Ring currents modulate optoelectronic properties of aromatic chromophores at 25 T

The Avoidance of Purine Stretches by Cancer Mutations

Purine stretches, sequences of adenine (A) and guanine (G) in DNA, play critical roles in binding regulatory protein factors and influence gene expression by affecting DNA folding. This study investigates the relationship between purine stretches and cancer development, considering the aromaticity of purines, quantified by methods like Hückel's rule and NICS calculations, and the importance of the flanking sequence context. A pronounced avoidance of long purine stretches by typical cancer mutations was observed in public data on the intergenic regions of cancer patients, suggesting a role of intergenic sequences in chromatin reorganization and gene regulation. A statistically significant shortening of purine stretches in cancerous tumors (p value < 0.0001) was found. The insights into the aromatic nature of purines and their stacking energies explain the role of purine stretches in DNA structure, contributing to their role in cancer progression. This research lays the groundwork for understanding the nature of purine stretches, emphasizing their importance in gene regulation and chromatin restructuring, and offers potential avenues for novel cancer therapies and insights into cancer etiology.

Making Peace with Random Phases: Ab Initio Conical Intersection Quantum Dynamics in Random Gauges

Ab initio modeling of conical intersection wave packet dynamics is crucial for various photochemical, photophysical, and biological processes. However, adiabatic electronic states obtained from electronic structure computations involve random phases, or more generally, random gauge fixings, which cannot be directly used in the modeling of nonadiabatic wave packet simulations. Here we develop a random-gauge local diabatic representation that allows an exact modeling of conical intersection dynamics directly using the adiabatic electronic states with phases randomly assigned during the electronic structure computations. Its utility is demonstrated by an exact ab initio modeling of the two-dimensional Shin-Metiu model with and without an external magnetic field. Our results provide a simple approach to integrating the electronic structure computations into nonadiabatic quantum dynamics, thus paving the way for ab initio modeling of conical intersection dynamics.

Direct Probe of Conical Intersection Photochemistry by Time-Resolved X-ray Magnetic Circular Dichroism

The direct probing of photochemical dynamics by detecting the electronic coherence generated during passage through conical intersections is an intriguing challenge. The weak coherence signal and the difficulty in preparing purely excited wave packets that exclude coherence from other sources make it experimentally challenging. We propose to use time-resolved X-ray magnetic circular dichroism to probe the wave packet dynamics around the conical intersection. The magnetic field amplifies the relative strength of the electronic coherence signal compared to populations through the magnetic field response anisotropy. More importantly, since the excited state relaxation through conical intersections involves a change of parity, the magnetic coupling matches the symmetry of the response function with the electronic coherence, making the coherence signal only sensitive to the conical intersection induced coherence and excludes the pump pulse induced coherence between the ground state and excited state. In this theoretical study, we apply this technique to the photodissociation dynamics of a pyrrole molecule and demonstrate its capability of probing electronic coherence at a conical intersection as well as population transfer. We demonstrate that a magnetic field can be effectively used to extract novel information about electron and nuclear molecular dynamics.

Molecular Pseudorotation in Phthalocyanines as a Tool for Magnetic Field Control at the Nanoscale

Metal phthalocyanines, a highly versatile class of aromatic, planar, macrocyclic molecules with a chelated central metal ion, are topical objects of ongoing research and particularly interesting due to their magnetic properties. However, while the current focus lies almost exclusively on spin-Zeeman-related effects, the high symmetry of the molecule and its circular shape suggests the exploitation of light-induced excitation of 2-fold degenerate vibrational states in order to generate, switch, and manipulate magnetic fields at the nanoscale. The underlying mechanism is a molecular pseudorotation that can be triggered by infrared pulses and gives rise to a quantized, small, but controllable magnetic dipole moment. We investigate the optical stimulation of vibrationally induced molecular magnetism and estimate changes in the magnetic shielding constants for confirmation by future experiments.

