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At least 343 records · Page 19

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NASA NTRS2019

Chemical Ecosystem Selection on Mineral Surfaces Reveals Long-Term Dynamics Consistent with the Spontaneous Emergence of Mutual Catalysis

How did chemicals first become organized into systems capable of self-propagation and adaptive evolution? One possibility is that the first evolvers were chemical ecosystems localized on mineral surfaces and composed of sets of molecular species that could catalyze each other’s formation. We used a bottom-up experimental framework; chemical ecosystem selection (CES) to evaluate this perspective and search for surface-associated; and mutually catalytic chemical systems based on the changes in chemistry they are expected to induce. Here, we report the results of preliminary CES experiments conducted using a synthetic “prebiotic soup” and pyrite grains, which yield dynamical patterns that are suggestive of the emergence of mutual catalysis. While more research is needed to better understand the specific patterns observed here and determine whether they are reflective of self-propagation, these results illustrate the potential power of CES to test competing hypotheses for the emergence of protobiological chemical systems.

autocatalysis↗

DOE OSTI2021

Challenges to detection: Humidity as a spur to chemical agent change

Detecting chemical agents in outdoor environments such as a battlefield is made challenging by not only the spurious signatures from background chemicals and surfaces (e.g. asphalt, dirt, concrete), but also by the chemical transformation of the actual agents. The change of CW agents to other species can be catalyzed by other chemicals present in the scene, by different substrates, as well as by local weather conditions. Some of the final environmental transformation products are known (e.g. for the G agents methylphosphonic acid), but many of the intermediate chemical states are not, nor are the rates of transformation to the other intermediates or the end products. In this study we have made preliminary optical investigations into the degradation products of a G-agent intermediate, namely methylphosphonic anhydride and its rate of conversion to the more stable methylphosphonic acid. Using infrared and Raman spectroscopies, we have found that the relative humidity (RH) greatly affects the rate of change and we report first results from these studies.

: chemical warfare agent detection, Methylphosphon↗

DOE OSTI2024

Probabilistic and maximum entropy modeling of chemical reaction systems: Characteristics and comparisons to mass action kinetic models

We demonstrate and characterize a first-principles approach to modeling the mass action dynamics of metabolism. Starting from a basic definition of entropy expressed as a multinomial probability density using Boltzmann probabilities with standard chemical potentials, we derive and compare the free energy dissipation and the entropy production rates. We express the relation between entropy production and the chemical master equation for modeling metabolism, which unifies chemical kinetics and chemical thermodynamics. Because prediction uncertainty with respect to parameter variability is frequently a concern with mass action models utilizing rate constants, we compare and contrast the maximum entropy model, which has its own set of rate parameters, to a population of standard mass action models in which the rate constants are randomly chosen. We show that a maximum entropy model is characterized by a high probability of free energy dissipation rate and likewise entropy production rate, relative to other models. We then characterize the variability of the maximum entropy model predictions with respect to uncertainties in parameters (standard free energies of formation) and with respect to ionic strengths typically found in a cell.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Conceptual range estimation for total cost of ownership of modular process‐intensified chemical plants

Abstract Chemical companies have used modularization to reduce capital costs and project timelines, putting capital to work faster and lowering the risk of entering new markets. Nevertheless, the impacts of using modularization along with advanced technologies, such as process intensification, have not yet been fully realized, often due to the uncertain business risks associated with their implementation. Therefore, new methods are needed for quantifying the impact of modular chemical process intensification (MCPI) on the capital and operating costs of chemical plants to help build a business case for this novel approach. This article presents a new conceptual range estimation technique for total cost of ownership that addresses the deficiencies of existing methods for quantifying MCPI impacts. The incorporation of percentage range estimates was employed to allow for adaptability across various cost and size scales. This work also begins to elucidate how chemical engineering and construction firms can benefit from MCPI and identifies barriers that inhibit MCPI applications in the chemical industry.

