A new category of hydrogen storage material is reported. The material is functionally similar to metal hydrides but is chemically composed of two metal oxides: iron oxide, which reversibly reacts with hydrogen to produce iron and water, and calcium oxide, which reversibly absorbs the water in situ. Hydrogen is stored simply upon contact with the material, with the reduction of iron oxide and absorption of water taking place in sequence. The direction of the reaction is easily controlled by changing the system temperature, pressure, or both. On the applicational side, the material is tested in a fixed bed for almost 100 cycles over the course of five months with a stable storage capacity of 10 g H2/kg. To reach a high degree of charge, a high pressure (10 bar) and relatively low temperature (350 degrees C) are needed. To investigate these effects in detail, thermogravimetric studies on the performance of the two components are conducted separately, revealing the inhibiting effect of water vapor on the reduction of iron oxide at lower pressures. By adjusting temperature and production rates during discharging, the process allows the generation of high-purity, pressurized H2 (up to 5.4 bar at 500 degrees C), making it directly usable by power-generating devices. On the theoretical side, a detailed thermodynamic analysis is conducted to support the experimental results and explain the optimal reaction conditions. This material behaves similarly to a metal hydride in terms of operational simplicity while offering additional advantages of material cost (7 $/kg H2 capacity) and stability in the presence of oxygen or water vapor. In comparison, magnesium hydride can reach a higher practical storage density of 6 wt %, but its sluggish kinetics often require more than 30 bar for hydrogenation, and it has a significantly higher material cost at 25 $/kg H2 capacity. The simplicity, low cost, and high oxygen resistance of the FeO x /CaO mixture make it highly interesting in the field of hydrogen storage.

DNA is an attractive medium for digital data storage. When data is stored on DNA, errors occur, which makes error-correcting coding techniques critical for reliable DNA data storage. To reduce the number of errors, a common technique is to include constraints that avoid homopolymers (consecutive repeated nucleotides) and balance the GC content, as sequences with homopolymers and unbalanced GC contents are often associated with larger error rates. However, constrained coding comes at the cost of an increase in redundancy. An alternative is to control the errors by randomizing the sequences, embracing the extra errors, and paying for them with additional coding redundancy. In this paper, we determine the error regimes in which embracing errors is more efficient than constrained coding. We find that constrained coding is inefficient in most common error regimes for DNA data storage. Specifically, the error probabilities for homopolymers and unbalanced GC contents must be very large for constrained coding to achieve a higher code rate than unconstrained coding.

Abstract Polyolefins are essential plastics to modern life yet create mounting sustainability challenges. Chemical recycling technologies are key but often require harsh conditions, costly hydrogen, noble metals, or complex catalysts with challenging design. We report hydrogen- and solvent-free depolymerization of polypropylene consumer goods at 240 °C, achieving >80% yield into gasoline hydrocarbons with stable performance. A library of tungstated zirconia catalysts synthesized by flame spray pyrolysis with controlled tungsten speciation enables correlating performance with the density of W–O–Zr ensembles prevalent in sub-nanoclusters, with W:Zr = 1:9 emerging as optimal. These acid sites formed during reaction mediate internal hydrogen transfer and selective backbone C–C scission, as deduced from operando spectroscopy. Life cycle and technoeconomic studies, benchmarked against hydrotreatment-based recycling in a harmonized framework, indicate competitive environmental and economic advantages. Together, this work establishes this route’s promise and underscores the value of precision catalyst synthesis in polyolefin valorization.

Many codecs with different error-correction approaches have been implemented for DNA data storage to date. However, no studies have systematically benchmarked codec implementations to establish their current state-of-the-art. Here, we use in silico and in vitro experiments to compare the performance of six representative codecs from literature. In isolation, these codecs can tolerate error rates up to 14% and a sequence loss of 65%. Under realistic conditions, we further establish that storage densities as high as 117 EB g-1 are feasible using existing codecs and current synthesis and sequencing technologies. Verifying our results experimentally, we demonstrate data storage at 43 EB g-1 using synthesis by material deposition and 13 EB g-1 using electrochemical synthesis, employing existing codecs from literature. Besides closing in on the physical limits of DNA data storage, this study thus demonstrates the maturity of error-correction coding, defines its current state-of-the-art, and establishes best practices for codec benchmarking.

