Programming the static friction of mechanical interfaces is critical for soft robotics, haptics, and precision gripping. Static friction is governed by the real contact area, and standard rough surfaces exhibit a linear area-load scaling inherent to classical models, severely restricting their functional range. Here, we propose a framework for the inverse design of tribological metainterfaces engineered for programmable contact behaviors. By utilizing general axisymmetric asperities, we unlock nonlinear macroscopic responses unattainable by standard Hertzian contacts. To solve the inverse problem, we embed a fully differentiable contact mechanics engine within a neural network and a quadratic optimizer. We leverage regularized physical gradients to automatically discover non-standard topographies that reproduce complex target contact laws, with only a few asperities in unit cells. The predicted designs are validated against high-fidelity boundary element method (BEM) simulations. By front-loading computational costs during training, the neural surrogate yields near-instantaneous design inference, bypassing the bottlenecks of traditional multi-start optimization. This framework bridges data-driven optimization and rigorous physics, offering a scale-invariant pathway for the high-throughput discovery and real-time control of functional tribological surfaces.

Fast pixelated detectors are driving 4D-STEM toward microsecond dwell times, a regime in which the finite response of the scan deflection coils becomes comparable to the dwell time itself. Using direct probe imaging and sub-frame diffraction analysis, we document a significant intra-dwell scan-coil delay that systematically smears the recorded signal anisotropically along the fast scan direction, with a settling timescale of several tens of microseconds. We present a phase-correlation-based sub-frame alignment procedure that measures and corrects this smearing, and we assess its impact on focused and defocused 4D-STEM reconstructions over a range of scan step sizes. The correction restores signal across a broad range of spatial frequencies, with the largest gains at the large step sizes required for low-dose biological imaging. Because it operates on existing data with no modification to the microscope, the method offers a practical route to recovering signal that would otherwise be lost to scan-coil delay.

A novel deep neural network classifier, a “particle transformer” (P ar T), is introduced for the identification of highly Lorentz-boosted resonances reconstructed as single, multipronged jets in measurements and searches performed by the CMS Collaboration at the CERN LHC. Based on a self-attention mechanism that allows the model to weigh the importance of different particles, P ar T is trained on a wide variety of topologies, notably demonstrating strong performance for the first time on jets originating from boosted Higgs boson decays to W bosons. The P ar T algorithm achieves a tagging efficiency of more than 50% for such jets at a background efficiency of 1%, while maintaining decorrelation from the jet mass. A calibration is performed in proton-proton collision data collected by CMS at a center-of-mass energy of 13 TeV, with a data set corresponding to a total luminosity of 138 fb − 1 . Data-to-simulation selection efficiency scale factors are measured to be in the 0.9–1.0 range, with relative uncertainties between 7 and 23%. The tagging capability of P ar T enhances the sensitivity of standard model measurements and searches for beyond-the-standard-model resonances decaying to hadronic diboson systems.

We present the weak gravitational lensing dataset from the Super-pressure Balloon-Borne Imaging Telescope (SuperBIT), which imaged 30 galaxy clusters during its 45 night flight in 2023 April–May. SuperBIT is a first-of-its-kind balloon-borne imaging telescope that achieved near-diffraction-limited observations in near-space conditions above 98% of the Earth’s atmosphere. We use the metacalibration algorithm to obtain calibrated galaxy shapes for our target clusters and several calibration fields, enabling unbiased reconstruction of the weak-lensing signal. We employ several diagnostics throughout the pipeline, including assessments of point-spread function (PSF) modeling residuals and their impact on weak-lensing measurements, as well as tests for correlations between galaxy shapes and measured galaxy and PSF properties. To assess the multiplicative shear bias of the pipeline, we analyze a parallel set of simulated images that incorporate the real observing conditions from the flight, including measured SuperBIT PSFs, observed sky backgrounds, and detector noise, yielding a bias of (1.54% ± 6.00%).

