Ensemble Level Research Articles

Chiral Inorganic Nanomaterials Characterized by Advanced TEM: A Qualitative and Quantitative Study.

Chiral inorganic nanomaterials (CINMs) have garnered significant interest due to their exceptional optical, electronic, and catalytic properties, offering promising advancements in energy conversion, data storage, catalysis, and biomedicine. While traditional optical spectrophotometers reveal the chiroptical performance of CINMs on an ensemble level, the direct structural visualization for the qualitative and quantitative discernment of their chiral features has become increasingly distinct with the advancements of transmission electron microscopy (TEM) techniques. The need for reasonable and high-standard discrimination requirements of CINMs has driven the progress of chirality-based TEM technologies. Therefore, this review in the good season takes the initiative to summarize the current advancements in TEM technologies for CINMs characterization, emphasizing a qualitative analysis of chiral atomic-level features, 0D, 1D, and 2D nanocrystals, and assembled nanomaterials. Then, the quantitative methods for determining chirality is also highlighted, such as 3D electron tomography, and further address the evolution of chiral structures monitored by the Ex-situ and In-situ TEM technologies. By providing a roadmap for the current challenges and proposing future advancements in TEM technologies for the qualitative, quantitative, and real-time analysis of CINMs, it can drive innovations in the field of chiral nanomaterials as well as the development of TEM technologies.

Stochastic thermodynamics of Brownian motion in a flowing fluid.

We study stochastic thermodynamics of overdamped Brownian motion in a flowing fluid. Unlike some previous papers, we treat the effects of the flow field as a nonconservational driving force acting on the Brownian particle. This allows us to apply the theoretical formalism developed in a recent paper for general nonconservative Langevin dynamics. We define heat and work both at the trajectory level and at the ensemble level, and prove the second law of thermodynamics explicitly. The entropy production is decomposed into a housekeeping part and an excess part, both of which are non-negative at the ensemble level. Fluctuation theorems are derived for the housekeeping work, the excess work, and the total work, which are further verified using numerical simulations. A comparison between our theory and an earlier theory by Speck etal. [Phys. Rev. Lett. 100, 178302 (2008)0031-900710.1103/PhysRevLett.100.178302] is also carried out.

Fluorescence-Detected Two-Dimensional Electronic Spectroscopy of a Single Molecule.

Single-molecule fluorescence spectroscopy is a powerful method that avoids ensemble averaging, but its temporal resolution is limited by the fluorescence lifetime to nanoseconds at most. At the ensemble level, two-dimensional spectroscopy provides insight into ultrafast femtosecond processes, such as energy transfer and line broadening, even beyond the Fourier limit, by correlating pump and probe spectra. Here, we combine these two techniques and demonstrate coherent 2D spectroscopy of individual dibenzoterrylene (DBT) molecules at room temperature. We excite the molecule in a confocal microscope with a phase-modulated train of femtosecond pulses and detect the emitted fluorescence with single-photon counting detectors. Using a phase-sensitive detection scheme, we were able to measure the nonlinear 2D spectra of most of the DBT molecules that we studied. Our method is applicable to a wide range of single emitters and opens new avenues for understanding energy transfer in single quantum objects on ultrafast time scales.

Detecting Measurement-Induced Entanglement Transitions with Unitary Mirror Circuits.

Monitored random circuits, consisting of alternating layers of entangling two-qubit gates and projective single-qubit measurements applied to some fraction p of the qubits, have been a topic of recent interest. In particular, the resulting steady state exhibits a phase transition from highly correlated states with "volume-law" entanglement at p<p_{c} to localized states with "area-law" entanglement at p>p_{c}. It is hard to access this transition experimentally, as it cannot be seen at the ensemble level. Naively, to observe it one must repeat the experiment until the set of measurement results repeats itself, with likelihood that is exponentially small in the number of measurements. To overcome this issue, we present a hybrid quantum-classical algorithm which creates a matrix product state (MPS) based "unitary mirror" of the projected circuit. Polynomial-sized tensor networks can represent quantum states with area-law entanglement, and so the unitary mirror can well approximate the experimental state above p_{c} but fails exponentially below it. The breaking of this mirror can thus pinpoint the critical point. We outline the algorithm and how such results would be obtained. We present a bound on the maximum entanglement entropy of any given state that is well represented by an MPS, and from the bound suggest how the volume-law phase can be bounded. We consider whether the entanglement could similarly be bounded from below where the MPS fails. Finally, we present numerical results for small qubit numbers and for monitored circuits with random Clifford gates.

