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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 523 records · Page 29
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Tailoring the Properties of Chemically Recyclable Polyethylene‐Like Multiblock Polymers by Modulating the Branch Structure

Abstract Developing plastics that fill the need of polyolefins yet are more easily recyclable is a critical need to address the plastic waste crisis. However, most efforts in this vein have focused on high‐density polyethylene (PE), while many different types of PE exist. To create broadly sustainable PE with modular properties, we present the synthesis, characterization, and demonstration of materials applications for chemically recyclable PE‐like multiblock polymers prepared from distinct hard and soft blocks using ruthenium‐catalyzed dehydrogenative polymerization. By altering the branching pattern within the soft blocks, a series of PE‐like multiblock polymers were synthesized with tunable glass transition temperatures ( T g ) while maintaining consistent high melting temperatures ( T m ). A clear U‐shape trend between T g and mechanical properties was found, showcasing their potential as sustainable materials with tailored properties spanning commercial linear low‐density polyethylene (LLDPE) and low‐density polyethylene (LDPE). These materials offer adjustable adhesive strength to metal and demonstrate chemical recyclability and selective depolymerization in mixed plastic streams, promoting circularity and separation.

Zhao, Yucheng↗
DOE OSTI2022

Static and Dynamic Gradient Based Directional Transportation of Neutral Molecules in Swollen Polymer Films

Abstract Materials which selectively transport molecules offer powerful opportunities for concentrating and separating chemical agents. Here, utilizing static and dynamic chemical gradients, transport of molecules within swollen crosslinked polymers is demonstrated. Using an ≈200 μm static hydroxyl to hexyl gradient, the neutral ambipolar nerve agent surrogate diethyl (cyanomethyl)phosphonate (DECP) is directionally transported and concentrated 60‐fold within 4 hours. To accelerate transport kinetics, a dynamic gradient (a “travelling wave”) is utilized. Here, the non‐polar dye pyrene was transported. The dynamic gradient is generated by an ion exchange process triggered by the localized introduction of an aqueous NaCl solution, which converts the gel from hydrophobic to hydrophilic. As the hydrophilic region expands, associated water enters the gel, and pyrene is pushed ahead of the expansion front. The dynamic gradient provides about 10‐fold faster transport kinetics than the static gradient.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Picosecond laser generated plasma as a source of singly charged ions for external injection into an EBIS

The focus of this paper is on practical aspects of ion generation by picosecond lasers as a source of low charge state ions for external injection of ions into an electron beam ion source (EBIS). For a ps-laser, compared to a ns-laser currently used at Relativistic Heavy Ion Collier Electron Beam Ion Source (RHIC EBIS), the influence of heat conductivity in the solid target is almost negligible, which results in a lower target consumption rate and less vacuum pressure rise. By using a laser with high repetition rate, it is possible to produce quasi continuous 1+ ion beams for periods of tens of milliseconds, making it possible to take advantage of the ability of the EBIS to accumulate ions in the “slow” injection capture mode. Producing such an ion pulse train is now feasible since ps-lasers with pulse energy up to 10 mJ and rep-rate up to 10 kHz have recently become commercially available. The advantages of this lower current accumulation scheme include: (1) easier beam transport due to lower space charge of lower beam currents required compared to the present single pulse fast injection scheme, (2) better EBIS pulse to pulse ion output stability since pulse to pulse ion variations of the laser source are averaged over a long train of laser pulses, and (3) the possible addition of an isotope separator in which rare beams could be produced and selected at run time from less expensive (unenriched) targets. We studied the properties of plasmas generated by a ps-laser with 1.27 mJ energy within an 8 ps pulse and a repetition rate up to 400 Hz at Argonne National Laboratory to investigate feasibility and specify parameters for a laser ion source for RHIC EBIS using a “slow” injection mode. Al, Ti, Cu, Nb, and Ta targets were tested with various target translation speeds. Here, we demonstrated that a both “slow” and “fast” injection modes are accessible with a single ion source geometry and single injection line, providing the most attractive option for an ion source for external injection into RHIC EBIS trap based on a ps-laser.

