ABSTRACT
Electrochemical deionization (ECDI) has emerged as a promising technology for water treatment, with faradaic ECDI systems garnering significant attention due to their enhanced performance potential. This study focuses on the development of a highly stable and efficient, full-polymer (polypyrrole, PPy) ECDI system based on two key strategies. Firstly, dopant engineering, involving the design of dopants with a high charge/molecular weight (MW) ratio and structural complexity, facilitating their effective integration into the polymer backbone. This ensures sustained contribution of strong negative charges, enhancing system performance, while the bulky dopant structure promotes stability during extended operation cycles. Secondly, operating the system with well-balanced charges between deionization and concentration processes significantly reduces irreversible reactions on the polymer, thereby mitigating dopant leakage. Implementing these strategies, the PPy(PSS)//PPy(ClO4) (PSS: polystyrene sulfonate) system achieves a high salt removal capacity (SRC) of 48 mg g-1, an ultra-low energy consumption (EC) of 0.167 kW h kgNaCl-1, and remarkable stability, with 96% SRC retention after 104 cycles of operation. Additionally, this study provides a detailed degradation mechanism based on pre- and post-cycling analyses, offering valuable insights for the construction of highly stable ECDI systems with superior performance in water treatment applications.
ABSTRACT
Manganese oxide is an effective active material in several electrochemical systems, including batteries, supercapacitors, and electrochemical deionization (ECDI). This work conducts a comprehensive study on the ion-selective behavior of MnOx to fulfill the emptiness in the energy and environmental science field. Furthermore, it broadens the promising application of MnOx in the ion-selective ECDI system. We propose a time-dependent multimechanism ion-selective behavior with the following guidelines by utilizing a microfluidic cell and the electrochemical quartz crystal microbalance (EQCM) analysis. (1) Hydrated radius is the most critical factor for ions with the same valence, and MnOx tends to capture cations with a small hydrated radius. (2) The importance of charge density rises when comparing cations with different valences, and MnOx prefers to capture divalent cations with a strong electrostatic attraction at prolonged times. Under this circumstance, ion swapping may occur where divalent cations replace monovalent cations. (3) NH4+ triggers MnOx dissolution, leading to performance and stability decay. The EQCM evidence has directly verified the proposed mechanisms, and these data provide a novel but simple method to judge ion selectivity preference. The overall ion selectivity sequence is Ca2+ > Mg2+ > K+ > NH4+> Na+ > Li+ with the highest selectivity values of ßCa//Li and ßCa//Na around 3 at the deionization time = 10 min.
ABSTRACT
Neural indicators of pain discriminability have far-reaching theoretical and clinical implications but have been largely overlooked previously. Here, to directly identify the neural basis of pain discriminability, we apply signal detection theory to three EEG (Datasets 1-3, total N = 366) and two fMRI (Datasets 4-5, total N = 399) datasets where participants receive transient stimuli of four sensory modalities (pain, touch, audition, and vision) and two intensities (high and low) and report perceptual ratings. Datasets 1 and 4 are used for exploration and others for validation. We find that most pain-evoked EEG and fMRI brain responses robustly encode pain discriminability, which is well replicated in validation datasets. The neural indicators are also pain selective since they cannot track tactile, auditory, or visual discriminability, even though perceptual ratings and sensory discriminability are well matched between modalities. Overall, we provide compelling evidence that pain-evoked brain responses can serve as replicable and selective neural indicators of pain discriminability.