Alhamouri, Khaled I.↗

DOE OSTI2023

Inclusion Complexes of a Metastable‐State Photoacid with High Acidity and Chemical Stability

Abstract Metastable‐state merocyanine photoacids (MCHs) have been widely applied to various chemical, material and biomedical areas to drive or control chemical processes with light. In this work, stoichiometry and association constants have been determined for inclusion complexes of a photoacid MCH1 ((E)‐3‐(2‐(2‐hydroxystyryl)‐3,3‐dimethyl‐3H‐indol‐1‐ium‐1‐yl)propane‐1‐sulfonate) withβ‐cyclodextrin (β‐CD), 2‐hydroxypropyl‐β‐CD (HP‐β‐CD),γ‐CD and HP‐γ‐CD by means of UV‐Vis absorption spectroscopic titrations. The inclusion complexes were studied to enhance acidity and chemical stability. Kinetic study showed that CDs stabilized the acidic metastable state and slowed its thermal relaxation. The acidity of the ground and metastable state (pK a GS and pK a MS ) increased upon addition of CDs. The pK a MS of [MCH1 ⋅ (γ‐CD) 2 ] is as low as 0.92 in comparison with 2.24 for MCH1, which is close to the lowest pK a MS values (1.20 and 1.03) reported previously, in which case the MCH1 was structurally modified with alkylammonium side chains. Addition of CDs also significantly enhanced the chemical stability of MCH1 against hydrolysis, which is one of the major concerns for the application of MCHs. In particular, the addition of HP‐β‐CD increased the half‐life of MCH1 in aqueous solution more than four‐fold. Moreover, the quantum chemical calculations confirmed the stoichiometry and analyzed the binding sites and hydrogen bonds of the inclusion complexes.

Chemistry↗

DOE OSTI2022

A generic scenario analysis of end-of-life plastic management: Chemical additives

Plastic growing demand and the increment in global plastics production have raised the number of spent plastics, out of which over 90% are either landfilled or incinerated. Both methods for handling spent plastics are susceptible to releasing toxic substances, damaging air, water, soil, organisms, and public health. Improvements to the existing infrastructure for plastics management are needed to limit chemical additive release and exposure resulting from the end-of-life (EoL) stage. This article analyzes the current plastic waste management infrastructure and identifies chemical additive releases through a material flow analysis. Additionally, we performed a facility-level generic scenario analysis of the current U.S. EoL stage of plastic additives to track and estimate their potential migration, releases, and occupational exposure. Potential scenarios were analyzed through sensitivity analysis to examine the merit of increasing recycling rates, using chemical recycling, and implementing additive extraction post-recycling. Our analyses identified that the current state of plastic EoL management possesses high mass flow intensity toward incineration and landfilling. Although maximizing the plastic recycling rate is a reasonably straightforward goal for enhancing material circularity, the conventional mechanical recycling method requires improvement because major chemical additive release and contamination routes act as obstacles to achieving high-quality plastics for future reuse and should be mitigated through chemical recycling and additive extraction. In conclusion, the potential hazards and risks identified in this research create an opportunity to design a safer closed-loop plastic recycling infrastructure to handle additives strategically and support sustainable materials management efforts to transform the US plastic economy from linear to circular.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Role of chemical disorder on radiation-induced defect production and damage evolution in NiFeCoCr