A series of esterified curcumin ligands containing structurally diverse acyl substituents were synthesized and used to prepare the corresponding neutral half-sandwich Ru(ii)–p-cymene complexes. All compounds were characterized by FT-IR, NMR spectroscopy, ESI mass spectrometry, and elemental analysis, while selected ligands and complexes were further confirmed by single-crystal X-ray diffraction. Density functional theory calculations provided additional insight into their structural and electronic properties. The antiproliferative activities of the ligands and ruthenium complexes were evaluated against human cancer cell lines (HepG2, Caco-2, MCF-7, A2780, and A2780cis) and non-tumorigenic controls (MCF10A and HEK293T). Overall, coordination to ruthenium enhanced the activity of the parent ligands, although the magnitude of this effect depended strongly on both the ester substituent and the cellular model. Among the compounds studied, the heptanoyl complex showed the highest activity in MCF-7 cells and was therefore selected for further investigation. DNA-binding studies, enzyme inhibition assays, and cellular biomarker analyses suggest that this compound combines increased DNA affinity with inhibition of the 20S proteasome and HMG-CoA reductase and promotes apoptotic signaling, as evidenced by DEVDase activity stimulation and Bcl-2 downregulation. Collectively, these results demonstrate that esterification of curcumin, together with Ru(ii)–arene coordination, is an effective strategy for tuning the anticancer profile of curcuminoid-based organoruthenium compounds.

In this thesis, we present the advances in self-organised nano-pillar lattice fabrication, including improvements in graphene (Gr) transfer onto targets in ultra-high vacuum (UHV), demonstrated increase of Gr capped self-organised cluster thermal stability, and creation of a high-density nano-pillar heterostructure achieved by second layer self-organised cluster growth on a transferred 2D material patterned by the clusters below it. We first present an ion intercalation-based graphene delamination method for the creation of a PTFE-supported graphene bilayer from which one monolayer is transferred onto the target sample using wafer bonding in UHV. With this method we demonstrate mm-scale graphene coverage onto Ir(111) and Irclust/Gr/Ir(111) targets in UHV. The novel method was tested alongside the acid etching-based bilayer preparation method and compared using Raman spectroscopy. The ion intercalation delamination method was found to result in higher quality and homogeneity bilayer graphene stamps, compatibility with graphene growth on reusable Pt substrates with larger grains further increasing graphene quality, as well as lower doping due to avoiding exposure to etchants. After transfer on Ir(111), scanning tunnelling microscopy (STM) imaging revealed significantly larger domain sizes of the monolayer Gr transferred. Additionally, Auger electron spectroscopy (AES) measurements were used to quantify graphene transfer coverage on a large scale, showing around 50% coverage over mm scale for both Ir(111) and Irclust/Gr/Ir(111) targets. Then we investigate the effects on thermal stability of Gr-capped Irclust/Gr/Ir(111) in UHV. STM imaging of samples prepared at varied temperatures revealed increased resistance against Ostwald ripening and peripheral diffusion, with filling factor values 20% - 30% higher in Gr-capped areas areas for a temperature range from 750 K to 1250 K, demonstrating the usefulness of the system for magnetic nano-pillars with high blocking temperature. Finally, the fabrication of a high-density self-organised nano-pillar heterostructure was demonstrated by subsequent cluster growth, Gr transfer, and growth of a second layer of Ir on Gr/Ir(111). The resulting Irclust/Grtransfer/Irclust/Gr/Ir(111) heterostructure has been investigated using STM, with second layer clusters shown to self-organise on top of Gr-capped clusters, and apparent height measurements demonstrating two Ir-layer high clusters grown on top of transferred graphene. An alternative system that could potentially be extended to 3D growth of self-organised Co clusters on Au(11,12,12) surface has been capped with Gr on the mm-scale as observed using AES, with capped areas showing a preserved self-organised pattern as investigated with STM. The demonstrated achievements offer the foundations for further development of nano-pillar heterostructure with the potential for high-density magnetic nano-pillars.

