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Although mid-infrared (mid-IR) spectroscopy is a mainstay of molecular fingerprinting, its sensitivity is diminished somewhat when looking at small volumes of sample. Nanophotonics provides a platform to enhance the detection capability. Tittl et al. built a mid-IR nanophotonic sensor based on reflection from an all-dielectric metasurface array of specially designed scattering elements. The scattering elements could be tuned via geometry across a broad range of wavelengths in the mid-IR. The approach successfully detected and differentiated the absorption fingerprints of various molecules. The technique offers the prospect of on-chip molecular fingerprinting without the need for spectrometry, frequency scanning, or moving mechanical parts. Science , this issue p. 1105 A pixelated dielectric metasurface is used for the mid-infrared detection of molecular fingerprints. Metasurfaces provide opportunities for wavefront control, flat optics, and subwavelength light focusing. We developed an imaging-based nanophotonic method for detecting mid-infrared molecular fingerprints and implemented it for the chemical identification and compositional analysis of surface-bound analytes. Our technique features a two-dimensional pixelated dielectric metasurface with a range of ultrasharp resonances, each tuned to a discrete frequency; this enables molecular absorption signatures to be read out at multiple spectral points, and the resulting information is then translated into a barcode-like spatial absorption map for imaging. The signatures of biological, polymer, and pesticide molecules can be detected with high sensitivity, covering applications such as biosensing and environmental monitoring. Our chemically specific technique can resolve absorption fingerprints without the need for spectrometry, frequency scanning, or moving mechanical parts, thereby paving the way toward sensitive and versatile miniaturized mid-infrared spectroscopy devices.

Polymers are a vital part of everyday life. Their chemical universe is so large that it presents unprecedented opportunities as well as significant challenges to identify suitable application-specific candidates. We present a complete end-to-end machine-driven polymer informatics pipeline that can search this space for suitable candidates at unprecedented speed and accuracy. This pipeline includes a polymer chemical fingerprinting capability called polyBERT (inspired by Natural Language Processing concepts), and a multitask learning approach that maps the polyBERT fingerprints to a host of properties. polyBERT is a chemical linguist that treats the chemical structure of polymers as a chemical language. The present approach outstrips the best presently available concepts for polymer property prediction based on handcrafted fingerprint schemes in speed by two orders of magnitude while preserving accuracy, thus making it a strong candidate for deployment in scalable architectures including cloud infrastructures. The polymer universe is gigantic. Searching this space effectively requires ultrafast high-fidelity property prediction methods. Here, the authors present a chemical language model that can probe this space at unprecedented speed and accuracy.

The proper functioning of living systems and physiological phenotypes depends on molecular composition. Yet simultaneous quantitative detection of a wide variety of molecules remains a challenge 1 – 8 . Here we show how broadband optical coherence opens up opportunities for fingerprinting complex molecular ensembles in their natural environment. Vibrationally excited molecules emit a coherent electric field following few-cycle infrared laser excitation 9 – 12 , and this field is specific to the sample’s molecular composition. Employing electro-optic sampling 10 , 12 – 15 , we directly measure this global molecular fingerprint down to field strengths 10 7 times weaker than that of the excitation. This enables transillumination of intact living systems with thicknesses of the order of 0.1 millimetres, permitting broadband infrared spectroscopic probing of human cells and plant leaves. In a proof-of-concept analysis of human blood serum, temporal isolation of the infrared electric-field fingerprint from its excitation along with its sampling with attosecond timing precision results in detection sensitivity of submicrograms per millilitre of blood serum and a detectable dynamic range of molecular concentration exceeding 10 5 . This technique promises improved molecular sensitivity and molecular coverage for probing complex, real-world biological and medical settings. A vibrational spectroscopy technique that measures the electric field emitted from organic molecules following infrared illumination allows their molecular fingerprints to be separated from the excitation background, even in complex biological samples.