Understanding chemical disorder in many concentrated solid solution alloys (CSAs) at the levels of electrons and atoms has attracted increasing attention as a path forward to reveal and identify underlying mechanisms for extraordinary mechanical properties and improved radiation tolerance. Single-phase NiFeCoCr CSA is a common base for many high-entropy alloys (HEAs) that have shown improved mechanical strength and radiation tolerance. In this study, defect production and damage evolution in NiFeCoCr under ion irradiation at room temperature to dose over 20 dpa are determined using ion channeling technique along both <100> and <110> directions utilizing multiple probing beam energies. The results obtained from the multi-axial and multi-energy channeling analysis are compared with those previously obtained for Ni crystals irradiated under similar conditions. The influence of chemical complexity on defect production and clustering at early-stage under room temperature irradiation up to dose of 1 dpa is discussed based on positron annihilation spectroscopy results. Defect structure evaluation in Ni and NiFeCoCr is also discussed based on transmission electron microscopy results over a prolonged irradiation at both room and elevated temperatures. Compared with chemically complex NiFeCoCr, larger dislocation loops thus less lattice strain are expected to form in pure Ni. Moreover, the role of chemical disorder in this CSA is also investigated based on ab initio calculations using large supercells. Finally, to understand the impact of chemical complexity effect on defect structure evolution, this integrated research effort attempts to link the relatively large charge redistribution due to difference in valence electron counts resulting from alloying different 3d transition metal elements, moderate lattice distortion arising from similar adaptable atomic size, and notable suppressed or delayed damage evolution in NiFeCoCr.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Ingeniously enhanced ferromagnetism in chemically-reduced 2D Ti 3 C 2 T X MXene

Chemical reduction is a facile and cost-effective technique for the modulation of the physical and chemical properties of nanomaterials. In this work, we demonstrate an enhancement of the magnetic behavior of Ti 3 C 2 T x MXene after its chemical reduction via l-ascorbic acid treatment. Small ferromagnetic loops have been observed below 50 K for Ti 3 C 2 T x prepared by hydrofluoric acid (HF) etching of Al layers from Ti 3 AlC 2 . Such a ferromagnetic ordering of spins was significantly enhanced via a chemical reduction of Ti 3 C 2 T x with l-ascorbic acid. Ferromagnetic hysteresis loops were observed for reduced Ti 3 C 2 T x (r-Ti 3 C 2 T x ) up to 150 K indicating a significant upshift of the paramagnetic to the ferromagnetic transition temperature, pushing towards room temperature. The enhancement of ferromagnetism and upshift of the ferromagnetic transition temperature could be attributed to the localized unpaired electron in Ti-3d orbital of the r-Ti 3 C 2 T x crystal and increase in the number of unsaturated Ti atoms upon l-ascorbic acid treatment. Chemical reduction via l-ascorbic acid treatment shows a promising pathway towards the modulation and enhancement of magnetism in various MXene materials for the development of 2D metallic soft ferromagnets and spintronic devices.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Automated theoretical chemical kinetics: Predicting the kinetics for the initial stages of pyrolysis

Large scale implementation of high level computational theoretical chemical kinetics offers the prospect for dramatically improving the fidelity of combustion chemical modeling. To facilitate such efforts, we have developed a suite of codes, collectively referred to as AutoMech, that allow for the automatic prediction of the kinetics for large sets of reactions via ab initio transition-state-theory based master-equation calculations. The primary input is simply the mechanism, a dictionary relating chemically identifiable species descriptors (e.g., SMILES or InChIs) to species labels in the mechanism, and a specification of the electronic structure and transition state theory models to be implemented. Here we illustrate the current utility of AutoMech through a study of the initial stages of pyrolysis for 3 sets of fuels: sequences of alkanes, alcohols, and aldehydes. For simplicity, the analysis focuses on abstractions from the fuel by H, CH 3 , and OH, and the decomposition of the resulting radicals. Altogether, there are a total of 166 input channels in these sets (more than 363 forward reactions when expanded to the full set of elementary reactions). The code successfully produces high quality rate estimates (with apparent uncertainties less than a factor of two in limited comparisons with experiment) for > 95% of these. For the radical decomposition reactions, the analysis includes predictions for the pressure dependence of the kinetics. This wide-ranging exploration illustrates (i) the effect of different levels of prediction on the expected accuracy, (ii) the branching between abstractions at different sites for different abstractors, (iii) the dependence of the rates on the chemical structure, and (iv) the variation in radical stabilities across chemical families. These results, as well as the demonstrated feasibility of the methodology, should find further utility in the development of accurate rate expressions for arbitrary fuels. (c) 2020 The Combustion Institute. Published by Elsevier Inc. All rights reserved.