DNA represents a promising solution for high-density and long-term data storage. In this study, we explore the scalability and robustness of DNA-of-Things (DoT) technology by embedding digital data encoded in silica-encapsulated DNA in newspaper ink. In honor of the 75th anniversary of the German Basic Law the "Grundgesetz" was encoded in DNA, encapsulated in silica nanoparticles, and mixed with paraffin-based offset ink for mass distribution in a newspaper with a circulation of over 500 000 copies of "The Süddeutsche Zeitung." We assessed the integrity and recoverability of the DNA after printing by retrieving, sequencing, and decoding the embedded data. Our results demonstrate the sensitivity and scalability of DNA-of-things technology. As a proof of concept, the DNA was reliably stored in printed media and successfully retrieved from a single dot of ink containing approximately 14 femtograms of DNA.

Indium-zirconium oxides rank among the most selective and stable catalysts for CO2 hydrogenation to methanol. Yet, despite extensive research, the mechanistic origin of the exceptional role of monoclinic zirconia remains unresolved and continues to set the benchmark in the field. Here we show that monoclinic hafnia, a wide-bandgap oxide rarely explored in catalysis, can outperform this benchmark. Nanostructured indium-hafnium oxides synthesized via flame spray pyrolysis achieve up to 70% higher indium-specific methanol productivity than indium-zirconium oxides, with the largest gains observed for single atoms of indium. Experimental and theoretical analyses reveal that a combination of stable monoclinic support surfaces, flexible chemical potential of indium single atoms and the presence of a cooperative hydride-proton reservoir collectively enhance CO2 activation and intermediate hydrogenation. Crucially, the precise control of surface hydroxylation is required. These findings establish a new benchmark for green methanol synthesis and provide generalizable design principles for next-generation oxide supports in single-atom catalysis.

Clean, disinfected surfaces and medical instruments are critical to maintaining a hygienic environment, especially in healthcare settings. Current methods for disinfection validation and training require either a long evaluation time or do not distinguish between physical (dilution) and chemical (disintegration) disinfection procedures. However, to achieve effective disinfection, both effects, dilution and disintegration, are required for many commonly used disinfectants (e.g., alcohol, sodium hypochlorite, quaternary ammonium compounds). In this study, a method is established for the real-time monitoring of surface disinfection using fluorescence-labeled DNA and lipid nanoparticles (LNP) encapsulating such DNA. It is shown that the spatial separation of quencher-modified DNA and fluorophore-modified complementary DNA by LNPs can be disrupted by ethanolic disinfectants, facilitating the disintegration of LNPs. The resulting quenching of fluorescence can immediately be detected using a manual setup comprising a hand-held laser, a color filter, and a smartphone camera. To demonstrate a potential application of this novel disinfection detection technology, disinfection of a commonly used medical instrument, a scalpel, is validated using the qualitative change in fluorescence upon disintegration of LNPs, enabling distinction between physical dilution and chemical disintegration. Therefore, LNPs spatially separating quencher and fluorophore offer real-time, qualitative monitoring of surface disinfection.

A wide range of codecs with vastly different error-correction approaches have been proposed and implemented for DNA data storage to date. However, while many codecs claim to provide superior performance, no studies have systematically benchmarked codec implementations to establish the current state-of-the-art in DNA data storage. In this study, we use standardized error scenarios – both in silico and in vitro – to compare the performance of six representative codecs from the literature. We find synthetic benchmarks commonly used in literature to be unsuitable indicators of codec performance, as our data shows that common experimental benchmarks fail to differentiate codecs under standardized conditions. Instead, we implement a comprehensive benchmark covering the major experimental parameters to assess codec performance under realistic DNA data storage conditions, while establishing important baselines for future codec development. Verifying our results with fair and standardized experiments, we demonstrate data storage at 43 EB g -1 using synthesis by material deposition and 13 EB g -1 using the more error prone electrochemical synthesis, employing only existing codecs from the literature. Besides closing in on the physical limits of DNA data storage, this study thus showcases the maturity of error-correction coding and defines its current state-of-the-art.

