Electrochemical and Photoelectrochemical Investigation of Water Oxidation with Hematite Electrodes

Atomic layer deposition (ALD) was utilized to deposit uniform thin films of hematite (α-Fe2O3) on transparent conductive substrates for photocatalytic water oxidation studies. Comparison of the oxidation of water to the oxidation of a fast redox shuttle allowed for new insight in determining the rate limiting processes of water oxidation at hematite electrodes. It was found that an additional overpotential is needed to initiate water oxidation compared to the fast redox shuttle. A combination of electrochemical impedance spectroscopy, photoelectrochemical and electrochemical measurements were employed to determine the cause of the additional overpotential. It was found that photogenerated holes initially oxidize the electrode surface under water oxidation conditions, which is attributed to the first step in water oxidation. A critical number of these surface intermediates need to be generated in order for the subsequent hole-transfer steps to proceed. At higher applied potentials, the behavior of the electrode is virtually identical while oxidizing either water or the fast redox shuttle; the slight discrepancy is attributed to a shift in potential associated with Fermi level pinning by the surface states in the absence of a redox shuttle. A water oxidation mechanism is proposed to interpret these results

The Human Affectome

Over the last decades, the interdisciplinary field of the affective sciences has seen proliferation rather than integration of theoretical perspectives. This is due to differences in metaphysical and mechanistic assumptions about human affective phenomena (what they are and how they work) which, shaped by academic motivations and values, have determined the affective constructs and operationalizations. An assumption on the purpose of affective phenomena can be used as a teleological principle to guide the construction of a common set of metaphysical and mechanistic assumptions—a framework for human affective research. In this capstone paper for the special issue “Towards an Integrated Understanding of the Human Affectome”, we gather the tiered purpose of human affective phenomena to synthesize assumptions that account for human affective phenomena collectively. This teleologically-grounded framework offers a principled agenda and launchpad for both organizing existing perspectives and generating new ones. Ultimately, we hope Human Affectome brings us a step closer to not only an integrated understanding of human affective phenomena, but an integrated field for affective research

Attachment styles modulate neural markers of threat and imagery when engaging in self-criticism

Attachment styles hold important downstream consequences for mental health through their contribution to the emergence of self-criticism. To date, no work has extended our understanding of the influence of attachment styles on self-criticism at a neurobiological level. Herein we investigate the relationship between self-reported attachment styles and neural markers of self-criticism using fMRI. A correlation network analysis revealed lingual gyrus activation during self-criticism, a marker of visual mental imagery, correlated with amygdala activity (threat response). It also identified that secure attachment positively correlated with lingual gyrus activation, whilst avoidant attachment was negatively correlated with lingual gyrus activation. Further, at greater levels of amygdala response, more securely attached individuals showed greater lingual gyrus activation, and more avoidantly attached individuals showed less lingual gyrus activation. Our data provide the first evidence that attachment mechanisms may modulate threat responses and mental imagery when engaging in self-criticism, which have important clinical and broader social implications.Australian Postgraduate Scholarship

An analysis-ready and quality controlled resource for pediatric brain white-matter research

We created a set of resources to enable research based on openly-available diffusion MRI (dMRI) data from the Healthy Brain Network (HBN) study. First, we curated the HBN dMRI data (N = 2747) into the Brain Imaging Data Structure and preprocessed it according to best-practices, including denoising and correcting for motion effects, susceptibility-related distortions, and eddy currents. Preprocessed, analysis-ready data was made openly available. Data quality plays a key role in the analysis of dMRI. To optimize QC and scale it to this large dataset, we trained a neural network through the combination of a small data subset scored by experts and a larger set scored by community scientists. The network performs QC highly concordant with that of experts on a held out set (ROC-AUC = 0.947). A further analysis of the neural network demonstrates that it relies on image features with relevance to QC. Altogether, this work both delivers resources to advance transdiagnostic research in brain connectivity and pediatric mental health, and establishes a novel paradigm for automated QC of large datasets