Elliott, Sarah N.↗

DOE OSTI2023

Protection against Chemical Warfare Agents and Biological Threats Using Metal–Organic Frameworks as Active Layers

The SARS-CoV-2 pandemic outbreak and the unfortunate misuse of toxic chemical warfare agents (CWAs) highlight the importance of developing functional materials to protect against these chemical and pathogen threats. Metal–organic frameworks (MOFs), which comprise a tunable class of crystalline porous materials built from inorganic nodes and organic linkers, have emerged as a class of heterogeneous catalysts capable of rapid detoxification of multiple classes of these harmful chemical or biological hazards. In particular, zirconium-based MOFs (Zr-MOFs) feature Lewis acidic nodes that serve as active sites for a wide range of catalytic reactions, including the hydrolysis of organophosphorus nerve agents within seconds in basic aqueous solutions. In addition, postsynthetic modification of Zr-MOFs enables the release of active species capable of reacting with and deactivating harmful pathogens. Despite this impressive performance, utilizing Zr- MOFs in powder form is not practical for application in masks or protective uniforms. To address this challenge, our team sought to develop MOF/fiber composite systems that could be adapted for use under realistic operating conditions to protect civilians, military personnel, and first responders from harmful pathogens and chemical warfare agents. Over the last several years, our group has designed and fabricated reactive and biocidal MOF/fiber composites that effectively capture and deactivate these toxic species. In this Account, we describe the evolution of these porous and reactive MOF/fiber composites and focus on key design challenges and considerations. First, we devised a scalable method for the integration of Zr-MOFs onto textile substrates using aqueous precursor solutions and without using pretreated textiles, highlighting the potential scalability of this method. Moving beyond standard textiles, we also developed a microbial synthesis strategy to prepare hierarchically porous MOF/bacterial cellulose nanofiber composite sponges that can both capture and detoxify nerve agents when exposed to contaminated gas flows. The mass loading of the MOF in the nanofibrous composite sponge is up to 90%, affording higher work capacities compared to those of textile-fiber-based composites with relatively lower MOF loadings. Next, we demonstrated that heterogeneous polymeric bases are suitable replacements for volatile liquid bases typically used in solution-phase reactions, and we showed that these composite systems are capable of effectively hydrolyzing nerve agents in the solid state by using only water that is present as humidity. Moreover, incorporating a reactive dye precursor into the composite affords a dual function sensing and detoxifying material that changes color from white to orange upon reaction with the byproduct following nerve agent hydrolysis, demonstrating the versatility of this platform for use in decontamination applications. We then created chlorine-loaded MOF/fiber composites that act as biocidal and reactive textiles that are capable of not only detoxifying sulfur-mustard-based chemical warfare agents and simulants but also deactivating both bacteria and the SARS-CoV-2 virus within minutes of exposure. Lastly, we synthesized a mixed-metal Ti/Zr-MOF coating on cotton fibers to afford a photoactive biocidal cloth that shows fast and broad-spectrum biocidal performance against viruses and Gram-positive and Gram-negative bacteria under visible light irradiation. Given the tunable, multifunctional nature of these MOF/fiber composites, we believe that this Account will offer new insights for the rational design and preparation of functional MOF/fiber composites and pave the way toward the development of next-generation reactive and protective textiles.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Bioinspired Supercharging of Photoredox Catalysis for Applications in Energy and Chemical Manufacturing

