Directory contains scene files, image data, run files and results accompanying: Wasileski S. and M. Andersen. Exploring interactions between shading and view using visual difference prediction (2025) Buildings & Cities

This archive contains the data used to calibrate and validate the linearhdr software and is a supplement to the publication describing the method and dataset of HDR environment maps. Raw images may be available on request so long as a means of transferring the 100+ GB of compressed data is provided. Contact the author (at email attached to orcid ID).

With the initiatives like Collections as Data and Computational Archival Science, archives are no longer seen as a static documentation of objects, but evolving sources of cultural and historical data. This work emphasizes the potential updates in preserving and documenting digital audiovisual (AV) content from a data perspective, considering the recent developments in natural language processing and computer vision tasks, as well as the emergence of interactive and embodied experiences and interfaces for innovatively accessing archival content. As part of Swiss national scientific fund Sinergia project, this work was able to work end-to-end with real-world AV archives like Télévision Suisse Romande (RTS). Resorting to an updated narrative model for mapping data that can be obtained from the content as well as the consumer, this work proposed an experimental attempt to build an ontology to formally sum up the potential new paradigm for preservation and accessibility from a data perspective for modern archives, in the hope for nurturing a digital and data driven mind-set for archive practices.

Twisted graphene based moiré heterostructures host a flat band at the magic angles where the kinetic energy of the charge carriers is quenched and interaction effects dominate. This results in emergent phases such as superconductors and correlated insulators that are electrostatically tunable. We investigate superconductivity in twisted trilayer graphene by integrating it as the weak link in a superconducting quantum interference device. The measured current phase relation yields a large and tunable kinetic inductance, up to 150 nH per square, of the electron and hole-type intrinsic superconductors. We further show that the specific kinetic inductance and the critical current density are universally related via the superconducting coherence length, and extract an upper bound of ∼200  nm for the coherence length. We discuss the implications of a large coherence length in twisted graphene superconductors.

Arsenic (As) is a toxic metalloid that occurs naturally and is widely distributed in the environment. The inorganic compounds arsenite (As(III)) and arsenate (As(V)) are the most prevalent As species in the biosphere, the former being predominant in reducing environments. Some microbes can methylate As(III) to produce methylated As compounds. This biotransformation alters the fate and toxicity of As, and is a key component of its biogeochemical cycle. Arsenic methylation is often observed in rice paddy fields, where it is enhanced upon soil flooding (i.e., under anoxic conditions). The methylated products can be absorbed by the rice plant through the roots and accumulate in the grains, and can also induce the sterility of the plant. This microbial transformation may thus pose a threat to both food security and safety. However, very few anaerobic As-methylating microbes have been isolated, which precludes further understanding of the controls on this transformation and its biological function. Their isolation is laborious because traditional techniques for the isolation of environmental anaerobes are time-consuming, and the As-methylating phenotype cannot be easily screened. To tackle these challenges, we developed an alternative isolation approach which consists of trapping and growing individual soil microbes within permeable compartments (hydrogel capsules), and subsequent sorting using fluorescence-activated cell sorting (FACS) to distribute compartmentalized isolates in microwell plates for further growth. This approach enabled the cultivation of anaerobic taxa which fail to grow on agar-based medium. Further, we employed a bacterial biosensor that fluorescently responds to methylated As to functionally screen for the isolates showing methylation capacity. This approach allowed us to rapidly isolate anaerobic As-methylating strains from a paddy soil.

The chemical industry faces mounting pressure to develop more sustainable processes while accelerating innovation to address climate challenges. Traditional discovery approaches in chemistry follow an iterative cycle: forming hypotheses about new molecules or reactions, testing these hypotheses experimentally, analyzing the results, and using these insights to refine future hypotheses. Each stage presents unique challenges that can create bottlenecks in the discovery process. At the hypothesis stage, researchers must navigate vast chemical spaces to identify promising candidates. During testing, optimal reaction conditions must be determined, focusing on reactivity while also considering sustainability. Analysis requires the interpretation of complex spectroscopic data, and the refinement stage demands efficient integration of all gathered information to guide future experiments.

Digital chemistry methods offer promising approaches to accelerate this discovery cycle. By leveraging artificial intelligence, machine learning, and optimization techniques, these methods can enhance each stage of the process while promoting more sustainable practices. This work investigates how these computational tools can be effectively deployed across the entire discovery workflow, demonstrating their potential through several case studies.

