arXiv subjects

Riccardo Rizzo

Publications and source records attributed to Riccardo Rizzo.

8 recordsLinked to original sources

arXiv2026

Overcoming Scattering in High-Cell-Density Tomographic Volumetric Bioprinting Using Computational Light Optimization

Tomographic volumetric additive manufacturing has emerged as a transformative 3D printing technology for rapidly fabricating complex geometries. It offers significant advantages for bioprinting due to its contactless and short process time (a few tens of seconds). However, the presence of high cell densities ($>10^7$ cells mL-1) in bioresins introduces substantial light scattering, which degrades printing resolution and fidelity, hindering the fabrication of biologically relevant microstructures such as vascular channels and cavities. To address this challenge, we utilize a computational patterning framework leveraging physically based inverse rendering to optimize light delivery in scattering environments. This method iteratively refines tomographic projections by simulating light-matter interactions in cell-laden hydrogels, enabling precise compensation for scattering effects. Experimental results demonstrate that our approach achieves 500 ${\mu}m$ diameter vascular channels at $2*10^7$ cells mL-1. Furthermore, we integrate this computational method with refractive index matching strategies, reducing scattering artifacts by minimizing optical mismatch between cells and the hydrogel matrix, enabling printing at $4.1*10^7$ cells mL-1. The compatibility of these dual strategies enables unprecedented print fidelity in turbid bioresins. This advancement expands the scope of tomographic volumetric additive manufacturing for engineering functional tissues with intricate microarchitectures.

physics.optics↗

arXiv2026

Too Big, Too Small, Too $O_2$: The Pandoro Effect from Oxygen Gradients in Tomographic Volumetric Additive Manufacturing

Tomographic Volumetric Additive Manufacturing (TVAM) enables rapid, layerless biofabrication; however, its application to thermoreversible hydrogels is often compromised by complex chemical kinetics. In this study, we identify and characterize a recurrent printing artifact - termed the Pandoro effect - manifesting as a truncated-cone distortion caused by premature polymerization at the vial bottom and inhibition at the top. We demonstrate that this phenomenon originates from a vertical oxygen gradient driven by the thermal hysteresis of resin preparation: heating depletes dissolved oxygen, while subsequent cooling induces diffusion-limited re-oxygenation from the air-resin interface. To mitigate this, we present a multi-tiered strategy. First, we introduce a coupled ray-optical and photochemical optimization model that rigorously accounts for spatially heterogeneous inhibitor concentrations. Unlike conventional threshold-based approaches, this differentiable framework explicitly simulates the spatiotemporal reaction-diffusion dynamics of oxygen depletion, allowing the inverse solver to predictively compensate for local inhibition gradients. Complementing this algorithmic correction, we validate two process-based interventions: the elimination of the air-resin interface and the control of headspace atmosphere. We demonstrate that these strategies effectively suppress the Pandoro effect, and are compatible with cell-laden resins. This work establishes guidelines for reproducible volumetric bioprinting and expands our open-source Dr.TVAM platform with advanced polymerization modeling capabilities.

physics.optics↗

arXiv2026

Single-View Holographic Volumetric 3D Printing with Coupled Differentiable Wave-Optical and Photochemical Optimization

Volumetric additive manufacturing promises near-instantaneous fabrication of 3D objects, yet achieving high fidelity at the micro-scale remains challenging due to the complex interplay between optical diffraction and chemical effects. We present \emph{Single-View Holographic Volumetric Additive Manufacturing} (SHVAM), a mechanically static system that shapes volumetric dose distributions using time-multiplexed, phase-only holograms projected from a single optical axis. To achieve high resolution with SHVAM, we formulate hologram synthesis as a coupled inverse problem, integrating a differentiable wave-optical forward model with a simplified photochemical model that explicitly captures inhibitor diffusion and non-linear dose response. Optimizing hologram sequences under these coupled constraints allows us to pre-compensate for chemical blur, yielding higher print fidelity than optical-only optimization. We demonstrate the efficacy of SHVAM by fabricating simple 2D and 3D structures with lateral feature sizes of approximately \SI{10}{\micro\meter} within a $\SI{0.8}{\milli\meter} \times \SI{0.8}{\milli\meter} \times \SI{3}{\milli\meter}$ volume in seconds.

physics.optics↗

arXiv2025

Overprinting with Tomographic Volumetric Additive Manufacturing

Tomographic Volumetric Additive Manufacturing (TVAM) is a light-based 3D printing technique capable of producing centimeter-scale objects within seconds. A key challenge lies in the calculation of tomographic projection patterns under non-standard conditions, such as the presence of occlusions and materials with diverse optical properties, including varying refractive indices or scattering surfaces. This work demonstrates a broad range of overprinting scenarios, where new structures are directly printed onto or around pre-existing components made from different materials. Our simulations and experimental verifications perform overprinting of absorbing, refracting, reflecting and scattering elements in both round and square vials. All scenarios are optimized with our differentiable, physically based ray-optics approach using the open-source Dr.TVAM framework, delivering high-quality projections for both laser- and LED-based illuminations within minutes and lower-quality projections within seconds, exceeding existing open-source solutions in speed, flexibility, and quality.