Multi-template polymerase chain reaction (PCR) is a critical technique enabling the parallel amplification of diverse DNA molecules, thereby facilitating applications in fields from quantitative molecular biology to DNA data storage. However, non-homogeneous amplification due to sequence-specific amplification efficiencies often results in skewed abundance data, compromising accuracy and sensitivity. In this study, we address amplification efficiency in complex amplicon libraries by employing one-dimensional convolutional neural networks (1D-CNNs) to predict sequence-specific amplification efficiencies, based on sequence information alone. Trained on reliably annotated datasets derived from synthetic DNA pools, these models achieve a high predictive performance (AUROC: 0.88, AUPRC: 0.44), thereby enabling the design of inherently homogeneous amplicon libraries. We further introduce CluMo, a deep learning interpretation framework that identifies specific motifs adjacent to adapter priming sites as closely associated with poor amplification. This insight leads to the elucidation of adapter-mediated self-priming as the major mechanism causing low amplification efficiency, challenging long-standing PCR design assumptions. By addressing the basis for non-homogeneous amplification in multi-template PCR, our deep-learning approach reduces the required sequencing depth to recover 99% of amplicon sequences fourfold, and opens new avenues to improve the efficiency of DNA amplification in fields such as genomics, diagnostics, and synthetic biology.

Indium-zirconium (InZrOx) and zinc-zirconium oxides (ZnZrOx) have emerged as highly selective and stable catalysts for CO2 hydrogenation to methanol, a versatile energy carrier. However, the disparity in synthesis methods, catalyst formulations, and structures previously studied precludes quantitative comparisons between the two families. Herein, a rigorous framework is pioneered to benchmark InZrOx and ZnZrOx materials prepared by a standardized flame spray pyrolysis synthesis platform, enabling consistently high surface areas and tunable metal speciation ranging from isolated atoms (<5 mol%) to predominantly nanoparticles (>10 mol%). Isolated indium and zinc species are commonly identified to be optimal for activity and methanol selectivity in their respective families, maximizing CO2 and H-2 activation abilities. InZrOx outperforms ZnZrOx across speciations and is less structure sensitive, as deviations from atomic dispersion is less detrimental on performance for the former. Focusing on representative catalysts featuring saturation of isolated species, the higher activity of 5 mol% InZrOx over its ZnZrOx counterpart is linked to differences in surface oxygen vacancy chemistry, a lower degree of product inhibition, and more facile hydrogenation of the formate intermediate to methoxy. The identification of reactivity descriptors governing both families facilitates the development of unified guidelines in designing reducible oxide catalysts.

Flame-Spray Pyrolysis (FSP) is a versatile synthetic aerosol method to produce inorganic mixed-metal nanoparticles, frequently used for catalysts, battery materials, or chromophores. This work introduces a novel automated robotic platform based on FSP - AutoFSP - to accelerate materials discovery and optimization while providing standardized, machine-readable documentation of all synthesis steps. The manuscript outlines the design considerations for both hardware and software of AutoFSP, as well as the platform's performance in terms of speed, accuracy, and repeatability. AutoFSP has demonstrated significant time savings by reducing operator workload by a factor of two to three, while also improving documentation and decreasing the chance of human experimental error. AutoFSP achieves high compositional accuracy and precision across two orders of magnitude. The relative error of the effective molar metal loading x in Zn x Zr1-x O y and In x Zr1-x O y nanoparticles produced with the setup remains within ± 5%. The platform showcases the potential of automation in chemical discovery and exemplifies how established manual synthetic methods can be adapted for robotic processes before integration into a materials acceleration platform (MAP).