Ensemble learning evaluation of mechanical property for mining waste cemented backfill

Processing the large amount of waste rock generated from mining into backfill materials enables the resource utilization of waste materials. Additionally, the mechanical properties of backfill materials influence the stability of overlying strata. Therefore, this study established a large-scale dataset of coal gangue-based and tailing-based backfill materials. Three novel ensemble learning models were developed to evaluate the nonlinear effects of 29 dimensional factors on the mechanical properties of backfill material. Additionally, factors like material type, preparation method, and measurement error can cause discrepancies in mechanical properties, affecting evaluation accuracy. Consequently, this paper constructed SVR fusion models using three ensemble learning methods to enhance robustness on differentiated data. It investigated the impacts of ensemble methods, ensemble levels, and perturbation levels on the model. The study found that the Bagging-ensembled SVR performed optimally, with R2, MAE, and RMSE of 0.951, 0.369, and 0.560, respectively, exhibiting better robustness on perturbed datasets. This study contributes to resource utilization in mining waste.

Temporal information in the anterior cingulate cortex relates to accumulated experiences

Anterior cingulate cortex (ACC) activity is important for operations that require the ability to integrate multiple experiences over time, such as rule learning, cognitive flexibility, working memory, and long-term memory recall. To shed light on this, we analyzed neuronal activity while rats repeated the same behaviors during hour-long sessions to investigate how activity changed over time. We recorded neuronal ensembles as rats performed a decision-free operant task with varying reward likelihoods at three different response ports (n= 5). Neuronal state space analysis revealed that each repetition of a behavior was distinct, with more recent behaviors more similar than those further apart in time. ACC activity was dominated by a slow, gradual change in low-dimensional representations of neural state space aligning with the pace of behavior. Temporal progression, or drift, was apparent on the top principal component for every session and was driven by the accumulation of experiences and not an internal clock. Notably, these signals were consistent across subjects, allowing us to accurately predict trial numbers based on a model trained on data from a different animal. We observed that non-continuous ramping firing rates over extended durations (tens of minutes) drove the low-dimensional ensemble representations. 40% of ACC neurons' firing ramped over a range of trial lengths and combinations of shorter duration ramping neurons created ensembles that tracked longer durations. These findings provide valuable insights into how the ACC, at an ensemble level, conveys temporal information by reflecting the accumulation of experiences over extended periods.

Predicting sales and cross-border e-commerce supply chain management using artificial neural networks and the Capuchin search algorithm

E-commerce provides a large selection of goods for sale and purchase, which promotes regular transactions and commodity flows. Efficient distribution of goods and precise estimation of customer wants are essential for cost reduction. In order to improve supply chain efficiency in the context of cross-border e-commerce, this article combines machine learning approaches with the Internet of Things. The suggested approach consists of two main stages. Order prediction is done in the first step to determine how many orders each merchant is expected to get in the future. In the second phase, allocation operations are conducted and resources required for each retailer are supplied depending on their needs and inventory, taking into account each store’s inventory as well as the anticipated sales level. This suggested approach makes use of a weighted mixture of neural networks to anticipate sales orders. The Capuchin Search Algorithm (CapSA) is used in this weighted combination to concurrently enhance the learning and ensemble performance of models. This indicates that an effort is made to reduce the local error of the learning model at the model level via model weight adjustments and neural network configuration. To guarantee more accurate output from the ensemble model, the best weight for each individual component is found at the ensemble model level using the CapSA method. This method yields the ensemble model’s final output in the form of weighted averages by choosing suitable weight values. With a Root Mean Squared Error of 2.27, the suggested technique has successfully predicted sales based on the acquired findings, showing a minimum decrease of 2.4 in comparison to the comparing methodologies. Additionally, the suggested method’s strong performance is shown by the fact that it was able to minimize the Mean Absolute Percentage Error by 14.67 when compared to other comparison approaches.

Hyperspectral Detection of the Fluorescence Shift between Chirality-Sorted Empty and Water-Filled Single-Wall Carbon Nanotube Enantiomers.

