This review article summarizes the key areas of self-cleaning coatings, primarily focusing on various materials that are widely used in recent research and also in commercial applications. The scope of this article orbits around hydrophobic and hydrophilic coatings, their working mechanism, fabrication techniques that enable the development of such coatings, various functions like Anti-icing, Electro-wetting, Surface switchability and the areas where selfcleaning technology can be implemented. Moreover, different characterization techniques and material testing feasibilities are also analyzed and discussed. Though several companies have commercialized a few products based on self-cleaning coating technology, much potential still remains in this field.

A review on self-cleaning coatings

SiO2 is one of the most abundant materials on Earth. It is cost-effective and also environmentally benign when used as an energy material. Although SiO2 was inactive to Li, it was engineered to react directly by a simple process. It exhibited a strong potential as a promising anode for Li-ion batteries.

Quartz (SiO2): a new energy storage anode material for Li-ion batteries

We study the paired-pulse-induced response of a NiOx-based memristor. The behavior of the memristor is surprisingly similar to the paired-pulse facilitation of a biological synapse. When the memristor is stimulated with a pair of electrical pulses, the current of the memristor induced by the second pulse is larger than that by the first pulse. In addition, the magnitude of the facilitation decreases with the pulse interval, while it increases with the pulse magnitude or pulse width.

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Emulating the paired-pulse facilitation of a biological synapse with a NiOx-based memristor

The well-known Ebbinghaus forgetting curve, which describes how information is forgotten over time, can be emulated using a NiO-based memristor with conductance that decreases with time after the application of electrical pulses. Here, the conductance is analogous to the memory state, while each electrical pulse represents a memory stimulation or learning event. The decrease in the conductance with time depends on the stimulation parameters, including pulse height and width and the number of pulses, which emulates memory loss behavior well in that the time taken for the memory to be lost depends on how the information is learned.

/pdf/emulating-the-ebbinghaus-forgetting-curve-of-the-human-brain-1li2c8hqyy.pdf

Emulating the Ebbinghaus forgetting curve of the human brain with a NiO-based memristor

A striking correlation between infrared photoinduced absorption spectra and the photoluminescence from silicon nanocrystals indicates that quantized electronic sublevels of the nanocrystals are resonantly coupled to surface vibrational modes via a polarization field produced by coherent longitudinal polar vibrations. Our experimental results and model support the assumption that the mechanism responsible for the efficient photoluminescence from silicon nanocrystals should be assigned to inhibition of nonradiative channels rather than enhancement of radiative channels.

Resonant coupling between surface vibrations and electronic states in silicon nanocrystals at the strong confinement regime.

Anomalous capacitance-voltage (C-V) characteristics of Al/Al-rich Al2O3/p-Si capacitors have been observed. The measured C-V curves exhibit rolloffs and frequency dispersion in the accumulation region and voltage stresses cause both horizontal and vertical shifts of the C-V curves. These anomalous behaviors are mainly due to the large current conduction and the charge trapping in the Al-rich Al2O3 layer. The anomalous C-V characteristics have been reconstructed based on a four-element circuit model. With the reconstructed C-V curves, the capacitance of the Al-rich Al2O3 layer and the charging-induced flatband voltage shift can be determined.

Anomalous capacitance-voltage characteristics of Al/Al-rich Al2O3/p-Si capacitors and their reconstruction

In this work, Si ions are implanted into the gate oxide of MOSFETs with different implantation schemes, followed by a high-temperature annealing. The memory characteristics of the MOSFETs have been investigated for the following two excess Si distributions: (1) the excess Si is distributed in a narrow layer in the gate oxide near the Si substrate; and (2) the excess Si is distributed throughout the gate oxide. It is observed that both the excess Si distributions have good endurance of up to 106 program/erase cycles. The second excess Si distribution exhibits a better retention characteristic with less than 50% charge loss after 10 years. In contrast, the first excess Si distribution shows a complete charge loss after 1 year.

Influence of excess Si distribution in the gate oxide on the memory characteristics of MOSFETs

In this work, we report on the fabrication of tetraethylorthosilicate (TEOS) thin dielectric film containing silicon nanocrystals (Si nc), synthesized by solid-state reaction, in a capacitor structure. A metal–insulator–semi-conductor (MIS) capacitor, with 28 nm thick Si nc in a TEOS thin film, has been fabricated. For this MIS, both electron and hole trapping in the Si nc are possible, depending on the polarity of the bias voltage. A VFB shift greater than 1 V can be experienced by a bias voltage of 16 V applied to the metal electrode for 1 s. Though there is no top control oxide, the discharge time for 10% of charges can be up to 4480 s when it is biased at 16 V for 1 s. It is further demonstrated that charging and discharging mechanisms are due to the Si nc rather than the TEOS oxide defects. This form of Si nc in a TEOS thin film capacitor provides the possibility of memory applications at low cost.

Charge trapping phenomena of tetraethylorthosilicate thin film containing Si nanocrystals synthesized by solid-state reaction

Epilepsy is a serious neurological condition caused by a sudden abnormality of brain neurons. An accurate epilepsy detection based on electroencephalogram (EEG) signals can provide vital information for diagnosis and treatment. In this study, we propose a lightweight automatic epilepsy detection system with artificial neural network based on our as-fabricated neuromorphic chip. The proposed system utilizes a neural network model to achieve high-accuracy detection without the need for epilepsy-related prior knowledge. The model uses a filter module and a convolutional neural network to preprocess the raw EEG signal and uses a long short-term memory recurrent neural network and a fully connected network as the classifier. In the examination, the classification accuracy of the normal cases and seizures approaches 99.10%, and the accuracy of the normal cases, and interictal and seizure cases can reach 94.46%. This design provides possible epilepsy detection in wearable or portable devices.

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Epilepsy detection with artificial neural network based on as-fabricated neuromorphic chip platform

Conduction switching, i.e., a sharp change in the conduction from a lower-conductance state to a higher-conductance state or vice versa in aluminum nitride thin films embedded with Al nanocrystals (nc–Al) has been observed in the ramped-voltage and ramped-current current–voltage (I–V) measurements and the time-domain current measurement as well. Each state is well defined and its I–V characteristic follows a power law. It is observed that the conductance decreases (or increases) with charging (or discharging) in the nc–Al. It is shown that the conduction switching is due to the charging and discharging in the nc–Al at certain strategic sites. With the connecting (or breaking) of some conductive tunneling paths formed by the uncharged nc–Al due to the discharging (or charging) in the nc–Al at the strategic sites, a conduction switching occurs.

Y. Liu

Papers

Anomalous capacitance-voltage characteristics of Al/Al-rich Al2O3/p-Si capacitors and their reconstruction

Influence of excess Si distribution in the gate oxide on the memory characteristics of MOSFETs

Charge trapping phenomena of tetraethylorthosilicate thin film containing Si nanocrystals synthesized by solid-state reaction

Epilepsy detection with artificial neural network based on as-fabricated neuromorphic chip platform

Conduction switching in aluminum nitride thin films containing Al nanocrystals