Microstructure-driven annihilation effects and dispersive excited state dynamics in solid-state films of a model sensitizer for photon energy up-conversion applications

Bimolecular processes involving exciton spin-state interactions gain attention for their deployment as wavelength-shifting tools. Particularly triplet-triplet annihilation induced photon energy up-conversion (TTA-UC) holds promise to enhance the performance of solar cell and photodetection technologies. Despite the progress noted, a correlation between the solid-state microstructure of photoactuating TTA-UC organic composites and their photophysical properties is missing. This lack of knowledge impedes the effective integration of functional TTA-UC interlayers as ancillary components in operating devices. We here investigate a solution-processed model green-to-blue TTA-UC binary composite. Solid-state films of a 9,10 diphenyl anthracene (DPA) blue-emitting activator blended with a (2,3,7,8,12,13,17,18-octaethyl-porphyrinato) PtII (PtOEP) green-absorbing sensitizer are prepared with a range of compositions and examined by a set of complementary characterization techniques. Grazing incidence X-ray diffractometry (GIXRD) measurements identify three PtOEP composition regions wherein the DPA:PtOEP composite microstructure varies due to changes in the packing motifs of the DPA and PtOEP phases. In Region 1 (≤2 wt%) DPA is semicrystalline and PtOEP is amorphous, in Region 2 (between 2 and 10 wt%) both DPA and PtOEP phases are amorphous, and in Region 3 (≥10 wt%) DPA remains amorphous and PtOEP is semicrystalline. GIXRD further reveals the metastable DPA-β polymorph species as the dominant DPA phase in Region 1. Composition dependent UV-vis and FT-IR measurements identify physical PtOEP dimers, irrespective of the structural order in the PtOEP phase. Time-gated photoluminescence (PL) spectroscopy and scanning electron microscopy imaging confirm the presence of PtOEP aggregates, even after dispersing DPA:PtOEP in amorphous poly(styrene). When arrested in Regions 1 and 2, DPA:PtOEP exhibits delayed PtOEP fluorescence at 580 nm that follows a power-law decay on the ns time scale. The origin of PtOEP delayed fluorescence is unraveled by temperature- and fluence-dependent PL experiments. Triplet PtOEP excitations undergo dispersive diffusion and enable TTA reactions that activate the first singlet-excited (S1) PtOEP state. The effect is reproduced when PtOEP is mixed with a poly(fluorene-2-octyl) (PFO) derivative. Transient absorption measurements on PFO:PtOEP films find that selective PtOEP photoexcitation activates the S1 of PFO within ∼100 fs through an up-converted 3(d, d*) PtII-centered state.

Approaches to monitor ATP levels in living cells: where do we stand?

ATP is the most universal and essential energy molecule in cells. This is due to its ability to store cellular energy in form of high-energy phosphate bonds, which are extremely stable and readily usable by the cell. This energy is key for a variety of biological functions such as cell growth and division, metabolism, and signaling, and for the turnover of biomolecules. Understanding how ATP is produced and hydrolyzed with a spatiotemporal resolution is necessary to understand its functions both in physiological and in pathological contexts. In this review, first we will describe the organization of the electron transport chain and ATP synthase, the main molecular motor for ATP production in mitochondria. Second, we will review the biochemical assays currently available to estimate ATP quantities in cells, and we will compare their readouts, strengths, and weaknesses. Finally, we will explore the palette of genetically encoded biosensors designed for microscopy-based approaches, and show how their spatiotemporal resolution opened up the possibility to follow ATP levels in living cells.

Harnessing deep reinforcement learning to construct time-dependent optimal fields for quantum control dynamics

We present an efficient deep reinforcement learning (DRL) approach to automatically construct time-dependent optimal control fields that enable desired transitions in dynamical chemical systems. Our DRL approach gives impressive performance in constructing optimal control fields, even for cases that are difficult to converge with existing gradient-based approaches. We provide a detailed description of the algorithms and hyperparameters as well as performance metrics for our DRL-based approach. Our results demonstrate that DRL can be employed as an effective artificial intelligence approach to efficiently and autonomously design control fields in quantum dynamical chemical systems.