physics.optics↗

arXiv2024

The GECo algorithm for Graph Neural Networks Explanation

Graph Neural Networks (GNNs) are powerful models that can manage complex data sources and their interconnection links. One of GNNs' main drawbacks is their lack of interpretability, which limits their application in sensitive fields. In this paper, we introduce a new methodology involving graph communities to address the interpretability of graph classification problems. The proposed method, called GECo, exploits the idea that if a community is a subset of graph nodes densely connected, this property should play a role in graph classification. This is reasonable, especially if we consider the message-passing mechanism, which is the basic mechanism of GNNs. GECo analyzes the contribution to the classification result of the communities in the graph, building a mask that highlights graph-relevant structures. GECo is tested for Graph Convolutional Networks on six artificial and four real-world graph datasets and is compared to the main explainability methods such as PGMExplainer, PGExplainer, GNNExplainer, and SubgraphX using four different metrics. The obtained results outperform the other methods for artificial graph datasets and most real-world datasets.

cs.LG↗

arXiv2024

A pH Sensor Scaffold for Mapping Spatiotemporal Gradients in Three Dimensional In Vitro Tumour Models

The detection of extracellular pH at single cell resolution is challenging and requires advanced sensibility. Sensing pH at a high spatial and temporal resolution might provide crucial information in understanding the role of pH and its fluctuations in a wide range of physio-pathological cellular processes, including cancer. Here, a method to embed silica-based fluorescent pH sensors into alginate-based three-dimensional (3D) microgels tumour models, coupled with a computational method for fine data analysis, is presented. By means of confocal laser scanning microscopy, live-cell time-lapse imaging of 3D alginate microgels was performed and the extracellular pH metabolic variations were monitored in both in vitro 3D mono- and 3D co-cultures of tumour and stromal pancreatic cells. The results show that the extracellular pH is cell line-specific and time-dependent. Moreover, differences in pH were also detected between 3D monocultures versus 3D co-cultures, thus suggesting the existence of a metabolic crosstalk between tumour and stromal cells. In conclusion, the system has the potential to image multiple live cells types in a 3D environment and to decipher in real-time their pH metabolic interplay under controlled experimental conditions, thus being also a suitable platform for drug screening and personalized medicine.

physics.bio-ph↗

arXiv2021

Highly sensitive fluorescent pH microsensor based on the ratiometric dye pyranine immobilized on silica microparticles

Pyranine (HPTS) is a remarkably interesting pH sensitive dye that has been used for plenty of applications. Its high quantum yield and extremely sensitive ratiometric fluorescence against pH change makes it a very favorable for pH sensing applications and development of pH nano/microsensors. However, its strong negative charge and lack of easily modifiable functional groups makes it difficult to be used with charged substrates such as silica. This study reports a noncovalent HPTS immobilization methodology on silica microparticles that considers the retention of pH sensitivity as well as long term stability of the pH microsensors. The study emphasizes on importance of surface charge for governing the sensitivity of the immobilized HPTS dye molecules on silica microparticles. Importance of methodology of immobilization that preserves the sensitivity as well as stability of the microsensors is also assessed.

physics.chem-ph↗

arXiv2016

Artwork creation by a cognitive architecture integrating computational creativity and dual process approaches

The paper proposes a novel cognitive architecture (CA) for computational creativity based on the Psi model and on the mechanisms inspired by dual process theories of reasoning and rationality. In recent years, many cognitive models have focused on dual process theories to better describe and implement complex cognitive skills in artificial agents, but creativity has been approached only at a descriptive level. In previous works we have described various modules of the cognitive architecture that allows a robot to execute creative paintings. By means of dual process theories we refine some relevant mechanisms to obtain artworks, and in particular we explain details about the resolution level of the CA dealing with different strategies of access to the Long Term Memory (LTM) and managing the interaction between S1 and S2 processes of the dual process theory. The creative process involves both divergent and convergent processes in either implicit or explicit manner. This leads to four activities (exploratory, reflective, tacit, and analytic) that, triggered by urges and motivations, generate creative acts. These creative acts exploit both the LTM and the WM in order to make novel substitutions to a perceived image by properly mixing parts of pictures coming from different domains. The paper highlights the role of the interaction between S1 and S2 processes, modulated by the resolution level, which focuses the attention of the creative agent by broadening or narrowing the exploration of novel solutions, or even drawing the solution from a set of already made associations. An example of artificial painter is described in some experimentations by using a robotic platform.

cs.AI↗