In addition to causing trillion-dollar economic losses every year, counterfeiting threatens human health, social equity and national security. Current materials for anti-counterfeiting labelling typically contain toxic inorganic quantum dots and the techniques to produce unclonable patterns require tedious fabrication or complex readout methods. Here we present a nanoprinting-assisted flash synthesis approach that generates fluorescent nanofilms with physical unclonable function micropatterns in milliseconds. This all-in-one approach yields quenching-resistant carbon dots in solid films, directly from simple monosaccharides. Moreover, we establish a nanofilm library comprising 1,920 experiments, offering conditions for various optical properties and microstructures. We produce 100 individual physical unclonable function patterns exhibiting near-ideal bit uniformity (0.492 ± 0.018), high uniqueness (0.498 ± 0.021) and excellent reliability (>93%). These unclonable patterns can be quickly and independently read out by fluorescence and topography scanning, greatly improving their security. An open-source deep-learning model guarantees precise authentication, even if patterns are challenged with different resolutions or devices.

Laser writing is used to structure surfaces in many different ways in materials and life sciences. However, combinatorial patterning applications are still limited. Here we present a method for cost-efficient combinatorial synthesis of very-high-density peptide arrays with natural and synthetic monomers. A laser automatically transfers nanometre-thin solid material spots from different donor slides to an acceptor. Each donor bears a thin polymer film, embedding one type of monomer. Coupling occurs in a separate heating step, where the matrix becomes viscous and building blocks diffuse and couple to the acceptor surface. Furthermore, we can consecutively deposit two material layers of activation reagents and amino acids. Subsequent heat-induced mixing facilitates an in situ activation and coupling of the monomers. This allows us to incorporate building blocks with click chemistry compatibility or a large variety of commercially available non-activated, for example, posttranslationally modified building blocks into the array’s peptides with >17,000 spots per cm².

B-cell epitope sequences from Zika virus (ZIKV) NS1 protein have been identified using epitope prediction tools. Mapping these sequences onto the NS1 surface reveals two major conformational epitopes and a single linear one. Despite an overall average sequence identity of ca. 55% between the NS1 from ZIKV and the four dengue virus (DENV) serotypes, epitope sequences were found to be highly conserved. Nevertheless, nonconserved epitope-flanking residues are responsible for a dramatically divergent electrostatic surface potential on the epitope regions of ZIKV and DENV2 serotypes. These findings suggest that strategies for differential diagnostics on the basis of short linear NS1 sequences are likely to fail due to immunological cross-reactions. Overall, results provide the molecular basis of differential discrimination between Zika and DENVs by NS1 monoclonal antibodies.

High-density peptide arrays are an excellent means to profile anti-plasmodial antibody responses. Different protein intrinsic epitopes can be distinguished, and additional insights are gained, when compared with assays involving the full-length protein. Distinct reactivities to specific epitopes within one protein may explain differences in published results, regarding immunity or susceptibility to malaria. We pursued three approaches to find specific epitopes within important plasmodial proteins, (1) twelve leading vaccine candidates were mapped as overlapping 15-mer peptides, (2) a bioinformatical approach served to predict immunogenic malaria epitopes which were subsequently validated in the assay, and (3) randomly selected peptides from the malaria proteome were screened as a control. Several peptide array replicas were prepared, employing particle-based laser printing, and were used to screen 27 serum samples from a malaria-endemic area in Burkina Faso, West Africa. The immunological status of the individuals was classified as "protected" or "unprotected" based on clinical symptoms, parasite density, and age. The vaccine candidate screening approach resulted in significant hits in all twelve proteins and allowed us (1) to verify many known immunogenic structures, (2) to map B-cell epitopes across the entire sequence of each antigen and (3) to uncover novel immunogenic epitopes. Predicting immunogenic regions in the proteome of the human malaria parasite Plasmodium falciparum, via the bioinformatics approach and subsequent array screening, confirmed known immunogenic sequences, such as in the leading malaria vaccine candidate CSP and discovered immunogenic epitopes derived from hypothetical or unknown proteins.