arXiv subjects

Guillermo C. Bazan

Publications and source records attributed to Guillermo C. Bazan.

5 recordsLinked to original sources

arXiv2026

AURA: Active-Response Attribution under Treatment Ambiguity in Bacterial Cytological Profiling

When a bacterial sample is exposed to several antibiotics, not every applied drug necessarily acts: if the organism is resistant to one of them, that drug leaves no morphological trace. The clinically meaningful quantity is therefore not which antibiotics were applied, but which ones were active. We show that these two are sharply decoupled in real E. coli microscopy - naively assuming the applied combination equals the active one is correct only about 37% of the time - yet existing computational tools are ill-suited to recovering the active set. Forward perturbation models such as scGen, CPA, and IMPA are designed to predict appearance from treatment, not the reverse, and inverting them degrades sharply; discriminative image classifiers tend to memorise strain- and batch-specific texture and fail to transfer across experimental replicates. We introduce AURA, which reframes the task as constrained, energy-based inverse attribution. Its central inductive bias is that the active set must be a subset of the applied set; this collapses the candidate space and lets AURA infer the active subset of applied antibiotics by decomposing residual morphology into antibiotic response atoms and selecting the subset with the lowest reconstruction energy, using no strain label at test time. AURA-E adds evidence-aware abstention, withholding a prediction when candidate explanations remain near-equally plausible. On cross-replicate transfer in an E. coli cytological profiling dataset, AURA recovers the active antibiotic combination with 95.47% exact-match accuracy.

cs.CV↗

arXiv2024

Forming Long-range Order of Semiconducting Polymers through Liquid-phase Directional Molecular Assemblies

Intermolecular interactions are crucial in determining the morphology of solution-processed semiconducting polymer thin films. However, these random interactions often lead to disordered or short-range ordered structures. Achieving long-range order in these films has been a challenge due to limited control over microscopic interactions in current techniques. Here, we present a molecular-level methodology that leverages spatial matching of intermolecular dynamics among solutes, solvents, and substrates to induce directional molecular assembly in weakly bonded polymers. Within the optimized dynamic scale of 2.5 Å between polymer side chains and self-assembled monolayers (SAMs) on nanogrooved substrates, our approach transforms random aggregates into unidirectional fibers with a remarkable increase in the anisotropic stacking ratio from 1 to 11. The Flory-Huggins-based molecular stacking model accurately predicts the transitioning order on various SAMs, validated by morphologic and spectroscopic observations. The enhanced structural ordering spans over 3 orders of magnitude in length, raising from the smallest 7.3 nm random crystallites to >14 um unidirectional fibers on sub-millimeter areas. Overall, this study provides insights into the control of complex intermolecular interactions and offers enhanced molecular-level controllability in solution-based processes.

cond-mat.soft↗

arXiv2021

Predicting antimicrobial activity of conjugated oligoelectrolyte molecules via machine learning

New antibiotics are needed to battle growing antibiotic resistance, but the development process from hit, to lead, and ultimately to a useful drug, takes decades. Although progress in molecular property prediction using machine-learning methods has opened up new pathways for aiding the antibiotics development process, many existing solutions rely on large datasets and finding structural similarities to existing antibiotics. Challenges remain in modelling of unconventional antibiotics classes that are drawing increasing research attention. In response, we developed an antimicrobial activity prediction model for conjugated oligoelectrolyte molecules, a new class of antibiotics that lacks extensive prior structure-activity relationship studies. Our approach enables us to predict minimum inhibitory concentration for E. coli K12, with 21 molecular descriptors selected by recursive elimination from a set of 5,305 descriptors. This predictive model achieves an R2 of 0.65 with no prior knowledge of the underlying mechanism. We find the molecular representation optimum for the domain is the key to good predictions of antimicrobial activity. In the case of conjugated oligoelectrolytes, a representation reflecting the 3-dimensional shape of the molecules is most critical. Although it is demonstrated with a specific example of conjugated oligoelectrolytes, our proposed approach for creating the predictive model can be readily adapted to other novel antibiotic candidate domains.

physics.app-ph↗

arXiv2021

The Impact of Bacteria Exposure on the Plasmonic Response of Silver Nanostructured Surfaces

Silver, especially in the form of nanostructures, is widely employed as an antimicrobial agent in a large range of commercial products. The origin of the biocidal mechanism has been elucidated in the last decades, and most likely originates from silver cation release due to oxidative dissolution followed by cellular uptake of silver ions, a process that causes a severe disruption of bacterial metabolism and eventually leads to eradication. Despite the large number of works dealing with the effects of nanosilver shape/size on the antibacterial mechanism and on the (bio)physical chemistry pathways that drive bacterial eradication, little effort has been devoted to the investigation of the silver NPs plasmon response upon interaction with bacteria. Here we present a detailed investigation of the bacteria-induced changes of the plasmon spectral and dynamical features after exposure to one of the most studied bacterial models, Escherichia Coli. Ultrafast pump-probe measurements indicate that the dramatic changes on particle size/shape and crystallinity, which stem from a bacteria-induced oxidative dissolution process, translate into a clear modification of the plasmon spectral and dynamical features. This study may open innovative new avenues in the field of biophysics of bio-responsive materials, with the aim of providing new and reliable biophysical signatures of the interaction of these materials with complex biological environments.

cond-mat.mes-hall↗

arXiv2020

The role of charge recombination to spin-triplet excitons in non-fullerene acceptor organic solar cells

The power conversion efficiencies (PCEs) of organic solar cells (OSCs) using non-fullerene acceptors (NFAs) have now reached 18%. However, this is still lower than inorganic solar cells, for which PCEs >20% are commonplace. A key reason is that OSCs still show low open-circuit voltages (Voc) relative to their optical band gaps, attributed to non-radiative recombination. For OSCs to compete with inorganics in efficiency, all non-radiative loss pathways must be identified and where possible, removed. Here, we show that in most NFA OSCs, the majority of charge recombination at open-circuit proceeds via formation of non-emissive NFA triplet excitons (T1); in the benchmark PM6:Y6 blend, this fraction reaches 90%, contributing 60 mV to the reduction of Voc. We develop a new design to prevent recombination via this non-radiative channel through the engineering of significant hybridisation between the NFA T1 and the spin-triplet charge transfer exciton (3CTE). We model that the rate of the back charge transfer from 3CTE to T1 can be reduced by an order of magnitude, allowing re-dissociation of the 3CTE. We then demonstrate NFA systems where T1 formation is suppressed. This work therefore provides a clear design pathway for improved OSC performance to 20% PCE and beyond.

physics.app-ph↗