For more than a decade, photoredox catalysis has been demonstrating that when photoactive catalysts are irradiated with visible light, reactions occur under milder, cheaper, and environmentally friendlier conditions. Furthermore, this methodology allows for the activation of abundant chemicals into valuable products through novel mechanisms that are otherwise inaccessible. The photoredox approach, however, has been primarily used for pharmaceutical applications, where its implementation has been highly effective, but typically with a more rudimentary understanding of the mechanisms involved in these transformations. From a global perspective, the manufacture of everyday chemicals by the chemical industry as a whole currently accounts for 10% of total global energy consumption, and generates 7% of the world's greenhouse gases annually. In this context, the Bio-Inspired Light-Escalated Chemistry (BioLEC) Energy Frontier Research Center (EFRC) was founded to supercharge the photoredox approach for applications in chemical manufacturing aimed at reducing its energy consumption and emissions burden, by using bio-inspired schemes to harvest multiple electrons to drive endothermically uphill chemical reactions. The Center comprises a diverse group of researchers with expertise that includes synthetic chemistry, biophysics, physical chemistry, and engineering. Here the team works together to gain a deeper understanding of the mechanistic details of photoredox reactions while amplifying the applications of these light-driven methodologies.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2026

CoSn-type NiIn1–xSbx (0 ≤ x ≤ 0.17): Site-Selective Substitution, Electronic Structure, Chemical Bonding, and Structural Transformation

CoSn-type intermetallic compounds have emerged as a model platform for Kagome-derived flat-band physics, where subtle chemical perturbations can strongly influence electronic structure and phase stability. Here, we present a combined experimental and theoretical study of Sb-substitution in CoSn-type NiIn1–xSbx (0 ≤ x ≤ 0.17) to elucidate the interplay between site selectivity, solubility limit, chemical bonding, and electronic structure. Rietveld refinements on Neutron powder diffraction data confirmed the selective Sb-substitution at the electron-rich In2 (2d) site forming the honeycomb substructure, while the In1 (1a) site within the Kagome layer remains exclusively occupied by In. Density functional theory (DFT) calculations revealed that pristine CoSn-type NiIn hosts Ni 3d-dominated flat bands near the Fermi level (EF), originating from the Kagome-like Ni substructure. Partial replacement of In by Sb within the honeycomb layer alters these flat-band features below EF, reducing the density of states and suppressing the flat-band topology near the Fermi level. Orbital-resolved electronic structure and chemical-bonding analyses show that Sb-substitution enhances Ni-p-block (In/Sb) covalency and optimizes charge compensation, stabilizing the CoSn-type structure up to the solubility limit x ≈ 0.17. Beyond the limit, the higher-Sb compositions show satellite reflections consistent with an incommensurately modulated phase. These results establish a link between site-selective chemical substitution, bonding optimization, and flat-band electronic structure evolution, providing fundamental insights into how chemical substitution influences the electronic properties of Kagome-based intermetallic compounds.

Roy, Nilanjan [National Institute of Technology Si↗

DOE OSTI2020

General Protocol for the Accurate Prediction of Molecular 13 C/ 1 H NMR Chemical Shifts via Machine Learning Augmented DFT

An accurate prediction of NMR chemical shifts at affordable computational cost is very important for different types of structural assignments in experimental studies. Density functional theory (DFT) and gauge-including atomic orbital (GIAO) are two of the most popular computational methods for NMR calculation, yet they often fail to resolve ambiguities in structural assignments. In this work, we present a new method that uses machine learning (ML) techniques (DFT + ML) that significantly increases the accuracy of 13 C/ 1 H NMR chemical shift prediction for a variety of organic molecules. The input of the generalizable DFT + ML model contains two critical parts: one is a vector providing insights into chemical environments, which can be evaluated without knowing the exact geometry of the molecule; the other one is the DFT-calculated isotropic shielding constant. The DFT + ML model was trained with a data set containing 476 13 C and 270 1 H experimental chemical shifts. For the DFT methods used here, the root mean square deviations (RMSDs) for the errors between predicted and experimental 13 C/ 1 H chemical shifts can be as small as 2.10/0.18 ppm, which is much lower than those from simple DFT (5.54/0.25 ppm), or DFT + linear regression (LR) (4.77/0.23 ppm) approaches. It also has a smaller maximum absolute error than two previously proposed NMR-predicting ML models. The robustness of the DFT + ML model is tested on two classes of organic molecules (TIC10 and hyacinthacines), where the correct isomers were unambiguously assigned to the experimental ones. Overall, the DFT + ML model shows promise for structural assignments in a variety of systems, including stereoisomers, that are often challenging to determine experimentally.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Chemical Interface Damping Revealed by Single-Particle Absorption Spectroscopy

