Search Results (27)

To study the adsorption of lanthanide (Ln) atoms on graphene containing a Stone–Wales defect, we used a cluster model (SWG) and performed calculations at the PBE-D2/DNP level of the density functional theory. Our previous study, where the above combination was complemented with the ECP pseudopotentials, was only partially successful due to the impossibility of calculating terbium-containing systems and a serious error found for the SWG complex with dysprosium. In the present study we employed the DSPP pseudopotentials and completely eliminated the latter two failures. We analyzed the optimized geometries of the full series of fifteen SWG + Ln complexes, along with their formation energies and electronic parameters, such as frontier orbital energies, atomic charges, and spins. In many regards, the two series of calculations show qualitatively similar features, such as roughly M-shaped curves of the adsorption energies and trends in the changes in charge and spin of the adsorbed Ln atoms, as well as the spin density plots. However, the quantitative results can differ significantly. For most characteristics we found no evident correlation with the lanthanide contraction. The only dataset where this phenomenon apparently manifests itself (albeit to a limited and irregular degree) is the changes in the closest Ln^…C approaches. Full article

Accurate estimation of the state of health (SOH) and remaining useful life (RUL) of lithium-ion batteries (LiBs) is critical for ensuring battery reliability and safety in applications such as electric vehicles and energy storage systems. However, existing methods developed for estimating the SOH and RUL of LiBs often rely on full-cycle charging data, which are difficult to obtain in engineering practice. To bridge this gap, this paper proposes a novel data-driven method to estimate the SOH and RUL of LiBs only using partial charging curve features. Key health features are extracted from the constant voltage (CV) charging process and voltage relaxation, validated through Pearson correlation analysis and SHapley Additive exPlanations (SHAP) interpretability. A hybrid framework combining CatBoost for SOH estimation and particle swarm optimization-support vector regression (PSO-SVR) for RUL estimation is developed. Experimental validation on public datasets demonstrates superior performance of the methodology described above, with an SOH estimation root mean square error (RMSE) and mean absolute error (MAE) below 1.42% and 0.52% and RUL estimation relative error (RE) under 1.87%. The proposed methodology also exhibits robustness and computational efficiency, making it suitable for battery management systems (BMSs) of LiBs. Full article

The determination of the surface charge density distribution and the transcapacitance of capacitive one-side-access circular sensors with three electrodes on the active surface remains problematic both theoretically and experimentally. To provide an input, a novel experimental study was carried out on the partial permittivity of rigid PU foams by means of a capacitive circular OSA sensor with three electrodes on the active surface. An original and effective method was elaborated in order to determine the model functions of the obtained experimental data of the partial permittivity. A numerical estimation for the rate of change in the partial permittivity was made and the highest rate of change was determined. It was identified that the highest rate of change takes place at the inter-electrode zone and depends on the density and the true permittivity in a nonlinear mode, approximated with second-order polynomials. The overall character of the rate of change in the partial permittivity in the dependence of the radius of the covered area was found to be comparable to that of the surface charge density distribution curve, estimated theoretically for a circular two-electrode OSA sensor. The experimental results on the partial permittivity can be useful in the performance evaluation and design of the optimal proportions of capacitive circular OSA sensors, as well as in the verification of the corresponding mathematical models. Full article

The control of the performance of single-walled carbon nanotube (SWCNT) random network-based transistors is of critical importance for their applications in electronic devices, such as complementary metal oxide semiconducting (CMOS)-based logics. In ambient conditions, SWCNTs are heavily p-doped by the H₂O/O₂ redox couple, and most doping processes have to counteract this effect, which usually leads to broadened hysteresis and poor stability. In this work, we coated an SWCNT network with various common polymers and compared their thin-film transistors’ (TFTs’) performance in a nitrogen-filled glove box. It was found that all polymer coatings will decrease the hysteresis of these transistors due to the partial removal of charge trapping sites and also provide the stable control of the doping level of the SWCNT network. Counter-intuitively, polymers with electron-withdrawing functional groups lead to a dramatically enhanced n-branch in their transfer curve. Specifically, SWCNT TFTs with poly (vinylidene fluoride) coating show an n-type mobility up to 61 cm²/Vs, with a decent on/off ratio and small hysteresis. The inverters constructed by connecting two ambipolar TFTs demonstrate high gain but with certain voltage loss. P-type or n-type doping from polymer coating layers could suppress unnecessary n- or p-branches, shift the threshold voltage and optimize the performance of these inverters to realize rail-to-rail switching. Similar devices also demonstrate interesting antiambipolar performance with tunable on and off voltage when tested in a different configuration. Full article