Frequency microcombs, alternative to mode-locked laser and fiber combs, enable miniature rulers of light for applications including precision metrology, molecular fingerprinting and exoplanet discoveries. To enable frequency ruling functions, microcombs must be stabilized by locking their carrier-envelope offset frequency. So far, the microcomb stabilization remains compounded by the elaborate optics external to the chip, thus evading its scaling benefit. To address this challenge, here we demonstrate a nanophotonic chip solution based on aluminum nitride thin films, which simultaneously offer optical Kerr nonlinearity for generating octave soliton combs and quadratic nonlinearity for enabling heterodyne detection of the offset frequency. The agile dispersion control of crystalline aluminum nitride photonics permits high-fidelity generation of solitons with features including 1.5-octave spectral span, dual dispersive waves, and sub-terahertz repetition rates down to 220 gigahertz. These attractive characteristics, aided by on-chip phase-matched aluminum nitride waveguides, allow the full determination of the offset frequency. Our proof-of-principle demonstration represents an important milestone towards fully integrated self-locked microcombs for portable optical atomic clocks and frequency synthesizers. Though octave soliton microcombs are attractive for on-chip metrology and optical clocks, limitations in existing materials lead to increased chip integration complexity. Here, the authors report access to octave soliton microcombs and self-referencing using aluminium nitride nanophotonic chips.

Machine learning and high-throughput computational screening have been valuable tools in accelerated first-principles screening for the discovery of the next generation of functionalized molecules and materials. The application of machine learning for chemical applications requires the conversion of molecular structures to a machine-readable format known as a molecular representation. The choice of such representations impacts the performance and outcomes of chemical machine learning methods. Herein, we present a new concise molecular representation derived from persistent homology, an applied branch of mathematics. We have demonstrated its applicability in a high-throughput computational screening of a large molecular database (GDB-9) with more than 133,000 organic molecules. Our target is to identify novel molecules that selectively interact with CO 2 . The methodology and performance of the novel molecular fingerprinting method is presented and the new chemically-driven persistence image representation is used to screen the GDB-9 database to suggest molecules and/or functional groups with enhanced properties. The choice of molecular representations can severely impact the performances of machine-learning methods. Here the authors demonstrate a persistence homology based molecular representation through an active-learning approach for predicting CO 2 /N 2 interaction energies at the density functional theory (DFT) level.

Infrared spectroscopy, especially for molecular vibrations in the fingerprint region between 600 and 1,500 cm −1 , is a powerful characterization method for bulk materials. However, molecular fingerprinting at the nanoscale level still remains a significant challenge, due to weak light–matter interaction between micron-wavelengthed infrared light and nano-sized molecules. Here we demonstrate molecular fingerprinting at the nanoscale level using our specially designed graphene plasmonic structure on CaF 2 nanofilm. This structure not only avoids the plasmon–phonon hybridization, but also provides in situ electrically-tunable graphene plasmon covering the entire molecular fingerprint region, which was previously unattainable. In addition, undisturbed and highly confined graphene plasmon offers simultaneous detection of in-plane and out-of-plane vibrational modes with ultrahigh detection sensitivity down to the sub-monolayer level, significantly pushing the current detection limit of far-field mid-infrared spectroscopies. Our results provide a platform, fulfilling the long-awaited expectation of high sensitivity and selectivity far-field fingerprint detection of nano-scale molecules for numerous applications. Despite being a powerful tool for molecular vibrational mode detection, infrared spectrosocpy is limited by weak sensitivity. Here, the authors demonstrate a platform for enhanced molecular fingerprint sensing based on a graphene/CaF 2 nanofilm plasmonic structure.

Spectral matching of MS 2 fragmentation spectra has become a popular method for characterizing natural products libraries but identification remains challenging due to differences in MS 2 fragmentation properties between instruments and the low coverage of current spectral reference libraries. To address this bottleneck we present Structural similarity Network Annotation Platform for Mass Spectrometry (SNAP-MS) which matches chemical similarity grouping in the Natural Products Atlas to grouping of mass spectrometry features from molecular networking. This approach assigns compound families to molecular networking subnetworks without the need for experimental or calculated reference spectra. We demonstrate SNAP-MS can accurately annotate subnetworks built from both reference spectra and an in-house microbial extract library, and correctly predict compound families from published molecular networks acquired on a range of MS instrumentation. Compound family annotations for the microbial extract library are validated by co-injection of standards or isolation and spectroscopic analysis. SNAP-MS is freely available at www.npatlas.org/discover/snapms . Comparing experimental mass spectra to reference spectra can enable natural product identification, but these spectral libraries are often incomplete and not universally applicable. Here, the authors present SNAP-MS, a tool that allows assigning compound families without experimental or calculated reference spectra.