Plasmon-induced interfacial charge separation is a promising way to efficiently extract energetic carriers through direct plasmon decay. This mechanism of charge transfer has been investigated by single-particle scattering spectroscopy, which measures the homogeneous plasmon line width. The line width is broadened by charge transfer, generally known as chemical interface damping. However, conflicting reports exist regarding the effect of chemical interface damping on the corresponding single-particle absorption spectrum, which is needed to accurately determine absolute light conversion efficiencies. This work aims to resolve this question by directly correlating absorption and scattering spectra of individual gold nanorods in the presence and absence of a charge-accepting interface. We find that for TiO 2 coated nanorods, the absorption line width is indeed broadened due to chemical interface damping but is overall narrower than the scattering line width. Here, chemical interface damping is furthermore found to increase with larger resonance energies. The observed differences in line widths between absorption and scattering are elucidated within the context of an analytically tractable model describing the lowest energy optically bright and higher-order optically dark plasmon modes of the nanorod, including bulk, radiative, and chemical interface damping effects. Taken together, these results establish that single-particle absorption spectroscopy is capable of revealing interfacial charge injection by direct plasmon decay.

absorption↗

DOE OSTI2021

High Resolution Infrared Spectroscopy of Highly Reactive Chemical Intermediates: Berkeley Inspiration and a C.B. Moore Retrospective

One of the long-standing paradoxes in chemistry is that the molecular species of greatest interest are often highly reactive chemical intermediates and thus present only at vanishingly small concentrations under typical steady state conditions. Here, this has proven both a key frustration and powerful motivation for physical chemists in probing such species, particularly for high resolution laser spectroscopic methods in the infrared. Over the past 4-5 decades, this fundamental sensitivity challenge has stimulated the development of many novel techniques for efficient generation, cooling, and infrared probing of such transient chemical intermediates, achieving increasingly sophisticated levels of spectroscopic detail, chemical insight, and a testbed for comparison with first principles ab initio quantum calculations. Two of the pioneering contributors to this scientific arena have been George Pimentel and C. Bradley Moore, both responsible for nourishing DJN’s own enduring fascination with spectroscopic IR study of elusive chemical intermediates. This chapter is not intended to provide a comprehensive review of this large and enormously successful field, but it rather more simply attempts to capture a few selective “Moore-centric” snapshots of the Nesbitt group scientific evolution, specifically in i) advances by Pimentel for the original development of rapid scan flash kinetic spectroscopy based on spectrometers with rapidly rotating IR gratings, ii) the modernization/extension of these methods in the Moore group toward the first high resolution IR laser spectroscopy of singlet/triplet methylene 1,3 CH 2 diradical, and finally to iii) the development of slit supersonic discharge expansion methods at JILA for sub-Doppler infrared laser study of multiple radicals, jet cooled molecular ions, and highly reactive chemical intermediates.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2026

Imaging from Macro to Nanoscale: Multimodal Advances in Chemical and Biomedical Imaging