Optical frequency combs link optical frequencies to electronically countable microwave sig- nals. Kerr microresonators miniaturize these sources, but extending their bandwidth remains challenging. The central aim of this thesis is to explore routes to broader optical frequency combs through the joint engineering of dispersion, resonator geometry, and excitation in three integrated architectures. The first part combines pulsed pumping and dispersion engineering in a silicon nitride (Si3N4) Euler racetrack. Synchronous electro-optic pulses supply high peak power at moderate average power. Adiabatic bends suppress parasitic spatial-mode conversion, while dispersion engineering and broadband extraction support an octave-spanning soliton microcomb at an electronically detectable repetition rate. Radio-frequency and heterodyne measurements distinguish the soliton from modulation instability and verify coherence at widely separated wavelengths. The second part combines gallium phosphide with photonic-crystal Fabryâ Pérot cavities whose chirped reflectors supply anomalous round-trip dispersion. Picosecond subharmonic pumping overcomes the thermo-refractive access barrier, enabling the first room-temperature dissipative Kerr solitons in GaP. Heterodyne noise measurements reveal the transfer of timing noise from the electro-optic drive. A modified Lugiatoâ Lefever model separates the nonlinear detuning shift caused by counter-propagating fields from the period-four dynamics and interleaved subcombs imposed by the drive. Finally, the Vernier effect in coupled rings reshapes the resonance spectrum into recurring hybridized dispersion valleys. A transfer-matrix model guides two device generations and relates measured hybridization to broadband comb generation. The analysis identifies a novel four-wave-mixing mechanism involving intra- and interband photon transfer between these valleys, offering a new route to spectral expansion in coupled-resonator systems. Together, these studies show that useful broadband microcombs require joint control of dispersion, loss, coupling, thermal dynamics, and the drive. Future work will focus on demon- strating f â 2f self-referencing, integrating pump-pulse generation and compression on chip, and achieving phase-locked comb generation across multiple Vernier valleys.

Membrane-based gas separation offers an energy-efficient alternative to conventional separation processes, but practical membranes must combine high permeance, selectivity, stability, mechanical robustness, and scalable fabrication. Carbon materials are promising candidates for gas separation membranes because of their structural versatility and chemical and physical stability. When fabricated as ultrathin selective layers with well-controlled molecular-sieving pores or channels, carbon membranes can combine short transport pathways with strong size selectivity. This thesis focuses on the design, synthesis, and characterization of ultrathin carbon-based membranes for gas separation, aiming to control molecular-sieving pores and channels at the angstrom scale to discriminate gas molecules with small differences in kinetic diameter. Two representative transport geometries are investigated: through-plane nanopores in porous graphene and confined channels between carbon layers or domains. The first part studies bottom-up synthesis of porous nanocrystalline graphene as an ultrathin molecular-sieving membrane. Nanopores are incorporated during graphene growth rather than by post-synthetic etching. By controlling the cooling rate during precipitation-based growth on nickel, graphene grain intergrowth and effective pore size can be tuned. Rapid cooling reduces the effective pore size and improves H2/CH4 selectivity from 6.2 to 53.3, demonstrating a synthesis-based strategy for angstrom-scale pore tuning. The second part develops 10-nm-thick carbon membranes derived from poly(4-vinylpyridine) by pyrolysis under controlled oxygen exposure. Trace O2 modifies the decomposition pathway of the polymer precursor during pyrolysis and transforms the resulting carbon membrane from a relatively uniform network into a disrupted ultramicroporous structure. The resulting multimodal ultramicroporous channels provide rapid transport pathways while retaining molecular-sieving capability. These 10-nm-thick carbon membranes achieved H2 permeance above 10,000 GPU (gas permeation units, 1 GPU = 3.35 × 10 -10 mol m-2 s-1 Pa-1) and H2/N2 mixture selectivity above 200 at 150 °C, showing that oxygen-modulated pyrolysis can overcome the thickness limitation of conventional carbon molecular sieve membranes. The third part addresses the challenge of structural characterization in fragile porous graphene membranes. Reliable pore analysis requires clean, suspended, and mechanically stable specimens, but conventional polymer-assisted transfer can leave residues that obscure nanopores, while direct transfer can damage porous films. Therefore, a polymer-free carbon nanotube mesh scaffold, termed CMesh, is developed to mechanically reinforce porous graphene during transfer and imaging. This scaffold enables robust atomic-resolution TEM/STEM characterization of pre-existing porous graphene and in situ heating studies up to 900 °C, providing a practical platform for linking nanopore structure with membrane transport. Overall, this thesis demonstrates an integrated strategy for engineering ultrathin carbon-based membranes by connecting synthesis, angstrom-scale pore/channel control, gas separation performance, and atomic-resolution characterization. The developed approaches provide new routes to tune through-plane nanopores and confined ultramicroporous channels, contributing to the development of high-performance molecular-sieving membranes for energy-efficient gas separation.

Coral reefs are among the most biodiverse and most threatened ecosystems on Earth, declining under ocean warming and recurrent mass bleaching. Whether they persist depends on how fast corals can adapt. In this context, this thesis assesses the adaptive potential of the reef-building coral Acropora millepora in the Coral Sea from a holobiont perspective, reading the environmental seascape, the host genome, the algal symbionts and the colony phenotype together across fourteen reef sites in two contrasting regions: the Capricorn Bunker Group, at the southern tip of the Australian Great Barrier Reef, and the remote Chesterfield-Bellona plateau of New Caledonia.