The last few decades have witnessed unprecedented convergence between humans and machines that closely operate around the human body. Despite these advances, traditional machines made of hard, dry and abiotic materials are substantially dissimilar to soft, wet and living biological tissues. This dissimilarity results in severe limitations for long-term, reliable and highly efficient interfacing between humans and machines. To bridge this gap, hydrogels have emerged as an ideal material candidate for interfacing between humans and machines owing to their mechanical and chemical similarities to biological tissues and the versatility and flexibility in designing their properties. In this Review, we provide a comprehensive summary of functional modes, design principles, and current and future applications for hydrogel interfaces towards merging humans and machines. Hydrogels are one of the most promising materials to bridge the stark disparities between traditional machines and biological tissues for successful interfacing between humans and machines. This Review discusses the functional modes, design principles, and current and future applications of hydrogel interfaces for the merging of humans and machines.

Carbonate mud represents one of the most important geochemical archives for reconstructing ancient climatic, environmental, and evolutionary change from the rock record. Mud also represents a major sink in the global carbon cycle. Yet, there remains no consensus about how and where carbonate mud is formed. Here, we present stable isotope and trace-element data from carbonate constituents in the Bahamas, including ooids, corals, foraminifera, and algae. We use geochemical fingerprinting to demonstrate that carbonate mud cannot be sourced from the abrasion and mixture of any combination of these macroscopic grains. Instead, an inverse Bayesian mixing model requires the presence of an additional aragonite source.We posit that this source represents a direct seawater precipitate. We use geological and geochemical data to show that “whitings” are unlikely to be the dominant source of this precipitate and, instead, present a model for mud precipitation on the bank margins that can explain the geographical distribution, clumped-isotope thermometry, and stable isotope signature of carbonate mud. Next, we address the enigma of why mud and ooids are so abundant in the Bahamas, yet so rare in the rest of the world: Mediterranean outflow feeds the Bahamas with the most alkaline waters in themodern ocean (>99.7th-percentile). Such high alkalinity appears to be a prerequisite for the nonskeletal carbonate factory because, when Mediterranean outflow was reduced in the Miocene, Bahamian carbonate export ceased for 3-million-years. Finally, we show how shutting off and turning on the shallow carbonate factory can send ripples through the global climate system.

Health state transitions are reflected in characteristic changes in the molecular composition of biofluids. Detecting these changes in parallel, across a broad spectrum of molecular species, could contribute to the detection of abnormal physiologies. Fingerprinting of biofluids by infrared vibrational spectroscopy offers that capacity. Whether its potential for health monitoring can indeed be exploited critically depends on how stable infrared molecular fingerprints (IMFs) of individuals prove to be over time. Here we report a proof-of-concept study that addresses this question. Using Fourier-transform infrared spectroscopy, we have fingerprinted blood serum and plasma samples from 31 healthy, non-symptomatic individuals, who were sampled up to 13 times over a period of 7 weeks and again after 6 months. The measurements were performed directly on liquid serum and plasma samples, yielding a time- and cost-effective workflow and a high degree of reproducibility. The resulting IMFs were found to be highly stable over clinically relevant time scales. Single measurements yielded a multiplicity of person-specific spectral markers, allowing individual molecular phenotypes to be detected and followed over time. This previously unknown temporal stability of individual biochemical fingerprints forms the basis for future applications of blood-based infrared spectral fingerprinting as a multiomics-based mode of health monitoring. Health status transitions are reflected as characteristic changes in molecular composition of biofluids. Here, the authors apply infrared molecular fingerprinting and reveal that blood-based phenotypes are sufficiently stable over time, providing the basis for time- and cost-effective health monitoring.