Following the cycle we first showcase the potential of generative AI to develop new hypotheses by creating new molecules with desired properties. In our case we propose new catalyst candidates for the Suzuki cross-coupling with desired binding energies. Subsequently, to support the testing phase with digital methods, transformer-based models for solvent recommendation were developed to predict suitable solvents for chemical reactions while suggesting greener alternatives. The effectiveness of these recommendations was successfully experimentally validated. The next step is automating the analysis, showcased by a case study on assisting in the interpretation of 1H-NMR spectra. The attention mechanisms in a transformer model were leveraged to establish a mapping between 1H-NMR spectral peaks and molecular substructures, achieving high accuracy in assigning the experimental spectra to the correct molecule. And lastly, the iterative formation of new hypotheses on previous experiments was accelerated using Bayesian optimization in combination with automated synthesis hardware. This combination enabled the efficient optimization of iodoalkyne synthesis across multiple starting materials while exploring only a small part of the potential parameter space.

Recognizing that the impact of digital chemistry tools depends heavily on their accessibility, we highlight a potential method to increase accessibility by packaging existing chemistry AI tools into an App, that does not requiring coding knowledge and focused on local execution. Finally, an examination of the environmental footprint of computational chemistry methods themselves emphasizes the importance of balancing their benefits for sustainable chemistry against their own resource consumption.

Through these case studies, we demonstrate how digital chemistry methods can significantly accelerate the discovery cycle while promoting sustainable practices, establishing a framework where computational tools complement and enhance experimental approaches rather than replace them.

Cyclopropanes have emerged as powerful C3 building blocks in organic synthesis. Cyclopropanes serve as precursors to generate reactive intermediates in various total syntheses of natural products and bioactive molecules. Several strategies have been developed to efficiently promote C-C bond cleavage, such as Lewis acid activation and transition metal-catalyzed oxidative addition. However, these methods still face limitations, including the use of expensive transition metals, the necessity for directing groups or chelating sites, and sometimes the requirement to conduct reactions at high temperatures. With an increasing focus on sustainable chemistry, photochemistry has emerged as an effective tool to initiate radical fragmentation of the C-C bond under mild conditions. Our objective is to utilize light energy to facilitate C-C bond cleavage in cyclopropanes through energy transfer catalysis or by photoexcitation of hypervalent iodine reagents to promote oxidative activation. In this context, we first developed a photocatalyst-free method for the alkynylation of aryl cyclopropanes through the direct excitation of a hypervalent iodine reagent. We found that the reaction outcome could be completely switched from C-C to C-H alkynylation when using cyclopropyl arenes that contain two ortho substituents on the aromatic ring. In addition, the same condition was successfully applied for the 1,3-oxyalkynylation of amino cyclopropanes and 1,2-oxyalkynylation of styrene derivatives. Overall, a wide range of alkynylated products were obtained under simple reaction conditions. Subsequently, we employed a Willgerodt-type reagent to perform photo-mediated chlorination of aryl cyclopropanes, olefins and activated C-H bonds. Building on the simplicity of this reaction, we developed a one-pot protocol for substitution reactions involving benzylic chlorides with nucleophiles, resulting in the formation of C-C, C-N, C-O, and C-S bonds. Additionally, we presented preliminary results on a novel photocatalytic iodine I/III process, which opens new possibilities for future advancements in this field. We then further targeted the activation of carbonyl cyclopropanes via energy transfer catalysis. Particularly, we employed alkynes and olefins for the annulation with carbonyl cyclopropanes, giving a wide range of 5-membered carbocycles. We also utilized bicyclo[1.1.0]butane derivatives as coupling partners for the cyclization with cyclopropyl ketones, resulting in the synthesis of the bicyclo[3.1.1]heptane scaffold. We expected that the bicyclo[3.1.1]heptane products could be used as bioisosteres for 1,2,4,5 tetra-substituted arenes. Through radical trapping experiments and computational studies, we demonstrated that a 1,3-biradical was formed by the excitation of cyclopropanes, which serves as a key intermediate in this transformation. Lastly, we conducted a comprehensive study on the generation of biradical via direct excitation carbonyl cyclopropanes. This led to the discovery of diverse reactivities, including [3+2] annulation and photo rearrangement. Based on these findings, we established a unified reductive ring-opening process. Through a sequence of cyclopropanation and reductive ring-opening reactions, we successfully developed the homologation of alkenyl carbonyl compounds. This method provides a novel approach to modify carbocycles, aligning with the rising interest in "skeletal editing" of organic compounds.