Single-wall carbon nanotubes (SWCNTs) have extraordinary electronic and optical properties that depend strongly on their exact chiral structure and their interaction with their inner and outer environment. The fluorescence (PL) of semiconducting SWCNTs, for instance, will shift depending on the molecules with which the SWCNT's hollow core is filled. These interaction-induced shifts are challenging to resolve on the ensemble level in samples containing a mixture of different filling contents due to the relatively large inhomogeneous line width of the ensemble SWCNT PL compared to the size of these shifts. To circumvent this inhomogeneous broadening, single-tube spectroscopy and hyperspectral imaging are often applied, which until now required time-consuming statistical studies. Here, we present hyperspectral PL microscopy combined with automated SWCNT segmenting based on either principal component analysis or a convolutional neural network, capable of both spatially and spectrally resolving the PL along the length of many individual SWCNTs at the same time and automatically fitting peak positions and line widths of individual SWCNTs. The methodology is demonstrated by accurately determining the emission shifts and line widths of thousands of left- and right-handed empty and water-filled SWCNTs coated with a chiral surfactant, resulting in four statistical distributions which cannot be resolved in ensemble spectroscopy of unsorted samples. The results demonstrate a robust method to quickly probe ensemble properties with single-enantiomer spectral resolution. Moreover, it promises to be an absolute quantitative method to characterize the relative abundances of SWCNTs with different handedness or filling content in macroscopic samples, simply by counting individual species.

Detecting m6A at single-molecular resolution via direct RNA sequencing and realistic training data.

Direct RNA sequencing offers the possibility to simultaneously identify canonical bases and epi-transcriptomic modifications in each single RNA molecule. Thus far, the development of computational methods has been hampered by the lack of biologically realistic training data that carries modification labels at molecular resolution. Here, we report on the synthesis of such samples and the development of a bespoke algorithm, mAFiA (m6A Finding Algorithm), that accurately detects single m6A nucleotides in both synthetic RNAs and natural mRNA on single read level. Our approach uncovers distinct modification patterns in single molecules that would appear identical at the ensemble level. Compared to existing methods, mAFiA also demonstrates improved accuracy in measuring site-level m6A stoichiometry in biological samples.

Electronic Coherences Excited by an Ultra Short Pulse Are Robust with Respect to Averaging over Randomly Oriented Molecules as Shown by Singular Value Decomposition.

We report a methodology for averaging quantum photoexcitation vibronic dynamics over the initial orientations of the molecules with respect to an ultrashort light pulse. We use singular value decomposition of the ensemble density matrix of the excited molecules, which allows the identification of the few dominant principal molecular orientations with respect to the polarization direction of the electric field. The principal orientations provide insights into the specific stereodynamics of the corresponding principal molecular vibronic states. The massive compaction of the vibronic density matrix of the ensemble of randomly oriented pumped molecules enables a most efficient fully quantum mechanical time propagation scheme. Two examples are discussed for the quantum dynamics of the LiH molecule in the manifolds of its electronically excited Σ and Π states. Our results show that electronic and vibrational coherences between excited states of the same symmetry are resilient to averaging over an ensemble of molecular orientations and can be selectively excited at the ensemble level by tuning the pulse parameters.

Prismatic Confinement Induces Tunable Orientation in Plasmonic Supercrystals.

Throughout history scientists have looked to Nature for inspiration and attempted to replicate intricate complex structures formed by self-assembly. In the context of synthetic supercrystals, achieving such complexity remains a challenge due to the highly symmetric nature of most nanoparticles (NPs). Previous works have shown intricate coupling between the self-assembly of NPs and confinement in templates, such as emulsion droplets (spherical confinement) or tubes (cylindrical confinement). This study focuses on the interplay between anisotropic NP shape and tunable "prismatic confinement" leading to the self-assembly of supercrystals in cavities featuring polygonal cross sections. A multiscale characterization strategy is employed to investigate the orientation and structure of the supercrystals locally and at the ensemble level. Our findings highlight the role of the mold interface in guiding the growth of distinct crystal domains: each side of the mold directs the formation of a monodomain that extends until it encounters another, leading to the creation of grain boundaries. Computer simulations in smaller prismatic cavities were conducted to predict the effect of an increased confinement. Comparison between prismatic and cylindrical confinements shows that flat interfaces are key to orienting the growth of supercrystals. This work shows a method of inducing orientation in plasmonic supercrystals and controlling their textural defects, thus offering insight into the design of functional metasurfaces and hierarchically structured devices.

Stochastic thermodynamics of Brownian motion in temperature gradient

We study stochastic thermodynamics of a Brownian particle which is subjected to a temperature gradient and is confined by an external potential. We first formulate an over-damped Ito-Langevin theory in terms of local temperature, friction coefficient, and steady state distribution, all of which are experimentally measurable. We then study the associated stochastic thermodynamics theory. We analyze the excess entropy production both at trajectory level and at ensemble level, and derive the Clausius inequality as well as the transient fluctuation theorem (FT). We also use molecular dynamics to simulate a Brownian particle inside a Lennard-Jones fluid and verify the FT. Remarkably we find that the FT remains valid even in the under-damped regime. We explain the possible mechanism underlying this surprising result.

Multimodal probing of T-cell recognition with hexapod heterostructures.

