How Do Methyl Groups Enhance the Triplet Chemiexcitation Yield of Dioxetane?

Dissociation Time, Quantum Yield, and Dynamic Reaction Pathways in the Thermolysis of trans-3,4-Dimethyl-1,2-dioxetane

The thermolysis of trans-3,4-dimethyl-1,2-dioxetane is studied by trajectory surface hopping. The significant difference between long and short dissociation times is rationalized by frustrated dissociations and the time spent in triplet states. If the C-C bond breaks through an excited state channel, then the trajectory passes over a ridge of the potential energy surface of that state. The calculated triplet quantum yields match the experimental results. The dissociation half-times and quantum yields follow the same ascending order as per the product states, justifying the conjecture that the longer dissociation time leads to a higher quantum yield, proposed in the context of the methylation effect. The populations of the molecular Coulomb Hamiltonian and diagonal states reach equilibrium, but the triplet populations with different Sz components fluctuate indefinitely. Certain initial velocities, leading the trajectories to given product states, can be identified as the most characteristic features for sorting trajectories according to their product states.

Unexcited Light Source Imaging for Biomedical Applications

Optical imaging has a wide range of applications in the biomedical field, allowing the visualization of physiological processes and helping in the diagnosis and treatment of diseases. Unexcited light source imaging technologies, such as chemiluminescence imaging, bioluminescence imaging and afterglow imaging have attracted great attention in recent years because of the absence of excitation light interference in their application and the advantages of high sensitivity and high signal-to-noise ratio. In this review, the latest advances in unexcited light source imaging technology for biomedical applications are highlighted. The design strategies of unexcited light source luminescent probes in improving luminescence brightness, penetration depth, quantum yield and targeting, and their applications in inflammation imaging, tumor imaging, liver and kidney injury imaging and bacterial infection imaging are introduced in detail. The research progress and future prospects of unexcited light source imaging for medical applications are further discussed.

Chemiexcitation: Mammalian Photochemistry in the Dark†

Light is one way to excite an electron in biology. Another is chemiexcitation, birthing a reaction product in an electronically excited state rather than exciting from the ground state. Chemiexcited molecules, as in bioluminescence, can release more energy than ATP. Excited states also allow bond rearrangements forbidden in ground states. Molecules with low-lying unoccupied orbitals, abundant in biology, are particularly susceptible. In mammals, chemiexcitation was discovered to transfer energy from excited melanin, neurotransmitters, or hormones to DNA, creating the lethal and carcinogenic cyclobutane pyrimidine dimer. That process was initiated by nitric oxide and superoxide, radicals triggered by ultraviolet light or inflammation. Several poorly understood chronic diseases share two properties: inflammation generates those radicals across the tissue, and cells that die are those containing melanin or neuromelanin. Chemiexcitation may therefore be a pathogenic event in noise- and drug-induced deafness, Parkinson's disease, and Alzheimer's; it may prevent macular degeneration early in life but turn pathogenic later. Beneficial evolutionary selection for excitable biomolecules may thus have conferred an Achilles heel. This review of recent findings on chemiexcitation in mammalian cells also describes the underlying physics, biochemistry, and potential pathogenesis, with the goal of making this interdisciplinary phenomenon accessible to researchers within each field.

Conical Intersection in Chemiluminescence of Cyclic Peroxides

Chemiluminescence (CL) utilizing chemiexcitation for energy transformation is one of the most highly sensitive and useful analytical techniques. The chemiexcitation is a chemical process of a ground-state reactant producing an excited-state product, in which a nonadiabatic event is facilitated by conical intersections (CIs), the specific molecular geometries where electronic states are degenerated. Cyclic peroxides, especially 1,2-dioxetane/dioxetanone derivatives, are the iconic chemiluminescent substances. In this Perspective, we concentrated on the CIs in the CL of cyclic peroxides. We first present a computational overview on the role of CIs between the ground (S₀) state and the lowest singlet excited (S₁) state in the thermolysis of cyclic peroxides. Subsequently, we discuss the role of the S₀/S₁ CI in the CL efficiency and point out misunderstandings in some theoretical studies on the singlet chemiexcitations of cyclic peroxides. Finally, we address the challenges and future prospects in theoretically calculating S₀/S₁ CIs and simulating the dynamics and chemiexcitation efficiency in the CL of cyclic peroxides.

