17 research outputs found
Nitrogen doped TiO2 catalysts for gas-phase visible photo catalysis synthesized by flame technique
No full textIn recent years there has been an increasing interest in developing catalysts for visible light photocatalytic degradation of volatile organic compounds. Compared to other dopants, N doped TiO2 catalysts shown higher efficiency in utilizing the visible light of the solar spectrum. So far, several wet methods have been employed for synthesizing N doped TiO2 materials. The Flame Spray Pyrolysis (FSP) technique has better control on crystal structure, particle size and its distribution compared to the wet methods. The present study mainly aimed at developing N doped TiO2 materials using flame technique for the destruction of VOCs in the gas phase. For this purpose, various nitrogen doped TiO2 catalysts are synthesized by FSP method using different nitrogen sources like NH4Cl, CH4N2O, NH4NO3, CH5N, C3H9N, (NH4)2CO3, and CH2N2. A combination of various physico-chemical characterizations such as N2 physisorption, O2 chemisorption, X-ray diffraction (XRD), diffuse reflectance UV-vis (DR UV-vis), FT-IR, Raman, and temperature program reduction (TPR) were used to characterize the chemical environment of these materials. The UV-vis spectroscopy results of N incorporated TiO2 showed enhancement of light absorption in the visible range by selected composite materials in the UV range (250-400 nm). Catalytic activity measurements show that the activity of N doped TiO2 depends on the precursor used. These interesting results will be discussed in the presentation
Visible-light-induced photodegradation of gas phase acetonitrile using aerosol-made transition metal (V, Cr, Fe, Co, Mn, Mo, Ni, Cu, Y, Ce, and Zr) doped TiO2
No full textA series of semiconductor photocatalysts based on transition metals (M'=V, Cr, Fe, Co, Mn, Mo, Ni, Cu, Y, Ce, and Zr) incorporated TiO2 (Ti-M'=20 atomic ratio) materials have been synthesized by adopting a one-step liquid flame aerosol synthesis technique. The resulting materials were explored for the destruction of acetonitrile in gas phase under visible light irradiation at ambient conditions. Our H2-TPR studies revealed the formation of MeOTi bonds, which suggest the strong interaction of dopant metal-TiO2 in all the as-synthesized materials. The reduction peaks in Cr-doped TiO2 shifted to much lower temperatures, due to the increase in the reduction potential of titania and chromium. The strong interaction (formation of CrOTi bonds) is the main reason that the Cr-TiO2 is an active photocatalyst in visible light. Our XPS studies suggest that the relative atomic percentage value of Ti3+-Ti4+ characterized by XPS was significantly high for our flame-made Cr-TiO2 nanoparticles (Ti3+-Ti4+=0.89, 32.9percent), whereas, other samples demonstrated poor atomic percentage value of Ti3+-Ti4+ (Ti3+-Ti4+=0.08-0.32). The existence of Ti3+ species with narrow band gap is highly beneficial for the promotion of visible light-induced photocatalytic activity. The position of the Cr 2p peaks shifted to lower binding energies in Cr-doped TiO2 nanoparticles. The electrons migrate from the TiO2 nanoparticles to chromium species, which reveals a strong interaction between Cr and TiO2 nanostructure in the interface of flame-made nanoparticles. Conversely, Mn3+ species combined with TiO2 because its surface metal dispersion was kept high after TiO2 loading. However, Mn3+ incorporated catalyst was inactive because of the small energy driving force for electrons to detrap from Mn2+. The UV-vis spectroscopy results of M'-doped TiO2 (M'=Fe, Cr, V, Co, Ce, and Ni) materials showed augmentation of light absorption in the visible range. The Cr, V and Fe (Ti:M' atomic ratio=20:1) titania aerosol catalysts reduced the bandgap energy of TiO2 to 2.9eV under visible light irradiation. Among all of the catalysts we tested, the transition metals (M'=Cr, Fe, and V) incorporated materials have shown an impressive catalytic performance in visible light. Among all the catalyst tested, Cr-doped titania demonstrated a superior catalytic performance and the rate constant is about 8-19 times higher than the rest of the metal doped catalysts. 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Over What Range Should Reliabilists Measure Reliability?