43 PARTICLE ACCELERATORS↗
DOE OSTI2021

Navigating Transition-Metal Chemical Space: Artificial Intelligence for First-Principles Design

Conspectus The variability of chemical bonding in open-shell transition-metal complexes not only motivates their study as functional materials and catalysts but also challenges conventional computational modeling tools. Here, tailoring ligand chemistry can alter preferred spin or oxidation states as well as electronic structure properties and reactivity, creating vast regions of chemical space to explore when designing new materials atom by atom. Although first-principles density functional theory (DFT) remains the workhorse of computational chemistry in mechanism deduction and property prediction, it is of limited use here. DFT is both far too computationally costly for widespread exploration of transition-metal chemical space and also prone to inaccuracies that limit its predictive performance for localized d electrons in transition-metal complexes. These challenges starkly contrast with the well-trodden regions of small-organic-molecule chemical space, where the analytical forms of molecular mechanics force fields and semiempirical theories have for decades accelerated the discovery of new molecules, accurate DFT functional performance has been demonstrated, and gold-standard methods from correlated wavefunction theory can predict experimental results to chemical accuracy. The combined promise of transition-metal chemical space exploration and lack of established tools has mandated a distinct approach. In this Account, we outline the path we charted in exploration of transition-metal chemical space starting from the first machine learning (ML) models (i.e., artificial neural network and kernel ridge regression) and representations for the prediction of open-shell transition-metal complex properties. The distinct importance of the immediate coordination environment of the metal center as well as the lack of low-level methods to accurately predict structural properties in this coordination environment first motivated and then benefited from these ML models and representations. Once developed, the recipe for prediction of geometric, spin state, and redox potential properties was straightforwardly extended to a diverse range of other properties, including in catalysis, computational “feasibility”, and the gas separation properties of periodic metal–organic frameworks. Interpretation of selected features most important for model prediction revealed new ways to encapsulate design rules and confirmed that models were robustly mapping essential structure–property relationships. Encountering the special challenge of ensuring that good model performance could generalize to new discovery targets motivated investigation of how to best carry out model uncertainty quantification. Distance-based approaches, whether in model latent space or in carefully engineered feature space, provided intuitive measures of the domain of applicability. With all of these pieces together, ML can be harnessed as an engine to tackle the large-scale exploration of transition-metal chemical space needed to satisfy multiple objectives using efficient global optimization methods. In practical terms, bringing these artificial intelligence tools to bear on the problems of transition-metal chemical space exploration has resulted in ML-model assessments of large, multimillion compound spaces in minutes and validated new design leads in weeks instead of decades.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Visualizing defects and pore connectivity within metal–organic frameworks by X-ray transmission tomography

Metal–Organic Frameworks (MOFs) have the potential to change the landscape of molecular separations in chemical processes owing to their ability of selectively binding molecules. Their molecular sorting properties generally rely on the micro- and meso-pore structure, as well as on the presence of coordinatively unsaturated sites that interact with the different chemical species present in the feed. In this work, we show a first-of-its-kind tomographic imaging of the crystal morphology of a metal– organic framework by means of transmission X-ray microscopy (TXM), including a detailed data reconstruction and processing approach. Corroboration with Focused Ion Beam-Scanning Electron Microscopy (FIB-SEM) images shows the potential of this strategy for further (non-destructively) assessing the inner architecture of MOF crystals. By doing this, we have unraveled the presence of large voids in the internal structure of a MIL-47(V) crystal, which are typically thought of as rather homogeneous lattices. This challenges the established opinion that hydrothermal syntheses yield relatively defect-free material and sheds further light on the internal morphology of crystals.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Efficient separation of xylene isomers by using a robust calcium-based metal–organic framework through a synergetic thermodynamically and kinetically controlled mechanism

Adsorptive separation of physically and chemically similar molecules and understanding the underlying host–guest interactions at the molecular level are of significant scientific and practical importance. Here we report the development of a novel calcium-based metal–organic framework, formulated as Ca 3 (Htcpp) 2 (H 4 tcpp = 2,3,5,6-tetrakis(4-carboxyphenyl)-pyrazine) featuring microporosity and high stability. This compound shows distinct adsorption behavior toward xylene isomers and is capable of separating them efficiently at an industrially relevant temperature. The selective adsorption is attributed to a synergetic thermodynamic and kinetic effect. Here, the host–guest interactions were probed directly by single-crystal X-ray diffraction analysis and the adsorption affinity was evaluated through computational molecular simulations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Modeling separation of lanthanides via heterogeneous ligand binding