Background: Nosocomial infections pose a serious threat. In neonatal intensive care units (NICUs) especially, there are repeated outbreaks caused by micro-organisms without the sources or dynamics being conclusively determined. Aim: To use amorphous silica nanoparticles with encapsulated DNA (SPED) to simulate outbreak events and to visualize dissemination patterns in a NICU to gain a better understanding of these dynamics. Methods: Three types of SPED were strategically placed on the ward to mimic three different dissemination dynamics among real-life conditions and employee activities. SPED DNA, resistant to disinfectants, was sampled at 22 predefined points across the ward for four days and quantitative polymerase chain reaction analysis was conducted. Findings: Starting from staff areas, a rapid ward-wide SPED dissemination including numerous patient rooms was demonstrated. In contrast, a primary deployment in a patient room only led to the spread in the staff area, with no distribution in the patient area. Conclusion: This study pioneers SPED utilization in simulating outbreak dynamics. By unmasking staff areas as potential key trigger spots for ward-wide dissemination the revealed patterns could contribute to a more comprehensive view of outbreak events leading to rethinking of hygiene measures and training to reduce the rate of nosocomial infections in hospitals. (c) 2024 The Author(s). Published by Elsevier Ltd on behalf of The Healthcare Infection Society. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

Motivated by DNA based data storage system, we investigate errors that occur when synthesizing DNA strands in parallel, where each strand is appended one nucleotide at a time by the machine according to a template supersequence. If there is a cycle such that the machine fails, then the strands meant to be appended at this cycle will not be appended, and we refer to this as a synthesis defect. In this paper, we present two families of codes correcting these synthesis defects, which are t-known-synthesis-defect correcting codes and t-synthesis-defect correcting codes. For the first one, it is assumed that the defective cycles are known, and each of the codeword is a quaternary sequence. We provide constructions for this family of codes for t = 1, 2, with redundancy log 4 and 2 log n + O(1), respectively. For the second one, the codeword is a set of M ordered sequences, and we give a construction for t = 1 to show a strategy for constructing this family of codes. Finally, we derive a lower bound on the redundancy for single-known-synthesis-defect correcting codes, which assures that our construction is almost optimal.

Efficient error-correction codes are crucial for realizing DNA's potential as a long-lasting, high-density storage medium for digital data. At the same time, new workflows promising low-cost, resilient DNA data storage are challenging their design and error-correcting capabilities. This study characterizes the errors and biases in two new additions to the state-of-the-art workflow in DNA data storage: photolithographic synthesis and DNA decay. Photolithographic synthesis offers low-cost, scalable oligonucleotide synthesis but suffers from high error rates, necessitating sophisticated error-correction schemes, for example codes introducing within-sequence redundancy combined with clustering and alignment techniques for retrieval. On the other hand, the decoding of oligo fragments after DNA decay promises unprecedented storage densities, but complicates data recovery by requiring the reassembly of full-length sequences or the use of partial sequences for decoding. Our analysis provides a detailed account of the error patterns and biases present in photolithographic synthesis and DNA decay, and identifies considerable bias stemming from sequencing workflows. We implement our findings into a digital twin of the two workflows, offering a tool for developing error-correction codes and providing benchmarks for the evaluation of codec performance.

Multi-template polymerase chain reaction is a key step in many amplicon sequencing protocols enabling parallel amplification of diverse DNA molecules sharing common adapters in applications, ranging as wide as quantitative molecular biology and DNA data storage. However, this process results in a skewed amplicon abundance, due to sequence-specific amplification biases. In this study, one-dimensional convolutional neural networks (1D-CNNs) were trained on synthetic DNA pools to learn the PCR amplification efficiency of individual templates. These 1D-CNN models can predict poorly amplifying templates based solely on sequence information, achieving an AUROC/AUPRC of up to 0.88/0.44 with very imbalanced prevalence of 2%, thereby greatly outperforming baseline models relying only on GC content and nucleotide frequency as predictors. A new, general-purpose framework for interpreting deep learning models, termed CluMo provides mechanistic insights into the amplification biases. Most strikingly, specific amplification reactions were identified as suffering from adaptor-template self-priming a mechanism previously disregarded in PCR. ### Competing Interest Statement The authors have declared no competing interest.