The accurate state of health (SOH) estimation of lithium-ion batteries (LIBs) during operation is crucial to ensure optimal performance, prolonging battery life and preventing unexpected failure or safety hazards. This work presents a storage- and performance-optimised deep learning approach to estimate the capacity-based SOH of LIBs using raw sensor data from partial charging curves under constant current condition. The proposed model is based on a combination of a one-dimensional convolutional and long short-term memory neural network, and processes time, voltage, and incremental capacity of partial charging curves as time series. The model is cross-validated on different ageing scenarios, reaching an overall MAE = 0.418% and RMSE = 0.531%, promising an accurate SOH estimation of LIBs under varying usage and environmental conditions in a real-world application. Full article

Lithium-ion batteries are widely used in electric vehicles, energy storage power stations, and many other applications. Accurate and reliable monitoring of battery health status and remaining capacity is the key to establish a lithium-ion cell management system. In this paper, based on a Bayesian optimization algorithm, a deep neural network is structured to evaluate the whole charging curve of the battery using partial charging curve data as input. A 0.74 Ah battery is used for experiments, and the effect of different input data lengths is also investigated to check the high flexibility of the approach. The consequences show that using only 20 points of partial charging data as input, the whole charging profile of a cell can be exactly predicted with a root-mean-square error (RMSE) of less than 19.16 mAh (2.59% of the nominal capacity of 0.74 Ah), and its mean absolute percentage error (MAPE) is less than 1.84%. In addition, critical information including battery state-of-charge (SOC) and state-of-health (SOH) can be extracted in this way to provide a basis for safe and long-lasting battery operation. Full article

The state of health is a crucial state that suggests the capacity of lithium-ion batteries to store and restitute energy at a certain power level, which should be carefully monitored in the battery management system. However, the state of health of batteries is unmeasurable and, currently, it is usually estimated within a specific area of the whole charging data, which is very limited in practical application because of the incomplete and random charging behaviors of users. In this paper, we intend to estimate the state of health of batteries with flexible partial charging curves and normal multi-layer perceptron based on the degradation data of eight 0.74 Ah batteries. To make the estimation more adaptive and flexible, we extract several features from partial charging curves. Analysis of the relationship between extracted features and the state of health shows that the extracted features are useful in estimation. As the length of the partial charging curve increases, the extracted features still function well, and the root mean square error of the test set is lower than 1.5%. Further validation on the other two types of batteries reveals that the proposed method achieves high accuracy even with different sampling and working conditions. The proposed method offers an easy-to-implement way to achieve an accurate estimation of a battery’s state of health. Full article

The capability of dielectric measurements was significantly increased with the development of capacitive one-side access physical sensors. Complete samples give no opportunity to study electric susceptibility at a partial coverage of the one-side access sensor’s active area; therefore, partial samples are proposed. The electric susceptibility at the partial coverage of a circular one-side access sensor with cylinders and shells is investigated for polyurethane materials. The implementation of the relative partial susceptibility permitted us to transform the calculated susceptibility data to a common scale of 0.0–1.0 and to outline the main trends for PU materials. The partial susceptibility, relative partial susceptibility, and change rate of relative partial susceptibility exhibited dependence on the coverage coefficient of the sensor’s active area. The overall character of the curves for the change rate of the relative partial susceptibility, characterised by slopes of lines and the ratio of the change rate in the centre and near the gap, corresponds with the character of the surface charge density distribution curves, calculated from mathematical models. The elaborated methods may be useful in the design and optimization of capacitive OSA sensors of other configurations of electrodes, independent of the particular technical solution. Full article

