Open Access

This study presents a novel approach for the development of antifouling and antibacterial hydrogel coatings for short‐term titanium implants to treat bone defects. Such implants provide temporary stabilization during bone healing and are intended to be explanted within a period of 12 months. The novel surface modification prevents complications during implant removal, like injury to tissue, nerves, or tendons due to adhesion to the untreated titanium surface. The coatings combine acrylamide‐based hydrogels with photocrosslinkers possessing intrinsic antibacterial properties and anchor groups designed for titanium substrates. Comprehensive in vitro evaluations are conducted to assess the biocompatibility and efficacy of these coatings. The results demonstrate that the water‐swellable polymer networks effectively prevent cell and tissue adhesion by their antifouling characteristics without inducing cytotoxicity. Importantly, these coatings also exhibit an intrinsic and non‐leaching antibacterial effect covalently incorporated into the molecular framework, which addresses the limitations of current implant coating technologies that often rely on the incorporation of antibiotics or bactericidal agents. As the experimental data conclusively verify the effectiveness of the coatings in inhibiting cell adhesion and bacterial colonization, this technology shows great potential to significantly advance the field of short‐term titanium implants.

Manual processing of nanosecond-resolved spICP-MS data is tiring and error prone. Automation can help to prevent errors from manual processing and to reduce data processing time from hours to seconds. This study presents our software prototype for processing nanosecond-resolved spICP-MS data. The software features a graphical user interface and multiple ways of processing the provided data in seconds. As general file type the open data standard HDF5 is used, while the system also supports import of binary data or comma separated values. Provided data can be analyzed based on the temporal distance between detector events. A proportionality between the inverse logarithm of the distance between detector events and the particle size[1], as well as a correlation between the number of particle events and the particle number concentration[1] were shown previously. Our software directly calculates the necessary inverse logarithm and the number of particle events from raw data. This data processing approach enables detection of gold nanoparticles (AuNP) as small as 7.5 nm[1] with a commercial single quadrupole ICP-MS instrument equipped with high temporal resolution data acquisition hardware. Furthermore, the software also features particle by particle analysis and the ability to visualize temporally highly resolved particle signals.

This study presents our data acquisition system prototype for single particle inductively coupled plasma mass spectrometry (spICP-MS) with nanosecond time resolution (nanoDAQ) and a matching data processing approach for nanosecond time-resolved data. The system continuously samples the secondary electron multiplier (SEM) detector signal with a dwell time of approximately 2 ns and enables detection of gold nanoparticles (AuNP) as small as 7.5 nm[1] with a commercial single quadrupole ICP-MS instrument. Analysis of acquired transient data is based on the temporal distance between detector events and event density. It was shown that the inverse logarithm of the distance between detector events is proportional to particle size. Also, the number of detector events per particle can be used to calibrate and determine the particle number concentration (PNC) of a nanoparticle dispersion.[1] High data acquisition frequency of the systems allows recording of a statistically significant number of data points in 60 s or less, which leaves only the sample uptake and rinsing steps as remaining factors for limiting the total measurement time.

Microplastics (MPs) refer to plastic particles, fibers, or beads with sizes ranging from 100 nm to 5 mm in size. Their widespread presence in the environment has become a pressing global concern. Primary MPs are intentionally produced particles for industrial and commercial applications, such as exfoliants in personal care and cosmetic formulations. In contrast, secondary MPs are generated through the fragmentation and degradation of larger plastic materials, due to environmental weathering processes.[1] Due to their high surface area-to-volume ratio and hydrophobic nature, MPs have the potential to serve as vectors for the accumulation and transport of diverse organic contaminants, including polycyclic aromatic hydrocarbons (PAHs), perfluoroalkyl substances (PFAS), pharmaceuticals and personal care products (PPCPs), as well as trace metals such as silver, cadmium, chromium, and copper. In the environment, MPs undergo aging processes driven by factors such as UV radiation, temperature, oxygen exposure, and interactions with environmental chemicals. These aging processes can significantly alter the physicochemical surface properties of MPs, thereby influencing their adsorption behavior.[2] This study aims to develop a novel approach for characterizing the surface chemistry of microplastics in the micrometer size range using time-of-flight secondary ion mass spectrometry (ToF-SIMS). The main objective of this study was to address how aging and particle size influence the capacity of MPs to adsorb environmental contaminants. Microplastics were prepared in-house and artificially aged under controlled UV-C exposure for 12, 24, 36, 48, 7, and hours. Subsequently, the aged MPs were exposed in plastic-free containers to model solutions to simulate environmental co-contaminants. The adsorption behavior of these onto the aged secondary microplastics, varying in size and aging stage, was analyzed.

Traditionally, single particle inductively coupled plasma quadrupole mass spectrometry (spICP-Q-MS) has been used primarily for inorganic analysis. Recently, however, an increasing number of studies have explored the detection of carbon signals. For example, Bolea-Fernandez et al. successfully detected microplastics directly via the 13C+ signal.[1] Since then, interest in this field has been growing steadily and numerous intriguing developments have been made. Here, time-of-flight mass analyzers are attractive to detect multiple particle constituents such as chlorine and carbon within a microplastic particle, as shown by the Clases group.[2] One important factor facilitating these recent advancements is the ongoing improvement of the instruments and their software. In particular, the improvement in time resolution is of considerable significance to enhance particle detection. Our group has developed a custom-built data acquisition unit for spICP Q MS that operates with nanosecond-time-resolution.[3,4] While this system has already been applied successfully for the detection of metallic nanoparticles, it was recently used to monitor carbon signals originating from microplastic particles.[5] Beyond demonstrating the general feasibility, the aim was to investigate if and to what extend established characteristics of spICP-MS, namely measurement parameters and performance indicators, can be adapted to the nanosecond spICP-MS of microparticles. For example, Bettmer et al. showed that optimal settings for microparticle detection may differ from those typically applied for inorganic nanoparticle detection.[6] In this work, dispersions of polystyrene particles with sizes in the low micrometer range were probed with high time-resolution spICP-MS to test the performance of multiple sample introduction systems and the influence of, e.g., the nebulizer gas flow rate, sample flow rate, plasma power, and sampling depth. The different sample introduction systems investigated included pneumatic nebulizers and a microdroplet generator. The suitability of these systems was assessed based on particle signal, particle detection efficiency, average background signal, and oxide ratio. The experiments show that optimal parameters may vary vastly depending on the sample introduction system in use and careful optimization is necessary to enhance particle detection.