Kinetics and mechanism of model reactions in thermoresponsive nanoreactors

Zwei Modellreaktionen wurden mit thermoresponsiven Nanoreaktoren untersucht. Die Reduktion von 4-Nitrophenol und von Kaliumhexacyanidoferrat(III) mit Natriumborhydrid. Die Nanoreaktoren bestehen aus einem Polystyrol Kern, umgeben von einer Hydrogel Schale aus Poly-(N-Isopropylacrylamid). Die Reaktionen werden auf der Oberfläche von Metall Nanopartikeln in der Hydrogel Schale katalysiert. In einer auf Gold- und Silberkatalysatoren fokussierten Literaturstudie zeigte sich, dass der geschwindigkeitsbestimmende Reaktionsschritt zwischen beiden Metallen variieren könnte. Kinetische Studien mit Silber haben gezeigt, dass ein erfolgreich auf Gold angewandtes Modell modifiziert werden muss um auf Silber anwendbar zu sein und haben gezeigt, dass sich die Kinetik der Reaktion auf beiden Metallen unterscheidet. Die weitere Analyse ergab die typische, nicht der Arrhenius Abhängigkeit folgende, Abhängigkeit der Reaktionsrate von der Temperatur und hat gezeigt, dass die Partitionierung der Reaktanden im Hydrogel für das kinetische Modell relevant ist. Die Reduktion von Kaliumhexacyanidoferrat(III) auf Gold hat gezeigt, dass elektrostatische Effekte hier eine maßgebliche Rolle spielen. Ein kinetisches Modell wurde erarbeitet, dass die relevanten Einflussfaktoren durch Hydrogel, Geometrie der Nanoreaktoren, diffusions- und elektrostatische Effekte miteinbezieht. Die gewonnenen Daten konnten mittels eines auf der Auswertung des stationären Zustands basierenden Modells erfolgreich gefittet werden. Hierbei wurde das komplexe Zusammenspiel von elektrostatischen Effekten, deren Abschirmung und Einfluss auf die Diffusion sowie die Reaktionsrate gezeigt. Mit wenigen physikalisch aussagekräftigen Fitparametern konnten alle beobachteten Effekte erfolgreich erklärt werden. Der Vergleich der Reduktion von 4-Nitrophenol und von Hexacyanidoferrat(III) zeigt hierbei die entscheidenden Faktoren sowohl für reaktions- als auch für diffusionskontrollierte Reaktionen in thermoresponsiven Hydrogelen.Two model reactions were investigated with thermoresponsive core-shell nanoreactors, the reduction of 4-nitrophenol and of potassium hexacyanoferrate(III), both reduced with sodium borohydride. The nanoreactors comprise of a polystyrene core surrounded by a hydrogel shell of poly-N-isopropylacrylamide (PNIPAM) crosslinked with N,N’-methylenebisacrylamide. Metal nanoparticles are immobilized inside the hydrogel shell on the surface of which the model reactions are catalyzed. In the reduction of 4-nitrophenol, special emphasis is laid on the reduction on gold and silver catalysts. A literature review of mechanistic as well as kinetic studies reveals that the rate determining step may differ between the two catalyst metals. Kinetic investigations with a silver catalyst reveal that the kinetic model derived previously for gold catalysts needs to be modified for the kinetic analysis in this study, confirming a difference in the kinetics for both catalyst metals. The temperature dependent analysis reveals the typical non-Arrhenius dependency of the reaction rate and shows that the partition ratio of the reactants is relevant for the kinetics. The reduction of potassium hexacyanoferrate(III) on gold reveals that electrostatic effects play a major role in this reaction. A new kinetic model is derived, accounting the relevant influence factors of the hydrogel, the nanoreactor geometry, diffusional and electrostatic effects. With a stationary state approach the experimental data are fitted successfully, revealing the complex interplay of electrostatic effects, the screening thereof and the influence on diffusion and reaction rate. With only a few physically meaningful fit parameters all observed effects can be explained successfully. The comparison of the reduction of 4-nitrophenol and potassium hexacyanoferrate(III) highlights the decisive factors in both, reaction and diffusion controlled reactions inside thermoresponsive hydrogels

Language recovery of the New South Wales South Coast Aboriginal languages

The recent years have witnessed an increase in revisiting language descriptions of the ‘sleeping’ traditional languages of south-east Australia from available historic material. The languages of south-east New South Wales have thus far been largely neglected and this thesis fills a gap in the contemporary language work that has and still is being undertaken on traditional New South Wales languages. This research study investigates the traditional Aboriginal languages of the New South Wales South Coast. The languages presented here are Dharrawal, Dharumba, Dhurga and Djirringanj, which were spoken from the southern parts of Sydney and Botany Bay down along the coast, close to the Victorian border. The language material used for the analysis consists entirely of archival material that was collected by various people between ca. 1834 and 1902. Although previous work on the New South Wales South Coast languages (see Capell (n.d.) and Eades’ (1976)) offered insight into the structure of the languages, the available archival material has not been exhaustively utilised until now. Part B of this thesis presents the seventeen previously unanalysed texts transcribed by Andrew Mackenzie and Robert Hamilton Mathews during the latter half of the 19th Century. These texts supply a significant amount of additional morphological and syntactical information, and insights into narrative and discourse features; as well as mythologies of the South Coast people. Throughout the thesis, issues of working from archival material are appropriately discussed to clarify interpretation of the material and to introduce the reader to the stages and processes involved in working from historic material. This work is ultimately produced as a tool for local Aboriginal communities and community members to assist in current and future language reclamation and revitalisation projects, and to allow for projects to aim for higher language proficiency than has previously been possible

