Electrospun nanofiber membranes (ENMs) exhibit high porosity, high specific surface area, and a distinctive interconnected structure. In this study, composite membranes were prepared by electrospinning. After hot pressing hybrid ENMs consisting of poly(vinylidene difluoride) (PVDF) as a thermoplastic polymer and polyacrylonitrile (PAN) as a skeleton polymer, the PVDF nanofibers can be partially melted, softened, or fractured, causing nanofibers to conglomerate or fuse. The connections between membrane properties and filtration performances have been thoroughly studied. To adjust membrane porosity, the hot pressing method was used at different temperatures (60 ^∘ and 140 ^∘ ) for the same pressure and hold time. Water permeability tests showed that these membranes had adequate transport properties for filtration applications, with the membranes that were processed at 60 ^∘ exhibiting high values of pure water flux [ > 420L/(m^2 h)] . Membrane surfaces were then functionalized with antimicrobial properties via soaking in solutions of silver nitrate, which were confirmed through techniques of disk diffusion and surface scanning. Results indicated that the nanofiber membranes that were decorated with silver nanoparticles (AgNPs) suppressed the growth of bacterial colonies. This study presented a cost-effective and convenient approach for creating antimicrobial nanofiber membranes that are especially beneficial for the filtration of water. With their exceptional permeability and disinfection effectiveness, these membranes provide an innovative solution ensuring a cleaner and safer water access for future generations.
Haloacetonitriles (HANs) are highly toxic disinfection byproduct-detected in drinking water. In this study, we applied machine learning (ML) to investigate the formation of dichloroacetonitrile (DCAN), the most common HAN, using a large literature-derived dataset. Among four models evaluated, CatBoost demonstrated the best predictive performance. SHapley Additive exPlanation (SHAP) analysis revealed that DCAN formation is not solely governed by individual parameters but is substantially influenced by feature interactions. For instance, while dissolved organic carbon (DOC) is generally positively correlated with DCAN formation, this relationship trends to weaken at higher specific ultraviolet absorbance at 254 nm (SUVA254) values, underscoring the role of non-aromatic fractions in DCAN formation. The interaction between DOC and SUVA254 is further influenced by the disinfectant, with chloramination generally resulting in lower formation than chlorination. To assess model generalizability, we developed a Reliability Index (RI) framework, which integrates a distributional similarity score (Mahalanobis distance) and an anomaly detection score (One-Class Support Vector Machine) to quantify how representative new data are relative to the training set. The model showed strong performance on an external dataset when RI values exceeded 0.25. This study demonstrates the potential of ML in uncovering complex mechanisms driving DCAN formation and introduces RI as a transferable tool for evaluating the generalizability of predictive models.
In this paper, we present a first quantitative test of detected light signals produced in a pulsed neutron source run in a small vertical drift LArTPC at the CERN Neutrino Platform ColdBox test facility. The ColdBox cryostat, detectors, neutron sources, and particle interactions are modeled and simulated using Fluka. We demonstrate the ability to identify the contribution from neutron interactions using X-ARAPUCA photodetectors, and show first comparisons of data to simulation, which indicate reasonable agreement. A time constant is also fitted from the neutron-beam-off light signal spectrum and found consistent between data and simulation. Several important systematic effects are discussed and serve as guides for future runs at larger LArTPCs.
DAMSA (DArk Messenger Searches at an Accelerator) is a novel short-baseline accelerator experiment aimed at probing short-lived physics processes, including searches for evidence of a dark sector of particle physics and well-motivated Standard Model signals. Motivated by open questions in neutrino physics and the absence of conclusive evidence for conventional weakly interacting massive particles, DAMSA targets MeV-to-sub-GeV dark-sector messengers with feeble couplings that can be produced in abundance at the PIP-II LINAC. By employing an ultra-short baseline of order one meter, DAMSA is uniquely positioned to overcome the beam-dump "ceiling" that limits sensitivity to promptly decaying particles in longer-baseline experiments. The conceptual design emphasizes a beam-dump production scheme combined with a compact detector optimized for rare decays while mitigating intense neutron-induced backgrounds inherent to high-power proton beams. To validate the experimental strategy and detector technologies, the Little DAMSA Path-Finder (LDPF) proof-of-concept experiment is proposed, focusing on axion-like particles decaying to two photons and operating with 300 MeV electron beams at FAST. Successful realization of LDPF will establish the feasibility of the DAMSA approach, enabling a broad and powerful program to explore short-lived new physics and precision Standard Model processes in a previously inaccessible regime. This conceptual design document outlines the technical details of DAMSA's physics goals, the beam facility proposals, key experimental challenges and how to overcome them, and the proposed experimental staging campaigns.
A cornerstone of advanced materials design is establishing a framework for assembling nanoparticle superstructures with tailored symmetries. A longstanding challenge has been assembling diamond-like superstructures for photonic devices. Traditionally, such open superstructures require functionalized nanoparticles with directional or anisotropic interactions, reminiscent of valence bonding in a diamond. Here, we present a robust strategy for assembling valence-free nanoparticles into a broad array of cubic superstructures. By grafting nanoparticles with oppositely charged, end-functionalized water-soluble polymers of adjustable molecular weight, we gain control over electrostatic interactions and conformational constraints. This unified approach yields lattices analogous to rock salt, CsCl, zinc-blende, diamond, and the rare simple cubic phase, with tunable lattice constants. Theoretical models and simulations elucidate the underlying interactions, providing a framework for engineering valence-free nanoparticle superlattices.
