorable surface morphology with a uniform distribution of Pt-Co alloy particles with an average size of about 15–25 µm. Under standard fuel cell test conditions, an MEA with a 50:50 atomic ratio of Pt:Co exhibited a peak power density of 0.879 Wcm⁻² for H₂/O₂ and 0.727 Wcm⁻² for H₂/air systems. The X-ray diffractometry (XRD), SEM, EDX, Cyclic Voltammetry (CV), impedance, and polarization studies validate that Pt:Co can be a potential affordable alternative to high-cost Pt. Additionally, a high degree of stability in the fuel cell performance was also demonstrated with Pt₅₀:Co₅₀. Full article

Correctly anticipating PV electricity production may lessen stochastic fluctuations and incentivize energy consumption. To address the intermittent and unpredictable nature of photovoltaic power generation, this article presents an ensemble learning model (MVMD-CLES) based on the whale optimization algorithm (WOA), variational mode decomposition (VMD), convolutional neural network (CNN), long and short-term memory (LSTM), and extreme learning machine (ELM) stacking. Given the variances in the spatiotemporal distribution of photovoltaic data and meteorological features, a multi-branch character extraction iterative mixture learning model is proposed: we apply the MWOA algorithm to find the optimal decomposition times and VMD penalty factor, and then divide the PV power sequences into sub-modes with different frequencies using a two-layer algorithmic structure to reconstruct the obtained power components. The primary learner is CNN–BiLSTM, which is utilized to understand the temporal and spatial correlation of PV power from information about the weather and the output of photovoltaic cells, and the LSTM learns the periodicity and proximity correlation of the power data and obtains the corresponding component predictions. The second level is the secondary learner—the output of the first layer is learned again using the ELM to attenuate noise and achieve short-term prediction. In different case studies, regardless of weather changes, the proposed method is provided with the best group of consistency and constancy, with an average RMSE improvement of 12.08–39.14% over a single-step forecast compared to other models, the average forecast RMSE increased by 5.71–9.47% for the first two steps. Full article

This paper proposes an S/X-band single-layer shared-aperture array antenna for the multifunction radars of military ships. A unit cell of the proposed antenna consists of one S-band element and four X-band elements. The S- and X-band elements are printed on the same layer to prevent a blockage effect by upper elements in the stacked shared-aperture antenna. Herein, the S-band element has a mutual complementary configuration for the X-band elements. In addition, the unit cell of the proposed antenna is designed in a symmetrical structure, which can be flexibly extended to a full array configuration. To verify the antenna feasibility, antenna performances are measured in a full anechoic chamber. The fractional bandwidths of the S- and X-band elements are 13.6% and 13.4%, respectively. Moreover, in the 2 × 2 array configuration, the S-band array gain in the bore-sight direction varies from 5.4 dBi to 3.5 dBi when the main beam is steered from 0° to 45°. Under the same conditions, the measured X-band array gain in the bore-sight direction decreases from 13.4 dBi to 11.6 dBi. Full article

As the most abundant RNA methylation modification, N6-methyladenosine (m6A) could regulate asymmetric and symmetric division of hematopoietic stem cells and play an important role in various diseases. Therefore, the precise identification of m6A sites around the genomes of different species is a critical step to further revealing their biological functions and influence on these diseases. However, the traditional wet-lab experimental methods for identifying m6A sites are often laborious and expensive. In this study, we proposed an ensemble deep learning model called m6A-BERT-Stacking, a powerful predictor for the detection of m6A sites in various tissues of three species. First, we utilized two encoding methods, i.e., di ribonucleotide index of RNA (DiNUCindex_RNA) and k-mer word segmentation, to extract RNA sequence features. Second, two encoding matrices together with the original sequences were respectively input into three different deep learning models in parallel to train three sub-models, namely residual networks with convolutional block attention module (Resnet-CBAM), bidirectional long short-term memory with attention (BiLSTM-Attention), and pre-trained bidirectional encoder representations from transformers model for DNA-language (DNABERT). Finally, the outputs of all sub-models were ensembled based on the stacking strategy to obtain the final prediction of m6A sites through the fully connected layer. The experimental results demonstrated that m6A-BERT-Stacking outperformed most of the existing methods based on the same independent datasets. Full article

Background: Surgically induced nerve damage is a common but debilitating side effect. By developing tracers that specifically target the most abundant protein in peripheral myelin, namely myelin protein zero (P0), we intend to support fluorescence-guided nerve-sparing surgery. To that end, we aimed to develop a dimeric tracer that shows a superior affinity for P0. Methods: Following truncation of homotypic P0 protein-based peptide sequences and fluorescence labeling, the lead compound Cy5-P0_101–125 was selected. Using a bifunctional fluorescent dye, the dimeric Cy5-(P0_101–125)₂ was created. Assessment of the performance of the mono- and bi-labeled compounds was based on (photo)physical evaluation. This was followed by in vitro assessment in P0 expressing Schwannoma cell cultures by means of fluorescence confocal imaging (specificity, location of binding) and flow cytometry (binding affinity; K_D). Results: Dimerization resulted in a 1.5-fold increase in affinity compared to the mono-labeled counterpart (70.3 +/− 10.0 nM vs. 104.9 +/− 16.7 nM; p = 0.003) which resulted in a 4-fold increase in staining efficiency in P0 expressing Schwannoma cells. Presence of two targeting vectors also improves a pharmacokinetics of labeled compounds by lowering serum binding and optical stability by preventing dye stacking. Conclusions: Dimerization of the nerve-targeting peptide P0_101–125 proves a valid strategy to improve P0 targeting. Full article