To estimate the state of health, charge, power, and safety (SoX) of lithium-ion batteries (LiBs) in real time, battery management systems (BMSs) need accurate and efficient battery models. The full-order partial two-dimensional (P2D) model is a common physics-based cell-level LiB model that faces challenges for real-time BMS implementation due to the complexity of its numerical solver. In this paper, we propose a method to discretise the P2D model equations using the Finite Volume and Verlet Integration Methods to significantly reduce the computational complexity of the solver. Our proposed iterative solver uses novel convergence criteria and physics-based initial guesses to provide high fidelity for discretised P2D equations. We also include both the kinetic-limited and diffusion-limited models for Solid Electrolyte Interface (SEI) growth into an iterative P2D solver. With these SEI models, we can estimate the capacity fade in real time once the model is tuned to the cell–voltage curve. The results are validated using three different operation scenarios, including the 1C discharge/charge cycle, multiple-C-rate discharges, and the Lawrence Livermore National Laboratory dynamic stress test. The proposed solver shows at least a 4.5 times improvement in performance with less than 1% error when compared to commercial solvers. Full article

The present work focuses on the investigation of protective coatings produced on zinc from chitosan (Chit) and an anionic dye, namely cresol red. Cresol red (CR) fulfills the basic requirements to be used as a corrosion inhibitor because it possesses a relatively high molecular weight and includes in its structure oxygen and sulfur atoms as well as aromatic rings. Moreover, it is an anionic compound that can interact with positively charged chitosan to produce reinforced coatings for zinc anti-corrosion protection. The influence of cresol red as a possible corrosion inhibitor for zinc substrates was investigated either in solution or incorporated in Chit coatings. Two preparation methods for the coatings were used: (i) Chit coating impregnation by immersion in the CR solution after Chit deposition on Zn, and (ii) chitosan mixing with the CR solution before applying the dip-coating technique. Potentiodynamic polarization curves were used to determine the kinetic parameters of the corrosion process. Long-term measurements were carried out in wet/dry cyclic conditions by using electrochemical impedance spectroscopy. EIS measurements recorded in 0.2 g/L Na₂SO₄ at pH = 7 show an important increase in the impedance of the coatings occurring from the first until the fifty-fifth day in a row, in dry–wet cycles. This increase is due to the beneficial effect of CR incorporated in Chitosan and could be, at least partially, related to a consolidation of the Chit coating structure in the presence of CR by crosslinking between Chit and CR molecules. The structure of the coatings was studied, and the interactions between chitosan and cresol red were put into evidence by using FT-IR spectroscopy. Adhesion and wettability measurements were also carried out. The adhesion of Chit incorporating CR on Zn was better than that on glass substrates and reached ~99.99%, suggesting a better affinity of the chitosan coating towards the Zn substrate due to the existence of ZnO on the substrate surface. All the results show that CR could be used on zinc as a corrosion inhibitor incorporated in chitosan at basic pHs, but without taking advantage of its pH-indicating properties, which are lost due to the interactions occurring between the positively charged biopolymer and the negatively charged dye molecule. The preparation method of Chit coating impregnation with CR by immersion in the solution after deposition on Zn led to poorer results than the method in which chitosan was previously mixed with CR before applying the dip-coating technique. Full article

In this article, the effects of high-energy proton irradiation on top-gate graphene field-effect transistors (GFETs) were investigated by using 20 MeV protons. The basic electrical parameters of the top-gate GFETs were measured before and after proton irradiation with a fluence of 1 × 10¹¹ p/cm² and 5 × 10¹¹ p/cm², respectively. Decreased saturation current, increased Dirac sheet resistance, and negative drift in the Dirac voltage in response to proton irradiation were observed. According to the transfer characteristic curves, it was found that the carrier mobility was reduced after proton irradiation. The analysis suggests that proton irradiation generates a large net positive charge in the gate oxide layer, which induces a negative drift in the Dirac voltage. Introducing defects and increased impurities at the gate oxide/graphene interface after proton irradiation resulted in enhanced Coulomb scattering and reduced mobility of the carriers, which in turn affects the Dirac sheet resistance and saturation current. After annealing at room temperature, the electrical characteristics of the devices were partially restored. The results of the technical computer-aided design (TCAD) simulation indicate that the reduction in carrier mobility is the main reason for the degradation of the electrical performance of the device. Monte Carlo simulations were conducted to determine the ionization and nonionization energy losses induced by proton incidence in top-gate GFET devices. The simulation data show that the ionization energy loss is the primary cause of the degradation of the electrical performance. Full article