No full textProcess reliabilist accounts claim that a belief is justified when it is the result of a reliable belief-forming process. Yet over what range of possible token processes is this reliability calculated? I argue against the idea that all possible token processes (in the actual world, or some other subset of possible worlds) are to be considered using the case of a user acquiring beliefs based on the output of an AI system, which is typically reliable for a substantial local range but unreliable when all possible inputs are considered. I show that existing solutions to the generality problem imply that these cases cannot be solved by a more fine-grained typing of the belief-forming process. Instead, I suggest that reliability is evaluated over a range restricted by the content of the actual belief and by the similarity of the input to the actual input.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Ethics & Philosophy of Technolog
Causal scientific explanations from machine learning
No full textMachine learning is used more and more in scientific contexts, from the recent breakthroughs with AlphaFold2 in protein fold prediction to the use of ML in parametrization for large climate/astronomy models. Yet it is unclear whether we can obtain scientific explanations from such models. I argue that when machine learning is used to conduct causal inference we can give a new positive answer to this question. However, these ML models are purpose-built models and there are technical results showing that standard machine learning models cannot be used for the same type of causal inference. Instead, there is a pathway to causal explanations from predictive ML models through new explainability techniques; specifically, new methods to extract structural equation models from such ML models. The extracted models are likely to suffer from issues though: they will often fail to account for confounders and colliders, as well as deliver simply incorrect causal graphs due to ML models tendency to violate physical laws such as the conservation of energy. In this case, extracted graphs are a starting point for new explanations, but predictive accuracy is no guarantee for good explanations.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Ethics & Philosophy of Technolog
Defining Explanation and Explanatory Depth in XAI
No full textExplainable artificial intelligence (XAI) aims to help people understand black box algorithms, particularly of their outputs. But what are these explanations and when is one explanation better than another? The manipulationist definition of explanation from the philosophy of science offers good answers to these questions, holding that an explanation consists of a generalization that shows what happens in counterfactual cases. Furthermore, when it comes to explanatory depth this account holds that a generalization that has more abstract variables, is broader in scope and/or more accurate is better. By applying these definitions and contrasting them with alternative definitions in the XAI literature I hope to help clarify what a good explanation is for AI.Ethics & Philosophy of Technolog
Why and How Should We Explain AI?
No full textWhy should we explain opaque algorithms? Here four papers are discussed that argue that, in fact, we don’t have to. Explainability, according to them, isn’t needed for trust in algorithms, nor is it needed for other goals we might have. I give a critical overview of these arguments, showing that there is still room to think that explainability is required for responsible AI. With that in mind, the second part of the paper looks at how we might achieve this end goal. I proceed not from technical tools in explainability, but rather highlight accounts of explanation in philosophy that might inform what those technical tools should ultimately deliver. While there is disagreement here on what constitutes an explanation, the three accounts surveyed offer a good overview of the current theoretical landscape in philosophy and of what information might constitute an explanation. As such, they can hopefully inspire improvements to the technical explainability tools.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Values Technology and InnovationEthics & Philosophy of Technolog
Building blocks for a cognitive science-led epistemology of arithmetic
No full textIn recent years philosophers have used results from cognitive science to formulate epistemologies of arithmetic (e.g. Giaquinto in J Philos 98(1):5–18, 2001). Such epistemologies have, however, been criticised, e.g. by Azzouni (Talking about nothing: numbers, hallucinations and fictions, Oxford University Press, 2010), for interpreting the capacities found by cognitive science in an overly numerical way. I offer an alternative framework for the way these psychological processes can be combined, forming the basis for an epistemology for arithmetic. The resulting framework avoids assigning numerical content to the Approximate Number System and Object Tracking System, two systems that have so far been the basis of epistemologies of arithmetic informed by cognitive science. The resulting account is, however, only a framework for an epistemology: in the final part of the paper I argue that it is compatible with both platonist and nominalist views of numbers by fitting it into an epistemology for ante rem structuralism and one for fictionalism. Unsurprisingly, cognitive science does not settle the debate between these positions in the philosophy of mathematics, but I it can be used to refine existing epistemologies and restrict our focus to the capacities that cognitive science has found to underly our mathematical knowledge.Ethics & Philosophy of Technolog
Transparency for AI systems: A value-based approach
No full textWith the widespread use of artificial intelligence, it becomes crucial to provide information about these systems and how they are used. Governments aim to disclose their use of algorithms to establish legitimacy and the EU AI Act mandates forms of transparency for all high-risk and limited-risk systems. Yet, what should the standards for transparency be? What information is needed to show to a wide public that a certain system can be used legitimately and responsibly? I argue that process-based approaches fail to satisfy, as knowledge about the development process is insufficient to predict the properties of the resulting system. Current outcome-based approaches [Mitchell et al., 2019; Loi et al., 2021] are also criticized for a lack of attention to the broader socio-technical system and failure to account for empirical results that show that people care about more than just the outcomes of a process [as reported by Meyerson et al. (Procedural justice and relational theory: Empirical, philosophical, and legal perspectives, Taylor & Francis, 2021)]. Instead, I propose value-based transparency, on which the information we need to provide is what values have been considered in the design and how successful these have been realized in the final system. This can handle the objections to other frameworks, matches with current best practices on the design of responsible AI and provides the public with information on the crucial aspects of a system’s design.Ethics & Philosophy of Technolog
Second-order characteristics don't favor a number-representing ANS
No full textClarke and Beck argue that the ANS doesn't represent non-numerical magnitudes because of its second-order character. A sensory integration mechanism can explain this character as well, provided the dumbbell studies involve interference from systems that segment by objects such as the Object Tracking System. Although currently equal hypotheses, I point to several ways the two can be distinguished.Ethics & Philosophy of Technolog
Spotting When Algorithms Are Wrong
No full textUsers of sociotechnical systems often have no way to independently verify whether the system output which they use to make decisions is correct; they are epistemically dependent on the system. We argue that this leads to problems when the system is wrong, namely to bad decisions and violations of the norm of practical reasoning. To prevent this from occurring we suggest the implementation of defeaters: information that a system is unreliable in a specific case (undercutting defeat) or independent information that the output is wrong (rebutting defeat). Practically, we suggest to design defeaters based on the different ways in which a system might produce erroneous outputs, and analyse this suggestion with a case study of the risk classification algorithm used by the Dutch tax agency.Ethics & Philosophy of Technolog