Individual lanthanide elements have physical/electronic/magnetic properties that make each useful for specific applications. Several of the lanthanides cations (Ln 3+ ) naturally occur together in the same ores. They are notoriously difficult to separate from each other due to their chemical similarity. Predicting the Ln 3+ differential binding energies (ΔΔE) or free energies (ΔΔG) at different binding sites, which are key figures of merit for separation applications, will help design of materials with lanthanide selectivity. We apply ab initio molecular dynamics (AIMD) simulations and density functional theory (DFT) to calculate ΔΔG for Ln 3+ coordinated to ligands in water and embedded in metal–organic frameworks (MOFs), and ΔΔE for Ln 3+ bonded to functionalized silica surfaces, thus circumventing the need for the computational costly absolute binding (free) energies ΔG and ΔE. Perturbative AIMD simulations of water-inundated simulation cells are applied to examine the selectivity of ligands towards adjacent Ln 3+ in the periodic table. Static DFT calculations with a full Ln 3+ first coordination shell, while less rigorous, show that all ligands examined with net negative charges are more selective towards the heavier lanthanides than a charge-neutral coordination shell made up of water molecules. Amine groups are predicted to be poor ligands for lanthanide-binding. Finally, we also address cooperative ion binding, i.e., using different ligands in concert to enhance lanthanide selectivity.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Relationship between microporous structure and light gas transport through glassy polymeric membranes revealed by molecular simulations

Microporous glassy polymers are attractive materials for gas separation membranes, due to their high permeability and tailorable selectivity, provided physical aging can be delayed. The archetypal microporous glassy polymer, PTMSP, can be blended with a hyper-crosslinked isatin–triptycene porous polymer network (PPN) to delay physical aging. However, while PPN is effective at reducing physical aging, it also affects the permeability of light gases through PTMSP. Molecular dynamics simulations were used here to shed fundamental light on the mechanisms responsible for these effects. Atomistic models are developed that satisfactorily reproduce experimental observations such as matrix density and cavity size distributions for neat PTMSP as well as for PTMSP–PPN blends. Analysis of the simulation results suggests that physical aging is delayed because the PPN inclusions slow down PTMSP relaxation while reducing the connectivity between free volume pockets. To understand how PPN inclusions affect light gas permeability, the atomistic models developed are used to probe CO 2 and CH 4 diffusion and sorption. These simulations are conducted for PTMSP matrices exhibiting varying density, towards reproducing experimental permeability data. Interrogating the simulation trajectories, it is discovered that while CH 4 travels preferentially through the free volume cavities, CO 2 preferentially interacts with the available surfaces, especially in the presence of PPN. These differences help interpret experimental observations. The transport of C 2 H 6 and H 2 S through PTMSP matrices was also investigated. These gases, and in particular H 2 S, were found to absorb within PTMSP, yielding very low diffusion coefficients. The ability to predict differences in diffusion pathways for various natural gas components unveils the possibility of engineering membranes to control permeability and selectivity towards large-scale applications.

Bao Le, Tran Thi [Univ. of Oklahoma, Norman, OK (U↗
DOE OSTI2026

Many roads to the seam: How conformational flexibility drives nonadiabatic relaxation in a prototypical tetrapyrrolic chromophore