With 19.3 million new cases and almost 10 million deaths in 2020, cancer has become a leading cause of death today. Curcumin and its analogues were found to have promising anticancer activity. Inspired by curcumin’s promising anticancer activity, we prepared three semi-synthetic analogues by chemically modifying the diketone function of curcumin to its pyrazole counterpart. The curcumin analogues (3a–c) were synthesized by two different methods, followed by their DFT analyses to study the HOMO/LUMO configuration to access the stability of compounds (∆E = 3.55 to 3.35 eV). The curcumin analogues (3a–c) were tested for antiproliferative activity against a total of five dozen cancer cell lines in a single (10 µM) and five dose (0.001 to 100 µM) assays. 3,5-Bis(4-hydroxy-3-methoxystyryl)-1H-pyrazole-1-yl-(phenoxy)ethanone (3b) and 3,5-bis(4-hydroxy-3-methoxystyryl)-1H-pyrazole-1-yl-(2,4-dichlorophenoxy)ethanone (3c) demonstrated the most promising antiproliferative activity against the cancer cell lines with growth inhibitions of 92.41% and 87.28%, respectively, in a high single dose of 10 µM and exhibited good antiproliferative activity (%GIs > 68%) against 54 out of 56 cancer cell lines and 54 out of 60 cell lines, respectively. The compound 3b and 3c demonstrated the most potent antiproliferative activity in a 5-dose assay with GI₅₀ values ranging between 0.281 and 5.59 µM and 0.39 and 0.196 and 3.07 µM, respectively. The compound 3b demonstrated moderate selectivity against a leukemia panel with a selectivity ratio of 4.59. The HOMO-LUMO energy-gap (∆E) of the compounds in the order of 3a > 3b > 3c, was found to be in harmony with the anticancer activity in the order of 3c ≥ 3b > 3a. Following that, all of the curcumin analogues were molecular docked against EGFR, one of the most appealing targets for antiproliferative activity. In a molecular docking simulation, the ligand 3b exhibited three different types of interactions: H-bond, π-π-stacking and π-cationic. The ligand 3b displayed three H-bonds with the residues Met793 (with methoxy group), Lys875 (with phenolic group) and Asp855 (with methoxy group). The π-π-stacking interaction was observed between the phenyl (of phenoxy) and the residue Phe997, while π-cationic interaction was displayed between the phenyl (of curcumin) and the residue Arg841. Similarly, the ligand 3c displayed five H-bonds with the residue Met793 (with methoxy and phenolic groups), Lys845 (methoxy group), Cys797 (phenoxy oxygen), and Asp855 (phenolic group), as well as a halogen bond with residue Cys797 (chloro group). Furthermore, all the compound 3a–c demonstrated significant binding affinity (−6.003 to −7.957 kcal/mol) against the active site of EGFR. The curcumin analogues described in the current work might offer beneficial therapeutic intervention for the treatment and prevention of cancer. Future anticancer drug discovery programs can be expedited by further modifying these analogues to create new compounds with powerful anticancer potentials. Full article

One-monolayer (ML) (thin) and 5-ML (thick) Si films were grown on the α-phase Si(111)√3 × √3R30°-Bi at a low substrate temperature of 200 °C. Si films have been studied in situ by reflection electron energy loss spectroscopy (REELS) and Auger electron spectroscopy, as a function of the electron beam incidence angle α and low-energy electron diffraction (LEED), as well as ex situ by grazing incidence X-ray diffraction (GIXRD). Scanning tunneling microscopy (STM), and scanning tunneling spectroscopy (STS) were also reported. The REELS spectra, taken at the Si K absorption edge (~1.840 KeV), reveal the presence of two distinct loss structures attributed to transitions 1s→π* and 1s→σ* according to their intensity dependence on α, attesting to the sp²-like hybridization of the silicon valence orbitals in both thin and thick Si films. The synthesis of a silicon allotrope on the α-phase of Si(111)√3 × √3R30°-Bi substrate was demonstrated by LEED patterns and GIXRD that discloses the presence of a Si stack of 3.099 (3) Å and a √3 × √3 unit cell of 6.474 Å, typically seen for multilayer silicene. STM and STS measurements corroborated the findings. These measurements provided a platform for the new √3 × √3R30° Si allotrope on a Si(111)√3 × √3 R30°-Bi template, paving the way for realizing topological insulator heterostructures from different two-dimensional materials, Bi and Si. Full article