Context. Technical and social innovations are central to the transition to renewable energy systems. Despite the availability of innovative technologies for renewable energy production, management, and e-mobility, their widespread societal adoption remains limited. Proximity between actors is key for energy technologies' diffusion. Yet, it is challenging to bring into a cohesive interdisciplinary conceptualisation.

Goal. This thesis examines proximity in the diffusion of energy innovations in Switzerland. I conceptualise proximity related to innovation-diffusion as relational and circumstantial. Relational proximity is studied by drawing on information networks to examine connectivity and diversity within five proximity dimensions: geographical, social, cognitive, organisational and institutional. I analyse circumstantial proximity by researching which socio-spatial contexts bring actors into situations where they exchange information and what characterises these situations.

Methods. Using data from 36 interviews with Swiss energy actors and surveys with 157 professionals and 4,000 adopters of photovoltaic panels, electric vehicles, and energy management systems, I analysed information exchanges that led to the diffusion of these technologies. I included exchanges among professionals, between professionals and adopters, and between adopters and their personal contacts. Results. Proximity relates to the diffusion of energy innovations through linking infrastructures that create the potential for interactions and circumstances that generate spaces of interface where information can be exchanged. The relational proximity results show linking infrastructures are geographically and socially connected and introduce a balance of actors with similar and diverse cognitive, institutional and organisational characteristics. Circumstantial proximity reveals face-to-face interactions to be key for the diffusion of innovations, allowing the flow of tacit knowledge and the development of trust. Specific contexts foster spaces of interface: a) diverse urban environments that increase interactions with people outside of the closest circle, b) socio-economic conditions that allow actors to have these interactions and, c) professionals and convinced adopters that support less urban and innovative ones.

Discussion. The interrelations between relational and circumstantial proximity highlight three avenues to nurture spaces of interface for innovation-diffusion. First, an appropriate configuration of the space, through densification, urban planning, design or event organisation, can be key to increasing connecting dimensions and opportunities for interaction. Second, establishing trustful relationships is important to transform these interactions into diffusion. Third, the integration of people from different backgrounds in social circles supports opportunities to discuss with "others" bringing novel information, and encourages diffusion from early adopters to the people from different sociodemographic backgrounds.

Conclusion. By complementing relational with circumstantial proximity, this research enriches the theory of the diffusion of innovations and offers practical recommendations for advancing the diffusion of energy technologies in Switzerland. It highlights the importance of socio-spatially mixed contexts that also foster trust and openness for innovation to flow in society.

Modern cosmological models include a nearly neutral, non-baryonic form of matter with extremely low speed in the context of structure formation, commonly known as ``cold dark matter''. Astrophysical observations indicate that dark matter constitutes 26.4% of the Universe's critical density and 84.4% of its total matter content. However, the nature of dark matter is still unknown. One of the most promising dark-matter candidates is the neutralino $\chi$, which could annihilate via the process $\chi \bar{\chi} \rightarrow X \gamma$, where $X$ represents either a $\gamma$, $Z$ or $H$ boson. In the approximation of non-relativistic neutralinos, this interaction may lead to monoenergetic gamma-ray emissions, which would represent smoking-gun signatures in the gamma-ray energy spectrum. The main objective of this thesis is the search for spectral lines in the gamma-ray energy flux using eight years of data collected with the space-borne Dark Matter Particle Explorer~(DAMPE). An efficient gamma-ray selection algorithm is developed, using both standard selection criteria and machine learning methods. A precise fitting method within reduced energy windows and a statistical significance evaluation tool are used to search for dark-matter annihilation lines in the gamma-ray energy spectrum. No line signal is detected between 5~GeV and 1~TeV across several regions of interest in the sky. Nevertheless, upper limits at the 95% confidence level are set on the dark-matter annihilation-induced gamma-ray flux. Constraints on the velocity-averaged cross section for neutralino annihilation into two gamma rays are estimated for different dark-matter density profiles.