Large and structurally flexible chromophores pose challenges for in silico modeling of photodeactivation due to the many vibrational modes that can funnel the system toward energy degeneracy. In this work, we examine how the multiple degrees of freedom in biliverdin, a prototypical tetrapyrrolic chromophore, cooperate to drive access to the S 1 /S 0 intersection seam in vacuo. We begin by mapping the ground-state potential energy surface to identify representative biliverdin conformers relevant to photoexcitation. We then use a CASSCF-based framework to map the excited-state landscape and characterize the intersection seam, identifying distinct conical-intersection types. Finally, we employ ab initio multiple spawning to resolve the dynamical pathways by which the system accesses these regions. DFT potential-energy and free-energy mappings indicate that, although several conformers are relevant, the “locked-helix” ZsZsZs conformer predominates in the ground state. The intersection seam comprises numerous geometrically distinct regions characterized by varying degrees and combinations of dihedral torsion, pyramidalization, and bond-length alternation. Yet only select regions lie within energetic reach, and moderate barriers separate them from the S 1 minimum. Nonadiabatic dynamics combined with multivariate analyses show that, despite extensive mode coupling during deactivation that guides the system toward multiple regions of the seam, a single dihedral torsion, together with bond-length alternation, predominantly drives energy degeneracy. This work offers new insight into biliverdin’s intrinsic photochemical response and underscores a general feature of flexible chromophores: many modes may participate during photorelaxation, but only a limited subset ultimately dictates seam accessibility.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

First β-decay spectroscopy of 135 In and new β-decay branches of 134 In

The β decay of the neutron-rich 134 In and 135 In was investigated experimentally in order to provide new insights into the nuclear structure of the tin isotopes with magic proton number Z=50 above the N=82 shell. The β-delayed γ-ray spectroscopy measurement was performed at the ISOLDE facility at CERN, where indium isotopes were selectively laser-ionized and on-line mass separated. Three β-decay branches of 134 In were established, two of which were observed for the first time. Population of neutron-unbound states decaying via γ rays was identified in the two daughter nuclei of 134 In, 134 Sn and 133 Sn, at excitation energies exceeding the neutron separation energy by 1 MeV. The β-delayed one- and two-neutron emission branching ratios of 134 In were determined and compared with theoretical calculations. The β-delayed one-neutron decay was observed to be dominant β-decay branch of 134 In even though the Gamow-Teller resonance is located substantially above the two-neutron separation energy of 134 Sn. Transitions following the β decay of 135 In are reported for the first time, including γ rays tentatively attributed to 135 Sn. In total, six new levels were identified in 134 Sn on the basis of the βγγ coincidences observed in the 134 In and 135 Inβ decays. A transition that might be a candidate for deexciting the missing neutron single-particle 13/2 + state in 133 Sn was observed in both β decays and its assignment is discussed. Experimental level schemes of 134 Sn and 135 Sn are compared with shell-model predictions. Using the fast timing technique, half-lives of the 2 + , 4 + , and 6 + levels in 134 Sn were determined. From the lifetime of the 4 + state measured for the first time, an unexpectedly large B(E2;4 + → 2 + ) transition strength was deduced, which is not reproduced by the shell-model calculations.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Prediction of DIII-D Pedestal Structure From Externally Controllable Parameters

The sharp increase of pressure at the edge of a high confinement mode (H-mode) plasma, the pedestal, strongly impacts overall plasma performance. Predicting the pedestal is a necessity to control and optimize tokamak operations. Here, an experimental data-driven machine learning (ML) approach is presented that predicts the pedestal heights and widths of electron density (n e ) and electron temperature (T e ) profiles as well as the separatrix ne from externally controllable parameters such as the plasma shape, heating method and power, and gas puff rate and integrated gas puff. The OMFIT framework was used with DIII-D data to efficiently, robustly, and automatically build a database of pedestal parameters to train machine learning models. Database creation was enabled by the search engine tool for DIII-D data, TokSearch, which parallelizes data fetching, enabling fast searches through basic signals of thousands of DIII-D shots and selection of relevant time intervals. Principal Component Analysis (PCA) separated the database into three clusters that represent classes of plasma shapes that are regularly used in DIII-D. The most important parameters for setting the pedestal structure were plasma current (I p ), toroidal magnetic field (B Φ ), neutral beam heating power (P NBI ) and shaping quantities. The Deep Jointly Informed Neural Networks (DJINN) algorithm was applied to identify suitable neural network (NN) architectures that appropriately capture the features of the pedestal database. Separate NNs were implemented for each pedestal parameter, and ensembling methods were used to improve the prediction accuracy and allowed estimation of the prediction uncertainty. The pedestal predictions of the test dataset lie within the measurement uncertainties of the pedestal parameters. The NN outperformed simple Linear Regression (LR) analysis, indicating non-linear dependencies in the pedestal structure. The presented achievements illustrate a promising path for future research, using feature extraction to infer experimental trends and thereby improve pedestal models as well as deploying NN for a fast pedestal prediction in DIII-D scenario development.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Detailed Simulation Study to Evaluate Future Transportation Decarbonization Potential