Studies using antigen-presenting systems at the single-cell and ensemble levels can provide complementary insights into T-cell signaling and activation. Although crucial for advancing basic immunology and immunotherapy, there is a notable absence of synthetic material toolkits that examine T cells at both levels, and especially those capable of single-molecule-level manipulation. Here we devise a biomimetic antigen-presenting system (bAPS) for single-cell stimulation and ensemble modulation of T-cell recognition. Our bAPS uses hexapod heterostructures composed of a submicrometer cubic hematite core (α-Fe2O3) and nanostructured silica branches with diverse surface modifications. At single-molecule resolution, we show T-cell activation by a single agonist peptide-loaded major histocompatibility complex; distinct T-cell receptor (TCR) responses to structurally similar peptides that differ by only one amino acid; and the superior antigen recognition sensitivity of TCRs compared with that of chimeric antigen receptors (CARs). We also demonstrate how the magnetic field-induced rotation of hexapods amplifies the immune responses in suspended T and CAR-T cells. In addition, we establish our bAPS as a precise and scalable method for identifying stimulatory antigen-specific TCRs at the single-cell level. Thus, our multimodal bAPS represents a unique biointerface tool for investigating T-cell recognition, signaling and function.

Unraveling the Chirality Transfer from Circularly Polarized Light to Single Plasmonic Nanoparticles.

Due to their broken symmetry, chiral plasmonic nanostructures have unique optical properties and numerous applications. However, there is still a lack of comprehension regarding how chirality transfer occurs between circularly polarized light (CPL) and these structures. Here, we thoroughly investigate the plasmon-assisted growth of chiral nanoparticles from achiral Au nanocubes (AuNCs) via CPL without the involvement of any chiral molecule stimulators. We identify the structural chirality of our synthesized chiral plasmonic nanostructures using circular differential scattering (CDS) spectroscopy, which is correlated with scanning electron microscopy imaging at both the single-particle and ensemble levels. Theoretical simulations, including hot-electron surface maps, reveal that the plasmon-induced chirality transfer is mediated by the asymmetric distribution of hot electrons on achiral AuNCs under CPL excitation. Furthermore, we shed light on how this plasmon-induced chirality transfer can also be utilized for chiral growth in bimetallic systems, such as Ag or Pd on AuNCs. The results presented here uncover fundamental aspects of chiral light-matter interaction and have implications for the future design and optimization of chiral sensors and chiral catalysis, among others.

Unraveling the Chirality Transfer from Circularly Polarized Light to Single Plasmonic Nanoparticles

AbstractDue to their broken symmetry, chiral plasmonic nanostructures have unique optical properties and numerous applications. However, there is still a lack of comprehension regarding how chirality transfer occurs between circularly polarized light (CPL) and these structures. Here, we thoroughly investigate the plasmon‐assisted growth of chiral nanoparticles from achiral Au nanocubes (AuNCs) via CPL without the involvement of any chiral molecule stimulators. We identify the structural chirality of our synthesized chiral plasmonic nanostructures using circular differential scattering (CDS) spectroscopy, which is correlated with scanning electron microscopy imaging at both the single‐particle and ensemble levels. Theoretical simulations, including hot‐electron surface maps, reveal that the plasmon‐induced chirality transfer is mediated by the asymmetric distribution of hot electrons on achiral AuNCs under CPL excitation. Furthermore, we shed light on how this plasmon‐induced chirality transfer can also be utilized for chiral growth in bimetallic systems, such as Ag or Pd on AuNCs. The results presented here uncover fundamental aspects of chiral light‐matter interaction and have implications for the future design and optimization of chiral sensors and chiral catalysis, among others.

Reaction-Initiated Single-Molecule Tracking of Mass Transfer in Core-Shell Mesoporous Silica Particles.

Understanding the host-guest interactions in porous materials is of great importance in the field of separation science. Probing it at the single-molecule level uncovers the inter- and intraparticle inhomogeneity and establishes structure-property relationships for guiding the design of porous materials for better separation performance. In this work, we investigated the dynamics of host-guest interactions in core-shell mesoporous silica particles under in situ conditions by using a fluorogenic reaction-initiated single-molecule tracking (riSMT) approach. Taking advantage of the low fluorescence background, three-dimensional (3D) tracking of the dynamics of the molecules inside the mesoporous silica pore was achieved with high spatial precision. Compared to the commonly used two-dimensional (2D) tracking method, the 3D tracking results show that the diffusion coefficients of the molecules are three times larger on average. Using riSMT, we quantitatively analyzed the mass transfer of probe molecules in the mesoporous silica pore, including the fraction of adsorption versus diffusion, diffusion coefficients, and residence time. Large interparticle inhomogeneity was revealed and is expected to contribute to the peak broadening for separation application at the ensemble level. We further investigated the impact of electrostatic interaction on the mass transfer of molecules in the mesoporous silica pore and discovered that the primary effect is on the fraction rather than their diffusion rates of resorufin molecules undergoing diffusion.