Role of conical intersection seam topography in the chemiexcitation of 1,2-dioxetanes

Chemiexcitation, the generation of electronic excited states by a thermal reaction initiated on the ground state, is an essential step in chemiluminescence, and it is mediated by the presence of a conical intersection that allows a nonadiabatic transition from ground state to excited state. Conical intersections classified as sloped favor chemiexcitation over ground state relaxation. The chemiexcitation yield of 1,2-dioxetanes is known to increase upon methylation. In this work we explore to which extent this trend can be attributed to changes in the conical intersection topography or accessibility. Since conical intersections are not isolated points, but continuous seams, we locate regions of the conical intersection seams that are close to the configuration space traversed by the molecules as they react on the ground state. We find that conical intersections are energetically and geometrically accessible from the reaction trajectory, and that topographies favorable to chemiexcitation are found in all three molecules studied. Nevertheless, the results suggest that dynamic effects are more important for explaining the different yields than the static features of the potential energy surfaces.

Building a Functionalizable, Potent Chemiluminescent Agent: A Rational Design Study on 6,8-Substituted Luminol Derivatives

Luminol is a prominent chemiluminescent (CL) agent, finding applications across numerous fields, including forensics, immunoassays, and imaging. Different substitution patterns on the aromatic ring can enhance or decrease its CL efficiency. We herein report a systematic study on the synthesis and photophysics of all possible 6,8-disubstituted luminol derivatives bearing H, Ph, and/or Me substituents. Their CL responses are monitored at three pH values (8, 10, and 12), thus revealing the architecture with the optimum CL efficiency. The most efficient pattern is used for the synthesis of a strongly CL luminol derivative, bearing a functional group for further, straightforward derivatization. This adduct exhibits an unprecedented increase in chemiluminescence efficiency at pH = 12, pH = 10, and especially at pH = 8 (closer to the biologically relevant conditions) compared to luminol. Complementary work on the fluorescence of the emissive species as well as quantum chemistry computations are employed for the rationalization of the observed results.

Machine Learning for Electronically Excited States of Molecules

Electronically excited states of molecules are at the heart of photochemistry, photophysics, as well as photobiology and also play a role in material science. Their theoretical description requires highly accurate quantum chemical calculations, which are computationally expensive. In this review, we focus on not only how machine learning is employed to speed up such excited-state simulations but also how this branch of artificial intelligence can be used to advance this exciting research field in all its aspects. Discussed applications of machine learning for excited states include excited-state dynamics simulations, static calculations of absorption spectra, as well as many others. In order to put these studies into context, we discuss the promises and pitfalls of the involved machine learning techniques. Since the latter are mostly based on quantum chemistry calculations, we also provide a short introduction into excited-state electronic structure methods and approaches for nonadiabatic dynamics simulations and describe tricks and problems when using them in machine learning for excited states of molecules.

Machine Learning for Electronically Excited States of Molecules

Electronically excited states of molecules are at the heart of photochemistry, photophysics, as well as photobiology and also play a role in material science. Their theoretical description requires highly accurate quantum chemical calculations, which are computationally expensive. In this review, we focus on not only how machine learning is employed to speed up such excited-state simulations but also how this branch of artificial intelligence can be used to advance this exciting research field in all its aspects. Discussed applications of machine learning for excited states include excited-state dynamics simulations, static calculations of absorption spectra, as well as many others. In order to put these studies into context, we discuss the promises and pitfalls of the involved machine learning techniques. Since the latter are mostly based on quantum chemistry calculations, we also provide a short introduction into excited-state electronic structure methods and approaches for nonadiabatic dynamics simulations and describe tricks and problems when using them in machine learning for excited states of molecules.

Modern quantum chemistry with [Open]Molcas

MOLCAS/OpenMolcas is an ab initio electronic structure program providing a large set of computational methods from Hartree-Fock and density functional theory to various implementations of multiconfigurational theory. This article provides a comprehensive overview of the main features of the code, specifically reviewing the use of the code in previously reported chemical applications as well as more recent applications including the calculation of magnetic properties from optimized density matrix renormalization group wave functions.

