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Supercritical carbon dioxide (SC-CO2) exhibits many advantages in the oil field, such as the facile creation of complex fractures, the mitigation of water sensitivity issues, and enhanced backflow efficiency. Nevertheless, the low viscosity of SC-CO2significantly impacts the production enhancement from CO2EOR and fracturing, limiting its widespread application. Therefore, the investigation of thickeners in SC-CO2and the design of clean and efficient SC-CO2thickeners are pivotal for advancing the CO2EOR and fracturing technology. This article elucidates the mechanisms behind SC-CO2thickening agents. Effective thickeners possess exceptional solubility and can establish spatial network structures through molecular aggregation or interactions between compounds, thereby restricting the movement of carbon dioxide molecules to achieve thickening. The article explores various supercritical CO2thickening techniques. The results indicate that supercritical CO2thickening technologies can effectively control the CO2flow, prevent the occurrence of viscous fingering phenomena, and enhance the sand-carrying capability of fracturing fluids, ultimately improving unconventional reservoir recovery rates. However, existing thickeners still suffer from limitations in thickening capacity, the need for significant cosolvents, and potential reservoir damage. Therefore, it is imperative to further enhance the performance of thickeners and explore the possibility of adding new nanomaterials.

Acoustic-scene-related parameters such as relative transfer functions (RTFs) and power spectral densities (PSDs) of the target source, late reverberation and ambient noise are essential for microphone array signal processing and are challenging to estimate. Existing methods typically only estimate a subset of the parameters by assuming the other parameters are known. This can lead to unmatched scenarios and reduced estimation performance on the parameters of interest. Moreover, many methods process time frames independently, despite they share common information such as the same RTF. In this work, we consider a noisy scenario by modelling the noise component as a spatially homogeneous sound field with a time-invariant spatial coherence matrix and time-varying PSD. We first modify an existing alternating least squares (ALS) method to obtain more accurate estimates using a single time frame. Then, we extend the method to use multiple time frames that share the same RTF. Furthermore, we propose more robust constraints on the PSDs to avoid large estimation errors. We compare our proposed methods to the state-of-the-art simultaneously confirmatory factor analysis (SCFA) method, a joint maximum likelihood estimation (JMLE) method and an existing ALS-based method. The experimental results in terms of estimation accuracy, noise reduction performance, predicted speech quality, and predicted speech intelligibility demonstrate that our proposed methods achieve similar performance compared to the state-of-the-art SCFA method, which outperforms the existing ALS method in all scenarios and outperforms the JMLE method particularly in low SNR scenarios. Moreover, our proposed methods have significantly lower computational complexity than SCFA.

Estimation of the acoustic-scene related parameters such as relative transfer functions (RTFs) from source to microphones, source power spectral densities (PSDs) and PSDs of the late reverberation is essential and also challenging. Existing maximum likelihood estimators typically consider only subsets of these parameters and use each time frame separately. In this paper we explicitly focus on the single source scenario and first propose a joint maximum likelihood estimator (MLE) to estimate all parameters jointly using a single time frame. Since the RTFs are typically invariant for a number of consecutive time frames we also propose a joint maximum likelihood estimator (MLE) using multiple time frames which has similar estimation performance compared to a recently proposed reference algorithm called simultaneously confirmatory factor analysis (SCFA), but at a much lower complexity. Moreover, we present experimental results which demonstrate that the estimation accuracy, together with the performance of noise reduction, speech quality and speech intelligibility, of our proposed joint MLE outperform those of existing MLE based approaches that use only a single time frame.

For hearing-impaired listeners, both ambient noise suppression and binaural cues preservation of directional sources are required, such that a complete spatial awareness of the acoustic scene can be obtained. It was shown that the binaural multichannel Wiener filter (MWF) with partial noise preservation can achieve joint noise reduction and binaural cues preservation and incorporating an external microphone signal improves the performance of binaural MWFs. Motivated by this, we propose a binaural MWF incorporating external wireless devices in this paper. First, we theoretically analyze the performance of the MWF in terms of output signal-to-noise ratio (SNR) and binaural cues preservation errors. As in practice the external devices are power driven with a limited amount of battery resource and the power consumption heavily depends on the transmission rate, given an expected noise reduction performance we then optimize the bit-rate for a single external microphone. Further, we consider to minimize the total power consumption over multiple external devices under a constraint on the output SNR, which turns out to be a rate distribution problem. The proposed rate-distributed binaural MWF is evaluated using a hearing-aid setup with various dynamics. It is shown that the proposed method can obtain a desired SNR at a much lower bit-rate, and an expected trade-off between SNR gain and binaural cues preservation accuracy can be obtained by optimizing the bit-rates. Increasing the bit-rates improves both instrumental speech quality and speech intelligibility.

As the world enters the era of the Internet of Things (IoT), wireless devices and their networks become essential fundamental components. Recently, with the rapid development of the triboelectric nanogenerator (TENG), breakdown discharge has become an emerging hot topic in the field since it is the key limiting factor of the output performance, and it may also trigger new applications such as self-powered wireless sensing. However, understandings of the discharge behaviors in TENG are still limited. This study proposed a method to study the breakdown discharge with a large serial resistance and discovered the time-lag behavior of the breakdown discharge. A model based on the Eyring equation is demonstrated to explain this time-lag phenomenon. A convenient method to adjust the breakdown-discharge voltage is developed through this study. As an application, a wireless spark switch being modulated by a series-connected resistance is designed, which may be potentially utilized in wireless applications.