This report is the ninth revision of a continuous-improvement, scenario-based study on program benefits from the U.S. Department of Energy’s Vehicle Technologies Office and Hydrogen and Fuel Cell Technologies Office. With each revision of the study, Argonne made improvements to the assumptions, control strategies at the vehicle level, methodologies, powertrain selections, and the number of vehicles analyzed. In 2019, separate reports were published on the analysis of light-duty and heavy-duty vehicles. From 2020 onwards, summaries for both light- and heavy-duty vehicles are consolidated into one report. The first part of the report covers the impact of technology progress on light-duty vehicles and the second part of the report is devoted to the technology progress assumed for medium- and heavy-duty trucks.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2022

The Time Domain Spectroscopic Survey: Changing-look Quasar Candidates from Multi-epoch Spectroscopy in SDSS-IV

Active galactic nuclei (AGN) can vary significantly in their rest-frame optical/UV continuum emission, and with strong associated changes in broad line emission, on much shorter timescales than predicted by standard models of accretion disks around supermassive black holes. Most such changing-look or changing-state AGN—and at higher luminosities, changing-look quasars (CLQs)—have been found via spectroscopic follow-up of known quasars showing strong photometric variability. The Time Domain Spectroscopic Survey of the Sloan Digital Sky Survey IV (SDSS-IV) includes repeat spectroscopy of large numbers of previously known quasars, many selected irrespective of photometric variability, and with spectral epochs separated by months to decades. Our visual examination of these repeat spectra for strong broad line variability yielded 61 newly discovered CLQ candidates. We quantitatively compare spectral epochs to measure changes in continuum and Hβ broad line emission, finding 19 CLQs, of which 15 are newly recognized. The parent sample includes only broad line quasars, so our study tends to find objects that have dimmed, i.e., turn-off CLQs. However, we nevertheless find four turn-on CLQs that meet our criteria, albeit with broad lines in both dim and bright states. We study the response of Hβ and Mg ii emission lines to continuum changes. The Eddington ratios of CLQs are low, and/or their Hβ broad line width is large relative to the overall quasar population. Repeat quasar spectroscopy in the upcoming SDSS-V black hole Mapper program will reveal significant numbers of CLQs, enhancing our understanding of the frequency and duty cycle of such strong variability, and the physics and dynamics of the phenomenon.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Chemical Cartography with APOGEE: Mapping Disk Populations with a 2-process Model and Residual Abundances

We apply a novel statistical analysis to measurements of 16 elemental abundances in 34,410 Milky Way disk stars from the final data release (DR17) of APOGEE-2. Building on recent work, we fit median abundance ratio trends [X/Mg] versus [Mg/H] with a 2-process model, which decomposes abundance patterns into a "prompt" component tracing core-collapse supernovae and a "delayed" component tracing Type Ia supernovae. For each sample star, we fit the amplitudes of these two components, then compute the residuals Δ[X/H] from this two-parameter fit. The rms residuals range from ~0.01–0.03 dex for the most precisely measured APOGEE abundances to ~0.1 dex for Na, V, and Ce. The correlations of residuals reveal a complex underlying structure, including a correlated element group comprised of Ca, Na, Al, K, Cr, and Ce and a separate group comprised of Ni, V, Mn, and Co. Selecting stars poorly fit by the 2-process model reveals a rich variety of physical outliers and sometimes subtle measurement errors. Residual abundances allow for the comparison of populations controlled for differences in metallicity and [α/Fe]. Relative to the main disk (R = 3–13 kpc), we find nearly identical abundance patterns in the outer disk (R = 15–17 kpc), 0.05–0.2 dex depressions of multiple elements in LMC and Gaia Sausage/Enceladus stars, and wild deviations (0.4–1 dex) of multiple elements in ω Cen. The residual abundance analysis opens new opportunities for discovering chemically distinctive stars and stellar populations, for empirically constraining nucleosynthetic yields, and for testing chemical evolution models that include stochasticity in the production and redistribution of elements.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Prediction of DIII-D Pedestal Structure from Externally Controllable Parameters