Adaptive manifold clustering

Clustering methods seek to partition data such that elements are more similar to elements in the same cluster than to elements in different clusters. The main challenge in this task is the lack of a unified definition of a cluster, especially for high dimensional data. Different methods and approaches have been proposed to address this problem. This paper continues the study originated by [6] where a novel approach to adaptive nonparametric clustering called Adaptive Weights Clustering (AWC) was offered. The method allows analyzing high-dimensional data with an unknown number of unbalanced clusters of arbitrary shape under very weak modeling as-sumptions. The procedure demonstrates a state-of-the-art performance and is very efficient even for large data dimension D. However, the theoretical study in [6] is very limited and did not re-ally address the question of efficiency. This paper makes a significant step in understanding the remarkable performance of the AWC procedure, particularly in high dimension. The approach is based on combining the ideas of adaptive clustering and manifold learning. The manifold hypoth-esis means that high dimensional data can be well approximated by a d-dimensional manifold for small d helping to overcome the curse of dimensionality problem and to get sharp bounds on the cluster separation which only depend on the intrinsic dimension d. We also address the problem of parameter tuning. Our general theoretical results are illustrated by some numerical experiments

Adaptive manifold clustering

Clustering methods seek to partition data such that elements are more similar to elements in the same cluster than to elements in different clusters. The main challenge in this task is the lack of a unified definition of a cluster, especially for high dimensional data. Different methods and approaches have been proposed to address this problem. This paper continues the study originated by [6] where a novel approach to adaptive nonparametric clustering called Adaptive Weights Clustering (AWC) was offered. The method allows analyzing high-dimensional data with an unknown number of unbalanced clusters of arbitrary shape under very weak modeling as-sumptions. The procedure demonstrates a state-of-the-art performance and is very efficient even for large data dimension D. However, the theoretical study in [6] is very limited and did not re-ally address the question of efficiency. This paper makes a significant step in understanding the remarkable performance of the AWC procedure, particularly in high dimension. The approach is based on combining the ideas of adaptive clustering and manifold learning. The manifold hypoth-esis means that high dimensional data can be well approximated by a d-dimensional manifold for small d helping to overcome the curse of dimensionality problem and to get sharp bounds on the cluster separation which only depend on the intrinsic dimension d. We also address the problem of parameter tuning. Our general theoretical results are illustrated by some numerical experiments

Adaptive Manifold Clustering

Clustering methods seek to partition data such that elements are more similar to elements in the same cluster than to elements in different clusters. The main challenge in this task is the lack of a unified definition of a cluster, especially for high dimensional data. Different methods and approaches have been proposed to address this problem. This paper continues the study originated by Efimov, Adamyan and Spokoiny (2019) where a novel approach to adaptive nonparametric clustering called Adaptive Weights Clustering (AWC) was offered. The method allows analyzing high-dimensional data with an unknown number of unbalanced clusters of arbitrary shape under very weak modeling assumptions. The procedure demonstrates a state-of-the-art performance and is very efficient even for large data dimension D. However, the theoretical study in Efimov, Adamyan and Spokoiny (2019) is very limited and did not really address the question of efficiency. This paper makes a significant step in understanding the remarkable performance of the AWC procedure, particularly in high dimension. The approach is based on combining the ideas of adaptive clustering and manifold learning. The manifold hypothesis means that high dimensional data can be well approximated by a d-dimensional manifold for small d helping to overcome the curse of dimensionality problem and to get sharp bounds on the cluster separation which only depend on the intrinsic dimension d. We also address the problem of parameter tuning. Our general theoretical results are illustrated by some numerical experiments

An approach to supervised learning of three valued Lukasiewicz logic in Hölldobler's core method

The core method [6] provides a way of translating logic programs into a multilayer perceptron computing least models of the programs. In [7] , a variant of the core method for three valued Lukasiewicz logic and its applicability to cognitive modelling were introduced. Building on these results, the present paper provides a modified core suitable for supervised learning, implements and executes supervised learning with the backpropagation algorithm and, finally, constructs a rule extraction method in order to close the neural-symbolic cycle

Learning Lukasiewicz logic

The integration between connectionist learning and logic-based reasoning is a longstanding foundational question in artificial intelligence, cognitive systems, and computer science in general. Research into neural-symbolic integration aims to tackle this challenge, developing approaches bridging the gap between sub-symbolic and symbolic representation and computation. In this line of work the core method has been suggested as a way of translating logic programs into a multilayer perceptron computing least models of the programs. In particular, a variant of the core method for three valued Łukasiewicz logic has proven to be applicable to cognitive modelling among others in the context of Byrne’s suppression task. Building on the underlying formal results and the corresponding computational framework, the present article provides a modified core method suitable for the supervised learning of Łukasiewicz logic (and of a closely-related variant thereof), implements and executes the corresponding supervised learning with the backpropagation algorithm and, finally, constructs a rule extraction method in order to close the neural-symbolic cycle. The resulting system is then evaluated in several empirical test cases, and recommendations for future developments are derived

Adaptive weights community detection

Due to the technological progress of the last decades, Community Detection has become a major topic in machine learning. However, there is still a huge gap between practical and theoretical results, as theoretically optimal procedures often lack a feasible implementation and vice versa. This paper aims to close this gap and presents a novel algorithm that is both numerically and statistically efficient. Our procedure uses a test of homogeneity to compute adaptive weights describing local communities. The approach was inspired by the Adaptive Weights Community Detection (AWCD) algorithm by [2]. This algorithm delivered some promising results on artificial and real-life data, but our theoretical analysis reveals its performance to be suboptimal on a stochastic block model. In particular, the involved estimators are biased and the procedure does not work for sparse graphs. We propose significant modifications, addressing both shortcomings and achieving a nearly optimal rate of strong consistency on the stochastic block model. Our theoretical results are illustrated and validated by numerical experiments