Coherent ring-current migration of Mg-phthalocyanine probed by time-resolved X-ray circular dichroism

The coherent ring current of Mg-phthalocyanine created by a broad band UV-visible pump pulse shows variation with time, where the ring currents at the corner benzene rings, around the Mg cation and on the outer ring oscillate with different time periods and the current density migrates among these regions. The 7 pairs of E_u degenerate excited states populated upon photoexcitation, generate 21 distinct coherent ring currents. We further calculate the time-resolved X-ray circular dichroism (TRXCD) spectrum of the coherences contributing to the ring current obtained by an attosecond X-ray probe pulse resonant with the nitrogen K-edge. A frequency domain TRXCD signal obtained by a Fourier transform of the signal with respect to the pump-probe delay time clearly separates the currents induced by different state pairs.

The coherent ring current of Mg-phthalocyanine are created by a broad band UV-visible pump pulse and migrate into different regions within the molecule. This coherent ring current dynamics is probed by time-resolved X-ray circular dichroism.

Role of the Exchange Interactions in the Stability of the Cellulose

The problem of the origin of biochirality and the related problem of the initial monomers' selection are still under discussion, and the main point here is not the mechanics of...

Modification of the Properties of Polymer Composites in a Constant Magnetic Field Environment

In this paper, polymer composites based on polylactide (PLA) and epoxy resin (Epidian 5) were studied in terms of the influence of magnetic induction on their changes in physicochemical properties. The composites contained admixtures in the form of magnetite (Fe₃O₄) and crystalline cellulose (Avicel PH-1010) in the amount of 10%, 20%, and 30% by weight and starch in the amount of 10%. The admixtures of cellulose and starch were intended to result in the composites becoming biodegradable biopolymers to some extent. Changes in physical and chemical properties due to the impact of a constant magnetic field with a magnetic induction value B = 0.5 T were observed. The changes were observed during tests of tensile strength, bending, impact strength, water absorbency, frost resistance, chemical resistance to acids and bases, as well as through SEM microscopy and with studies of the composition of the composites that use the EDS method and of their structure with the XRD method. Based on the obtained results, it was found that the magnetic induction value changes the properties of composites. This therefore acts as one method of receiving new alternative materials, the degradation of which in the environment would take far less time.

Magnetic-Field-Induced Modulation of Charge-Recombination Dynamics in a Rosarin-Fullerene Complex

Charge-recombination processes are critical for photovoltaic applications and should be suppressed for efficient charge transport. Here, we report that an applied magnetic field (0-1 T) can be used control the charge-recombination dynamics in an expanded rosarin-C₆₀ complex. In the low magnetic field regime (<100 mT), the charge-recombination rate slows down due to hyperfine coupling, as inferred from transient absorption spectroscopic analyses. In contrast, in the high field regime, i.e., over 500 mT, the charge-recombination rate recovers and increases because the Δg mechanism facilitates spin conversion to a triplet charge-separated state (S to T₀ ) that undergoes rapid charge-recombination to a localized rosarin triplet state. Therefore, we highlight the charge-recombination rate and the localized triplet state population can be modulated by the magnetic field in charge donor/acceptor non-covalent complexes.

A Volumetric Analysis of the 1H NMR Chemical Shielding in Supramolecular Systems

The liquid state NMR chemical shift of protons is a parameter frequently used to characterize host-guest complexes. Its theoretical counterpart, that is, the 1H NMR chemical shielding affected by the solvent (1H CS), may provide important insights into spatial arrangements of supramolecular systems, and it can also be reliably obtained for challenging cases of an aggregation of aromatic and antiaromatic molecules in solution. This computational analysis is performed for the complex of coronene and an antiaromatic model compound in acetonitrile by employing the GIAO-B3LYP-PCM approach combined with a saturated basis set. Predicted 1H CS values are used to generate volumetric data, whose properties are thoroughly investigated. The 1H CS isosurface, corresponding to a value of the proton chemical shift taken from a previous experimental study, is described. The presence of the 1H CS isosurface should be taken into account in deriving structural information about supramolecular hosts and their encapsulation of small molecules.