Quantum yields of singlet and triplet chemiexcitation of dimethyl 1,2-dioxetane: ab initio nonadiabatic molecular dynamic simulations

The global nonadiabatic switching on-the-fly trajectory surface hopping simulation at the 8SA-CASSCF quantum level has been performed to estimate the quantum yield of chemiexcitation for the uncatalyzed decomposition reaction of the open-shell biradical trans-3,4-dimethyl-1,2-dioxetane system. The present ab initio nonadiabatic molecular dynamic simulation involving both conical intersection and intersystem crossing is to compute for the first time the population evolution of quantum yields at the four lowest singlet and four lowest triplet states. The simulated results demonstrate not only the stepwise dissociation of O-O and C-C bond breaking, but also confirm the existence of a biradical entropic trap which is responsible for chemiexcitation. The simulated quantum yield of the triplet chemiexcitation ΦT1 = 0.266 ± 0.096 agrees with the experimental value of 0.20 ± 0.04 very well. The present nonadiabatic molecular dynamic simulation of dimethyl 1,2-dioxetanes provides a further advanced understanding and stepping stone for future studies on chemi- and bio-luminescence.

Thermochemiluminescent peroxide crystals

Chemiluminescence, a process of transduction of energy stored within chemical bonds of ground-state reactants into light via high-energy excited intermediates, is known in solution, but has remained undetected in macroscopic crystalline solids. By detecting thermally induced chemiluminescence from centimeter-size crystals of an organic peroxide here we demonstrate direct transduction of heat into light by thermochemiluminescence of bulk crystals. Heating of crystals of lophine hydroperoxide to ~115 °C results in detectable emission of blue-green light with maximum at 530 nm with low chemiluminescent quantum yield [(2.1 ± 0.1) × 10^‒7 E mol^‒1]. Spectral comparison of the thermochemiluminescence in the solid state and in solution revealed that the solid-state thermochemiluminescence of lophine peroxide is due to emission from deprotonated lophine. With selected 1,2-dioxetane, endoperoxide and aroyl peroxide we also establish that the thermochemiluminescence is common for crystalline peroxides, with the color of the emitted light varying from blue to green to red.

Chemiluminescence is known in solution, but has remained undetected in macroscopic crystalline solids so far. Here the authors demonstrate direct transduction of heat into light by thermochemiluminescence in a centimeter-size lophine hydroperoxide crystal.

How machine learning can assist the interpretation of ab initio molecular dynamics simulations and conceptual understanding of chemistry

Machine learning models, trained to reproduce molecular dynamics results, help interpreting simulations and extracting new understanding of chemistry.

Molecular dynamics simulations are often key to the understanding of the mechanism, rate and yield of chemical reactions. One current challenge is the in-depth analysis of the large amount of data produced by the simulations, in order to produce valuable insight and general trends. In the present study, we propose to employ recent machine learning analysis tools to extract relevant information from simulation data without a priori knowledge on chemical reactions. This is demonstrated by training machine learning models to predict directly a specific outcome quantity of ab initio molecular dynamics simulations – the timescale of the decomposition of 1,2-dioxetane. The machine learning models accurately reproduce the dissociation time of the compound. Keeping the aim of gaining physical insight, it is demonstrated that, in order to make accurate predictions, the models evidence empirical rules that are, today, part of the common chemical knowledge. This opens the way for conceptual breakthroughs in chemistry where machine analysis would provide a source of inspiration to humans.

Chemi- and Bioluminescence of Cyclic Peroxides

Bioluminescence is a phenomenon that has fascinated mankind for centuries. Today the phenomenon and its sibling, chemiluminescence, have impacted society with a number of useful applications in fields like analytical chemistry and medicine, just to mention two. In this review, a molecular-orbital perspective is adopted to explain the chemistry behind chemiexcitation in both chemi- and bioluminescence. First, the uncatalyzed thermal dissociation of 1,2-dioxetane is presented and analyzed to explain, for example, the preference for triplet excited product states and increased yield with larger nonreactive substituents. The catalyzed fragmentation reaction and related details are then exemplified with substituted 1,2-dioxetanone species. In particular, the preference for singlet excited product states in that case is explained. The review also examines the diversity of specific solutions both in Nature and in artificial systems and the difficulties in identifying the emitting species and unraveling the color modulation process. The related subject of excited-state chemistry without light absorption is finally discussed. The content of this review should be an inspiration to human design of new molecular systems expressing unique light-emitting properties. An appendix describing the state-of-the-art experimental and theoretical methods used to study the phenomena serves as a complement.