Exploitation of properties of the MPC algorithms in fault detection

Algorytmy regulacji predykcyjnej dzięki sposobowi ich formułowania, w którym w umiejętny sposób wykorzystuje się model procesu, gwarantują bardzo dobrą jakość regulacji. Często ma to miejsce także w przypadku wystąpienia uszkodzenia w układzie regulacji. Wiele takich uszkodzeń może być nawet trudnych do zauważenia dzięki działaniu regulatora, jednak pożądane jest ich wykrycie. W artykule zaproponowano wykorzystanie w celu wykrywania uszkodzeń faktu, że w algorytmach regulacji predykcyjnej na bieżąco oceniana jest jakość modelu, na którym algorytm bazuje, przez porównywanie jego wyjścia z wartością wyjścia obiektu. Metoda może więc zostać użyta w algorytmach regulacji predykcyjnej dowolnego typu, bazujących zarówno na liniowych, jak i nieliniowych modelach obiektów.The model predictive control (MPC) algorithms due to their formulation and clever usage of the process model offer good control performance. It is also the case when some faults take place in the control system. Many of such faults can be even hard to notice thanks to the operation of the controller. It is, however, desired to be able to detect such situations. In the paper it is proposed to use internal signals of the MPC controllers in order to detect faults that occurred in the system. The method can be applied in the MPC algorithms based on both linear or nonlinear models

Block-structured models composed of nonlinear fuzzy dynamic and static parts : a case study

The paper addresses issues of the dynamic fuzzy Takagi- Sugeno models identification for multi-step ahead prediction. In the case of highly nonlinear models, standard Takagi-Sugeno models may be hard to identify if they should be designed for recurrent prediction generation. In such a case, alternative fuzzy block-structured models composed of fuzzy dynamic and fuzzy static parts may be useful. Two main benefits of the proposed models are: (1) possibility to speed-up model tuning procedure, (2) potential to fine-tune an already available, standard Takagi-Sugeno model. The benefits offered by the proposed models are illustrated using the example of identification of a nonlinear process – a system consisting of two tanks of different shapes (cylindrical and conical ones)

Effective dual-mode fuzzy DMC algorithms with on-line quadratic optimization and guaranteed stability

Dual-mode fuzzy dynamic matrix control (fuzzy DMC-FDMC) algorithms with guaranteed nominal stability for constrained nonlinear plants are presented. The algorithms join the advantages of fuzzy Takagi-Sugeno modeling and the predictive dual-mode approach in a computationally efficient version. Thus, they can bring an improvement in control quality compared with predictive controllers based on linear models and, at the same time, control performance similar to that obtained using more demanding algorithms with nonlinear optimization. Numerical effectiveness is obtained by using a successive linearization approach resulting in a quadratic programming problem solved on-line at each sampling instant. It is a computationally robust and fast optimization problem, which is important for on-line applications. Stability is achieved by appropriate introduction of dual-mode type stabilization mechanisms, which are simple and easy to implement. The effectiveness of the proposed approach is tested on a control system of a nonlinear plant-a distillation column with basic feedback controllers

Actuator fault tolerance in control systems with predictive constrained set-point optimizers

Mechanisms of fault tolerance to actuator faults in a control structure with a predictive constrained set-point optimizer are proposed. The structure considered consists of a basic feedback control layer and a local supervisory set-point optimizer which executes as frequently as the feedback controllers do with the aim to recalculate the set-points both for constraint feasibility and economic performance. The main goal of the presented reconfiguration mechanisms activated in response to an actuator blockade is to continue the operation of the control system with the fault, until it is fixed. This may be even long-term, if additional manipulated variables are available. The mechanisms are relatively simple and consist in the reconfiguration of the model structure and the introduction of appropriate constraints into the optimization problem of the optimizer, thus not affecting the numerical effectiveness. Simulation results of the presented control system for a multivariable plant are provided, illustrating the efficiency of the proposed approach