Regulation of chromatin architecture by transcription factor binding.

Transcription factors (TF) bind to chromatin and regulate the expression of genes. The pair Myc:Max binds to E-box regulatory DNA elements throughout the genome to control the transcription of a large group of specific genes. We introduce an implicit modeling protocol for Myc:Max binding to mesoscale chromatin fibers at nucleosome resolution to determine TF effect on chromatin architecture and shed light into its mechanism of gene regulation. We first bind Myc:Max to different chromatin locations and show how it can direct fiber folding and formation of microdomains, and how this depends on the linker DNA length. Second, by simulating increasing concentrations of Myc:Max binding to fibers that differ in the DNA linker length, linker histone density, and acetylation levels, we assess the interplay between Myc:Max and other chromatin internal parameters. Third, we study the mechanism of gene silencing by Myc:Max binding to the Eed gene loci. Overall, our results show how chromatin architecture can be regulated by TF binding. The position of TF binding dictates the formation of microdomains that appear visible only at the ensemble level. At the same time, the level of linker histone and tail acetylation, or different linker DNA lengths, regulates the concentration-dependent effect of TF binding. Furthermore, we show how TF binding can repress gene expression by increasing fiber folding motifs that help compact and occlude the promoter region. Importantly, this effect can be reversed by increasing linker histone density. Overall, these results shed light on the epigenetic control of the genome dictated by TF binding.

Regulation of chromatin architecture by transcription factor binding

Transcription factors (TF) bind to chromatin and regulate the expression of genes. The pair Myc:Max binds to E-box regulatory DNA elements throughout the genome to control the transcription of a large group of specific genes. We introduce an implicit modeling protocol for Myc:Max binding to mesoscale chromatin fibers at nucleosome resolution to determine TF effect on chromatin architecture and shed light into its mechanism of gene regulation. We first bind Myc:Max to different chromatin locations and show how it can direct fiber folding and formation of microdomains, and how this depends on the linker DNA length. Second, by simulating increasing concentrations of Myc:Max binding to fibers that differ in the DNA linker length, linker histone density, and acetylation levels, we assess the interplay between Myc:Max and other chromatin internal parameters. Third, we study the mechanism of gene silencing by Myc:Max binding to the Eed gene loci. Overall, our results show how chromatin architecture can be regulated by TF binding. The position of TF binding dictates the formation of microdomains that appear visible only at the ensemble level. At the same time, the level of linker histone and tail acetylation, or different linker DNA lengths, regulates the concentration-dependent effect of TF binding. Furthermore, we show how TF binding can repress gene expression by increasing fiber folding motifs that help compact and occlude the promoter region. Importantly, this effect can be reversed by increasing linker histone density. Overall, these results shed light on the epigenetic control of the genome dictated by TF binding.

Optimization of the Operation Reliability Level of the Protection System.

Ensuring the proper reliability level for the protection system is achieved by reserving the protection function. An excessive enhance of the reservation level leads to decreasing of the system reliability, due to the more situations in which protection tempestuously operates. The paper presents some considerations referring to definition of some indices regarding the protection system security. There is approached the problem of determination of the multiplicity level for each of the component protections, so that the optimization of the ensemble security level to be ensured. A more accurate analysis could be achieved by considering the corresponding phenomena as Markov processes.

Non-equilibrium attractor for non-linear stochastic dynamics

We study the dynamical behaviour of mesoscopic systems in contact with a thermal bath, described either via a non-linear Langevin equation at the trajectory level —or the corresponding Fokker-Planck equation for the probability distribution function at the ensemble level. Our focus is put on one-dimensional —or d-dimensional isotropic— systems in confining potentials, with detailed balance —fluctuation-dissipation thus holds, and the stationary probability distribution has the canonical form at the bath temperature. When quenching the bath temperature to low enough values, a far-from-equilibrium state emerges that rules the dynamics over a characteristic intermediate timescale. Such a long-lived state has a Dirac-delta probability distribution function and attracts all solutions over this intermediate timescale, in which the initial conditions are immaterial while the influence of the bath is still negligible. Numerical evidence and qualitative physical arguments suggest that the above picture extends to higher-dimensional systems, with anisotropy and interactions.