AbstractMetal promotion could unlock high performance in zinc-zirconium catalysts, ZnZrOx, for CO2 hydrogenation to methanol. Still, with most efforts devoted to costly palladium, the optimal metal choice and necessary atomic-level architecture remain unclear. Herein, we investigate the promotion of ZnZrOx catalysts with small amounts (0.5 mol%) of diverse hydrogenation metals (Re, Co, Au, Ni, Rh, Ag, Ir, Ru, Pt, Pd, and Cu) prepared via a standardized flame spray pyrolysis approach. Cu emerges as the most effective promoter, doubling methanol productivity. Operando X-ray absorption, infrared, and electron paramagnetic resonance spectroscopic analyses and density functional theory simulations reveal that Cu0 species form Zn-rich low-nuclearity CuZn clusters on the ZrO2 surface during reaction, which correlates with the generation of oxygen vacancies in their vicinity. Mechanistic studies demonstrate that this catalytic ensemble promotes the rapid hydrogenation of intermediate formate into methanol while effectively suppressing CO production, showcasing the potential of low-nuclearity metal ensembles in CO2-based methanol synthesis.

Physical unclonable functions (PUFs) based on unique tokens generated by random manufacturing processes have been proposed as an alternative to mathematical one-way algorithms. However, these tokens are not distributable, which is a disadvantage for decentralized applications. Finding unclonable, yet distributable functions would help bridge this gap and expand the applications of object-bound cryptography. Here we show that large random DNA pools with a segmented structure of alternating constant and randomly generated portions are able to calculate distinct outputs from millions of inputs in a specific and reproducible manner, in analogy to physical unclonable functions. Our experimental data with pools comprising up to >1010 unique sequences and encompassing >750 comparisons of resulting outputs demonstrate that the proposed chemical unclonable function (CUF) system is robust, distributable, and scalable. Based on this proof of concept, CUF-based anti-counterfeiting systems, non-fungible objects and decentralized multi-user authentication are conceivable.

Counterfeit products are a problem known across many industries. Chemical products such as pharmaceuticals belong to the most targeted markets, with harmful consequences for consumer health and safety. However, many of the currently used anticounterfeit measures are associated with the packaging, with the readout method and level of security varying between different solutions. Identifiers that can be directly and safely mixed into the product to securely authenticate a batch would be desirable. For this purpose, we propose the use of chemical unclonable functions based on pools of short random DNA oligos, which allow the integration of a cryptographic authentication system into chemical products. We demonstrate and characterize a simplified workflow for readout, showing that results are robust and clearly differentiate between the correct tag and a counterfeit. As a proof of concept, we demonstrate the labeling of an acetaminophen formulation with a chemical unclonable function. The acetaminophen was successfully authenticated from a subsample of the product at a DNA admixing concentration of below 50 ng/g. Stability tests revealed that the readout is stable at room temperature for several years, exceeding the shelf life of most drug products. Our work thus shows that chemical unclonable functions are a valid alternative to state-of-the-art anticounterfeit methods, enabling a secure authentication scheme that is physically linked to the product and safe for consumption. The method is widely applicable beyond pharmaceuticals, allowing for more secure product tracing across industries.

Abstract Internet access has reached 60% of the global population, with the average user spending over 40% of their waking life on the Internet, yet the environmental implications remain poorly understood. Here, we assess the environmental impacts of digital content consumption in relation to the Earth’s carrying capacity, finding that currently the global average consumption of web surfing, social media, video and music streaming, and video conferencing could account for approximately 40% of the per capita carbon budget consistent with limiting global warming to 1.5 °C, as well as around 55% of the per capita carrying capacity for mineral and metal resources use and over 10% for five other impact categories. Decarbonising electricity would substantially mitigate the climate impacts linked to Internet consumption, while the use of mineral and metal resources would remain of concern. A synergistic combination of rapid decarbonisation and additional measures aimed at reducing the use of fresh raw materials in electronic devices (e.g., lifetime extension) is paramount to prevent the growing Internet demand from exacerbating the pressure on the finite Earth’s carrying capacity.