The sharp increase of pressure at the edge of a high confinement mode (H-mode) plasma, the pedestal, strongly impacts overall plasma performance. Predicting the pedestal is a necessity to control and optimize tokamak operations. An experimental data-driven machine learning (ML) approach is presented that predicts the pedestal heights and widths of electron density (ne) and electron temperature (Te) profiles as well as the separatrix ne from externally controllable parameters such as the plasma shape, heating method and power, and gas puff rate and integrated gas puff. The OMFIT framework was used with DIII-D data to efficiently, robustly, and automatically build a database of pedestal parameters to train machine learning models. Database creation was enabled by the search engine tool for DIII-D data, TokSearch, which parallelizes data fetching, enabling fast searches through basic signals of thousands of DIII-D shots and selection of relevant time intervals. Principal Component Analysis (PCA) separated the database into three clusters that represent classes of plasma shapes that are regularly used in DIII-D. The most important parameters for setting the pedestal structure were plasma current (Ip), toroidal magnetic field (Bφ), neutral beam heating power (PNBI) and shaping quantities. The Deep Jointly Informed Neural Networks (DJINN) algorithm was applied to identify suitable neural network (NN) architectures that appropriately capture the features of the pedestal database. Separate NNs were implemented for each pedestal parameter, and ensembling methods were used to improve the prediction accuracy and allowed estimation of the prediction uncertainty. The pedestal predictions of the test dataset lie within the measurement uncertainties of the pedestal parameters. The NN outperformed simple Linear Regression (LR) analysis, indicating non-linear dependencies in the pedestal structure. The presented achievements illustrate a promising path for future research, using feature extraction to infer experimental trends and thereby improve pedestal models as well as deploying NN for a fast pedestal prediction in DIII-D scenario development.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Cleaning and dewatering fine coal

Fine coal is cleaned of its mineral matter impurities and dewatered by mixing the aqueous slurry containing both with a hydrophobic liquid, subjecting the mixture to a phase separation. The resulting hydrophobic liquid phase contains coal particles free of surface moisture and droplets of water stabilized by coal particles, while the aqueous phase contains the mineral matter. By separating the entrained water droplets from the coal particles mechanically, a clean coal product of substantially reduced mineral matter and moisture contents is obtained. The spent hydrophobic liquid is separated from the clean coal product and recycled. The process can also be used to separate one type of hydrophilic particles from another by selectively hydrophobizing one.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2022

Wavelength selective transfer of optical energy

An all fiber wavelength selective coupler provides wavelength selective transfer of optical energy between two or more separated waveguides. The coupler includes signal cores that are separated enough that they can be fusion spliced to standard fibers as lead-in and lead-out pigtails. A bridge between the signal cores facilitates transfer of the optical energy through a process of evanescent coupling. In one example, the bridge is formed of a series of graded index cores.

Allen, Graham S.↗
DOE OSTI2026

Ultrafast Spectroscopy in Chemistry

Ultrafast spectroscopy has become an indispensable investigation tool in numerous areas of chemical research encompassing purely synthetic to semiconductor material domains. Harnessing light on its femtosecond time scales offers a glimpse into submolecular processes that direct reaction pathways, energy transfer, charge separation, and many other fundamental chemical phenomena. In this Perspective, we highlight select examples that introduce how ultrafast spectroscopy became a pillar for modern chemical science. We emphasize the achievements of conventional pump–probe spectroscopy by showing its contribution to disentangling peculiar mechanisms of carrier relaxation in semiconductor nanocrystals, charge separation in organic photovoltaic devices, and photoredox catalyst activation. Our examples show that even traditional pump–probe experiments can provide extensive insights that go beyond the resolution of ultrafast time scales if combined with a thorough preliminary assessment of the target system. We conclude with suggestions for how ultrafast spectroscopy in tandem with chemical science can embark on advancing practical quantum information research.

Excitons↗