The environmental seascape was characterised from satellite remote sensing, as eighty descriptors of thermal stress, water quality, salinity and currents computed over four time windows, and was used to guide sampling across the environmental range rather than across geography alone. Colony morphology, colour, health and microhabitat were scored from standardised in situ photographs of 268 colonies. The host genome of 236 colonies was resolved by low-coverage whole genome sequencing (lcWGS), and the algal symbionts of 237 colonies by ITS2 metabarcoding. Genome-environment associations were then scanned with LFMM2 and SAMbada, cross-checked against the environment-free PCAdapt selection scan, and integrated, alongside the symbionts and the phenotype, in redundancy analyses that projected a polygenic adaptive index across the reefs.

The two regions form two genetically distinct units, each effectively panmictic within itself, separated by low but consistent differentiation and with Chesterfield-Bellona carrying about three times the nucleotide diversity of the Capricorn Bunker Group. Within Chesterfield-Bellona, genetic differentiation increases with geographic distance, a stepping-stone pattern absent from the Capricorn. Phenotype, symbiont community and host genome separate the same two regions, the dominant Cladocopium type profile switching between them. Candidate loci associated with sea surface temperature anomalies and heat-stress metrics fall in molecular chaperones, an antioxidant transporter, and extracellular-matrix and cilia genes, a set that recurs in independent global coral studies. The adaptive index draws a warm, clear-water-adapted pole across the Chesterfield-Bellona plateau, strongest in its north, grading to a cooler, more turbid-adapted pole in the Capricorn Bunker Group. It is on the Chesterfield-Bellona plateau that the adaptive signal of the host, the symbiont community and colony health agree, a first step towards seascape genomics at the scale of the holobiont.

Because the two regions lie far apart, the regional contrast between them is confounded with the environment, the symbionts and the host structure, so adaptive signals within each region were harder to resolve. However, sampled in June and September 2023, months before the severe 2024 El Niño marine heatwave, the colonies offer a rare opportunity to test in the field whether the genotypes predicted to be heat-adapted actually survived. Conserving the adaptive potential of corals, in a climate that no longer holds still, means reading the host, the symbiont and the environment together.

Flavour-changing neutral current (FCNC) transitions such as b -> s mu+ mu- are forbidden at tree level in the Standard Model and proceed only through loop diagrams. Their consequently low rates make them sensitive probes of physics beyond the Standard Model at mass scales well above those directly accessible at colliders. This thesis presents a measurement of the differential branching fraction of the FCNC decay B+ -> K+ pi+ pi- mu+ mu-, in bins of the invariant mass squared of the dimuon system, q^2. The measurement uses proton-proton collision data recorded by the LHCb experiment during Run 1 and Run 2 of the LHC, corresponding to an integrated luminosity of 9 fb^-1. Signal candidates are isolated using a combination of cut-based requirements, dedicated vetoes against misidentified backgrounds, and boosted decision tree classifiers. Efficiencies are determined from simulation, corrected using data-driven techniques derived from the B+ -> K+ pi+ pi- J/psi(-> mu+ mu-) control mode, and modelled as a function of the decay phase space using a machine-learning approach that accommodates the high dimensionality of the five-body final state. Signal and normalisation yields are extracted from fits to the B+ invariant-mass distribution, and the branching fraction is measured relative to that of the B+ -> psi(2S)(-> mu+ mu-) K+ decay. The results present the most precise measurement of this decay channel to date, and are reported in intervals of q^2 between 0.1 and 19.0 GeV^2/c^4, including two alternative definitions of the region close to the kinematic limit. In this high-q^2 region, the signal significances are measured to be 2.8 sigma in q^2 in [15, 19] GeV^2/c^4, and 5.1 sigma in the wider q^2 in [14.3, 19] GeV^2/c^4 bin. The branching fraction results are found to agree with the previous LHCb measurement of this decay. As a cross-check, the branching fraction of the resonant B+ -> K+ pi+ pi- J/psi decay is also measured, improving substantially on the precision of the current world average. For the rare B+ -> K+ pi+ pi- mu+ mu- decay, the experimental precision remains limited by the size of the dataset, which motivates a future measurement with the recently completed LHCb Run 3 dataset; preliminary work in this direction is also discussed at the end of this thesis and shows promising results.