Connecting reservoir computing with statistical forecasting and deep neural networks. - In: Nature Communications, ISSN 2041-1723, Bd. 13 (2022), 227, S. 1-3
Among the existing machine learning frameworks, reservoir computing demonstrates fast and low-cost training, and its suitability for implementation in various physical systems. This Comment reports on how aspects of reservoir computing can be applied to classical forecasting methods to accelerate the learning process, and highlights a new approach that makes the hardware implementation of traditional machine learning algorithms practicable in electronic and photonic systems.
A highly robust self-supporting nickel nanoarray based on anodic alumina oxide template for determination of dopamine. - In: Sensors and actuators, ISSN 0925-4005, Bd. 350 (2022), 130835
Ratiometric electrochemical sensors can effectively reduce system errors and environmental interference during the detection of a target, affording good sensitivity, reproducibility, and a linear response range. However, traditional proportional electrochemical sensors are limited by the need for complex modifications and the lack of internal reference probes. In this study, we developed a ratiometric electrochemical sensing platform based on nickel nanoarrays as a self-supporting electrode (NiNASSE) by using an anodic alumina oxide template method. An internal reference probe was developed based on nickel nanoparticles (NiNPs) as nickel nanoarrays, presenting a facile modification process and stable redox signal. Furthermore, the highly ordered nanoarray structure expands the specific surface area of NiNASSE and accelerates the electron transfer rate. This new self-supporting proportional electrochemical sensor was successfully applied for the detection of dopamine and displayed good electrocatalytic ability, stability, and feasibility.
Construction of Co0.85Se@nickel nanopores array hybrid electrode for high-performance asymmetric supercapacitors. - In: Chemical engineering science, Bd. 247 (2022), 117081, insges. 9 S.
Nanostructured current collectors have larger specific surface area and short ion/electron transport path, which are highly desirable for supercapacitors applications. Herein, Co0.85SeNiNPs (Co0.85Se@NiNP) hybrid electrodes are proposed and fabricated, in which NiNP is served as nanostructured current collectors. NiNP has a vertical pore structure and a large specific surface area, which could effectively promote the ion/electron transport efficiency and reduce internal electrical resistance. Compared with Ni foam and Ni foil as current collectors, NiNP enables Co0.85Se@NiNP electrodes show significantly improved specific capacity, rate performance and cycle stability. Finally, an asymmetric supercapacitor device was assembled with Co0.85Se@NiNP hybrid electrode as the binder-free positive electrode and activated carbon (AC) coated on nickel foam as negative electrode. The Co0.85Se@NiNP//AC asymmetric supercapacitors can work in a wide potential window of 0 - 1.6 V with an ultrahigh specific capacity of 182.3 F g^-1 at 1 A g^-1. Most importantly, Co0.85Se@NiNP//AC has a high energy density of 64.81 Wh kg^-1 at 800 W kg^-1 and an outstanding cycle stability of up to 12000 cycles, indicating that Co0.85Se@NiNP electrode has great application potential in supercapacitors.
Optimal vibration analysis for a combustion motor. - In: IEEE Xplore digital library, ISSN 2473-2001, (2021), S. 166-170
Combustion motors have quite important uses in Peru due to capacity of energy that is achieved to solve multiple tasks, such as for example public transport, mining and factories. However, a big disadvantage is given owing to pollution that is produced through them. Therefore, there are many proposal solutions as for example, optimal control over physical variables, which have information of consumed fuel. Nevertheless, it gets complications in interesting (but longer) algorithms as strategies. That is the reason, why in this research is proposed a mathematical procedure that is correlated with faster and robust sensors/actuators according to achieve an enhancement performance over the efficiency of combustion motors.
Active noise cancellation techniques to enhance audition in noisy cities. - In: IEEE Xplore digital library, ISSN 2473-2001, (2021), S. 148-151
It is proposed in this research some suggestions and applications to enhance and care of the hearing health in noisy cities, such as for example, the noise that is caused by traffic, engines from factories and imprudent behavior from drivers and street sellers (as it happens in Peruvian cities). In this research, it was analyzed many techniques according to propose enhancement of the health audition by engineering analysis of the noise cancellation and advanced sensors/actuators (microphones and loudspeakers) that were based in nanostructures, because to achieve this objective. Therefore, it is expected that this research could be a support for institutions, which need technical analysis results, regarding to study the necessity to care the hearing health in noisy cities, such as for example, many hospitals, homes, universities and schools that are located near noisy avenues cannot get attenuation of noise, if there are not noise cancellation systems to care the hearing health.
Carbon-free crystal-like Fe1-xS as an anode for potassium-ion batteries. - In: ACS applied materials & interfaces, ISSN 1944-8252, Bd. 13 (2021), 46, S. 55218-55226
Im Titel ist "1-x" tiefgestellt
Potassium-ion batteries (PIBs) as a new electrochemical energy storage system have been considered as a desirable candidate in the post-lithium-ion battery era. Nevertheless, the study on this realm is in its infancy; it is urgent to develop electrode materials with high electrochemical performance and low cost. Iron sulfides as anode materials have aroused wide attention by virtue of their merits of high theoretical capacities, environmental benignity, and cost competitiveness. Herein, we constructed carbon-free crystal-like Fe1-xS and demonstrated its feasibility as a PIB anode. The unique structural feature endows the prepared Fe1-xS with plentiful active sites for electrochemical reactions and short transmission pathways for ions/electrons. The Fe1-xS electrode retained capacities of 420.8 mAh g-1 after 100 cycles at 0.1 A g-1 and 212.9 mAh g-1 after 250 cycles at 1.0 A g-1. Even at a high rate of 5.0 A g-1, an average capacity of 167.6 mAh g-1 was achieved. In addition, a potassium-ion full cell is assembled by employing Fe1-xS as an anode and potassium Prussian blue as a cathode; it delivered a discharge capacity of 127.6 mAh g-1 at 100 mA g-1 after 50 cycles.
Wafer-level fabrication of an EWOD-driven micropump. - In: MikroSystemTechnik Kongress 2021, (2021), S. 574-577
Dimensioning and characterisation of an EWOD-driven chipintegrated micropump using time-resolved simulations. - In: MikroSystemTechnik Kongress 2021, (2021), S. 531-534
Simulation model and dimensioning of a photoacoustic sensor for the detection of radiation-induced pressure surges. - In: MikroSystemTechnik Kongress 2021, (2021), S. 523-526
Reservoir computing with delayed input for fast and easy optimisation. - In: Entropy, ISSN 1099-4300, Bd. 23 (2021), 12, 1560, S. 1-13
Reservoir computing is a machine learning method that solves tasks using the response of a dynamical system to a certain input. As the training scheme only involves optimising the weights of the responses of the dynamical system, this method is particularly suited for hardware implementation. Furthermore, the inherent memory of dynamical systems which are suitable for use as reservoirs mean that this method has the potential to perform well on time series prediction tasks, as well as other tasks with time dependence. However, reservoir computing still requires extensive task-dependent parameter optimisation in order to achieve good performance. We demonstrate that by including a time-delayed version of the input for various time series prediction tasks, good performance can be achieved with an unoptimised reservoir. Furthermore, we show that by including the appropriate time-delayed input, one unaltered reservoir can perform well on six different time series prediction tasks at a very low computational expense. Our approach is of particular relevance to hardware implemented reservoirs, as one does not necessarily have access to pertinent optimisation parameters in physical systems but the inclusion of an additional input is generally possible.