Theoretical Study of Dynamic Stark-Induced π-Electron Rotations in Low-Symmetry Aromatic Ring Molecules beyond the Frozen Nuclear Approximation

The effects of vibrational motions on dynamic Stark-induced π-electron rotations in a low-symmetry aromatic ring molecule are theoretically studied in the adiabatic approximation. We adopt a simplified three-electronic state model with a few vibronic states. A pair of the lowest vibronic states in two electronic excited states is set degenerate by irradiation of two linearly polarized UV lasers. The resultant degenerate state is named the dynamic Stark-induced degenerate vibronic state (DSIDVS). The laser parameters (intensities and central frequencies) are determined under the conditions of DSIDVS formation. The aromatic ring molecules of interest are supposed to belong to the weak coupling case. The analytical expressions for the DSIDVS and coherent angular momentum L_Z(t) are derived in the displaced harmonic oscillator (DHO) model. Two horizontal potential displacements (δ_α, δ_β) between the two electronic excited states (α and β) and the ground state are the parameters in the DHO model. The L_Z(t) calculated with δ_α = δ_β is characterized by a regular sequence of the angular momentum pulses with a positive (or negative) constant. For a more general case with δ_α ≠ δ_β, the regular sequence is broken down because of the contribution of the first excited vibronic state in each electronic state to L_Z(t).

Parametrizing the Spatial Dependence of 1H NMR Chemical Shifts in π-Stacked Molecular Fragments

Most recently a renewed interest in several areas has arisen in factors governing the 1H NMR chemical shift (1H CS) of protons in aromatic systems. Therefore, it is important to describe how 1H CS values are affected by π-stacking intermolecular interactions. The parametrization of radial and angular dependences of the 1H CS is proposed, which is based on conventional gauge-independent atomic orbital (GIAO) calculations of explicit molecular fragments. Such a parametrization is exemplified for a benzene dimer with intermonomer vertical and horizontal distances which are in the range of values often found in crystals of organic compounds. Results obtained by the GIAO calculations combined with B3LYP and MP2 methods were compared, and revealed qualitatively the same trends in the 1H CS data. The parametrization was found to be quantitatively correct for the T-shaped benzene dimers, and its limitations were discussed. Parametrized 1H CS surfaces should become useful for providing additional restraints in the search of site-specific information through an analysis of structurally induced 1H CS changes.

Harnessing deep neural networks to solve inverse problems in quantum dynamics: machine-learned predictions of time-dependent optimal control fields

Inverse problems continue to garner immense interest in the physical sciences, particularly in the context of controlling desired phenomena in non-equilibrium systems. In this work, we utilize a series of deep neural networks for predicting time-dependent optimal control fields, E(t), that enable desired electronic transitions in reduced-dimensional quantum dynamical systems. To solve this inverse problem, we investigated two independent machine learning approaches: (1) a feedforward neural network for predicting the frequency and amplitude content of the power spectrum in the frequency domain (i.e., the Fourier transform of E(t)), and (2) a cross-correlation neural network approach for directly predicting E(t) in the time domain. Both of these machine learning methods give complementary approaches for probing the underlying quantum dynamics and also exhibit impressive performance in accurately predicting both the frequency and strength of the optimal control field. We provide detailed architectures and hyperparameters for these deep neural networks as well as performance metrics for each of our machine-learned models. From these results, we show that machine learning, particularly deep neural networks, can be employed as cost-effective statistical approaches for designing electromagnetic fields to enable desired transitions in these quantum dynamical systems.