Imaging increasingly serves as a multiscale framework for linking molecular mechanisms to cellular behavior, tissue architecture, and organ phenotypes in biology and unraveling fundamental processes in chemistry, physics and materials science. This Perspective highlights recent advances in chemical and biomedical imaging across macro-, micro-, and nanoscales, using representative examples published in Chemical and Biomedical Imaging (CBMI). At the macroscale, we discuss chemically selective MRI, including endogenous and exogenous CEST strategies, together with photoacoustic imaging as a hybrid modality with functional and chemical contrast. At the microscale, we consider fluorescence, label-free optical and vibrational imaging, and selected X-ray approaches that expand sensitivity, specificity, and temporal resolution in biological and materials systems. At the nanoscale, we highlight super-resolution fluorescence microscopy, single-molecule methods, tip-enhanced Raman spectroscopy, and correlative imaging strategies that resolve local heterogeneity and molecular organization. Across scales, a common theme emerges that advances in probes, contrast mechanisms, instrumentation, and sample handling are enabling chemically informed imaging that connects molecular specificity with biological context.

multiscale imaging↗

DOE OSTI2025

Photon–photon chemical thermodynamics of frequency conversion processes in highly multimode systems

Abstract Frequency generation in highly multimode nonlinear optical systems is inherently a complex process, giving rise to an exceedingly convoluted landscape of evolution dynamics. While predicting and controlling the global conversion efficiencies in such nonlinear environments has long been considered impossible, here, we formally address this challenge even in scenarios involving a very large number of spatial modes. By utilizing fundamental notions from optical statistical mechanics, we develop a universal theoretical framework that effectively treats all frequency components as chemical reactants/products, capable of undergoing optical thermodynamic reactions facilitated by a variety of multi-wave mixing effects. These photon–photon reactions are governed by conservation laws that directly determine the optical temperatures and chemical potentials of the ensued chemical equilibria for each frequency species. In this context, we develop a comprehensive stoichiometric model and formally derive an expression that relates the chemical potentials to the optical stoichiometric coefficients, in a manner akin to atomic/molecular chemical reactions. This advancement unlocks new predictive capabilities that can facilitate the optimization of frequency generation in highly multimode photonic arrangements, surpassing the limitations of conventional schemes that rely exclusively on nonlinear optical dynamics. Notably, we identify a universal regime of Rayleigh–Jeans thermalization where an optical reaction at near-zero optical temperatures can promote the complete and entropically irreversible conversion of light to the fundamental mode at a target frequency. Our theoretical results are corroborated by numerical simulations in settings where second-harmonic generation, sum-frequency generation and four-wave mixing processes can manifest.

Optics↗

DOE OSTI2026

Detection and imaging of chemicals and hidden explosives using terahertz time-domain spectroscopy and deep learning

Detecting concealed chemicals and explosives remains a critical challenge in global security. Terahertz time-domain spectroscopy (THz-TDS) offers a promising non-invasive and stand-off detection technique owing to its ability to penetrate optically opaque materials without causing ionization damage. While many chemicals exhibit distinct spectral features in the terahertz range, conventional terahertz-based detection methods often struggle in real-world environments, where variations in sample geometry, thickness, and packaging can lead to inconsistent spectral responses. In this study, we present a chemical imaging system that integrates THz-TDS with deep learning to enable accurate pixel-level identification and classification of different explosives. Operating in reflection mode and enhanced with plasmonic nanoantenna arrays, our THz-TDS system achieves a peak dynamic range of 96 dB and a detection bandwidth of 4.5 THz, supporting practical, stand-off operation. By analyzing individual time-domain pulses with deep neural networks, the system exhibits strong resilience to environmental variations and sample inconsistencies. Blind testing across eight chemicals—including pharmaceutical excipients and explosive compounds—resulted in an average classification accuracy of 99.42% at the pixel level. Notably, the system maintained an average accuracy of 88.83% when detecting explosives concealed under opaque paper coverings, demonstrating its robust generalization capability. These results highlight the potential of combining advanced terahertz spectroscopy with neural networks for highly sensitive and specific chemical and explosive detection in diverse and operationally relevant scenarios.

Imaging and sensing↗