Zinc–tungsten coatings have been considered as environmentally friendly, and corrosion- and wear-resistant coatings. Here, Zn–W coatings were successfully electrodeposited from an aqueous solution. Citrate-based electrolytes with pH in the range of 3.0 to 5.7 were used as plating baths. The kinetics of co-reduction in the Zn(II)–W(VI)–Cit system was studied on the basis of partial polarization curves. The effects of applied potential, electrolyte composition, pH, hydrodynamic conditions and passed charge on the electrodeposition of Zn–W layers were determined. X-ray photoelectron spectroscopy confirmed the presence of metallic tungsten co-deposited with zinc. X-ray diffraction analysis revealed the formation of hexagonal Zn–W phase resulting from a substitution of Zn atoms by W atoms in the Zn crystal lattice. The formation of the proper stable and electroactive W(VI) and Zn(II) complexes is the first crucial factor enabling the induced codeposition of Zn–W alloys. The tungsten content in the Zn–W deposit is closely related to the concentration of electroactive tungstate–citrate species and its ratio relative to the zinc–citrate electroactive species in the electrolytic bath. The oxidation state of tungsten in the electrodeposited Zn–W layers can be controlled mainly by the applied deposition potential and by the bath pH, which determines the type of W(VI)–Cit species that can be reduced. Full article

Lithium-ion batteries, known for their high efficiency and high energy output, have gained significant attention as energy storage devices. Monitoring the state of charge through battery management systems plays a crucial role in enhancing the safety and extending the lifespan of lithium-ion batteries. In this paper, we propose a state-of-charge estimation method to overcome the limitations of the traditional open-circuit voltage method and electrochemical impedance spectroscopy. We verified changes in the shape of the voltage relaxation curve based on battery impedance through simulations and analyzed the impact of individual impedance on the voltage relaxation curve using differential equations. Based on this relationship, we estimated the impedance from the battery’s voltage relaxation curve through curve fitting and subsequently estimated the state of charge using a pre-established lookup table. In addition, we introduced a partial curve-fitting method to reduce the estimation time compared to the existing open-circuit voltage method and confirmed the trade-off relationship between the estimation time and estimation error. Full article

Due to global warming issues, the rapid growth of electric vehicle sales is fully expected to result in a dramatic increase in returned batteries after the first use. Naturally, industries have shown great interest in establishing business models for retired battery reuse and recycling. However, they still have many challenges, such as high costs from the logistics of returned batteries and evaluating returned battery quality. One of the most important characteristics of a returned battery is the battery storage capacity. Conventionally, the battery’s energy capacity is measured through the low current full charging and discharging process. While this traditional measurement procedure gives a reliable estimate of battery storage capacity, the time required for a reliable estimate is unacceptably long to support profitable business models. In this paper, we propose a new algorithm to estimate battery storage capacity that can dramatically reduce the time for estimation through the partial discharging process. To demonstrate the applicability of the proposed algorithm, cylindrical and prismatic cells were used in the experiments. Initially, five indicators were selected from the voltage response curves that can identify battery storage capacity. Then, the five indicators were applied to principal component analysis (PCA) to extract dominant factors. The extracted factors were applied to a multiple linear regression model to produce a reliable estimation of battery storage capacity. Full article

In today’s digital circuits, Si-based MOS devices have become the most widely used technology in medical, military, aerospace, and aviation due to their advantages of mature technology, high performance, and low cost. With the continuous integration of transistors, the characteristic size of MOSFETs is shrinking. Time-dependent dielectric electrical breakdown (TDDB) is still a key reliability problem of MOSFETs in recent years. Many researchers focus on the TDDB life of advanced devices and the mechanism of oxide damage, ignoring the impact of the TDDB effect on device parameters. Therefore, in this paper, the critical parameters of partially depleted silicon-on-insulator (PDSOI) under time-dependent dielectric electrical breakdown (TDDB) stress are studied. By applying the TDDB acceleration stress experiment, we obtained the degradation of the devices’ critical parameters including transfer characteristic curves, threshold voltage, off-state leakage current, and the TDDB lifetime. The results show that TDDB acceleration stress will lead to the accumulation of negative charge in the gate oxide. The negative charge affects the electric field distribution. The transfer curves of the devices are positively shifted, as is the threshold voltage. Comparing the experimental data of I/O and Core devices, we can conclude that the ultra-thin gate oxide device’s electrical characteristics are barely affected by the TDDB stress, while the opposite is true for a thick-gate oxide device. Full article