Browse Articles

Enhancing breast Cancer immunotherapy using gold nanoparticles carrying tumor antigens

Polarization-selective unidirectional and bidirectional diffractive neural networks for information security and sharing

Identification of causal plasma metabolite biomarkers for ischemic stroke using Mendelian randomization and mediation analysis

Yoked surface codes

Abstract One of the biggest obstacles to building a large scale quantum computer is the high qubit cost of protecting quantum information. For two-dimensional architectures, the surface code has long been the leading candidate quantum memory, but can require upwards of a thousand physical qubits per logical qubit to reach algorithmically-relevant logical error rates. In this work, we introduce a hierarchical memory formed from surface codes concatenated into high-density parity check codes. These yoked surface codes are arrayed in a rectangular grid, with parity checks (yokes) measured along each row, and optionally along each column, using lattice surgery. Our construction assumes no additional connectivity beyond a nearest-neighbor square qubit grid operating at a physical error rate of 10−3. At algorithmically-relevant logical error rates, yoked surface codes use as few as one-third the number of physical qubits per logical qubit as standard surface codes, enabling moderate-overhead fault-tolerant quantum memories in two dimensions.

Study on the effects of traffic noise and spring water sound at different sound pressure levels on brain dynamic activity

Elucidation of the biosynthetic pathway of hydroxysafflor yellow A

Azide–alkyne cycloaddition reactions over cobalt (0) nanoparticles supported on CaTiO3 perovskite

A Programmable Wafer-scale Chiroptical Heterostructure of Twisted Aligned Carbon Nanotubes and Phase Change Materials

Abstract The ability to design and dynamically control chiroptical responses in solid-state matter at a wafer scale enables new opportunities in various areas. Here, we present a full stack of computer-aided designs and experimental implementations of a dynamically programmable, unified, scalable chiroptical heterostructure containing wafer-scale twisted aligned one-dimensional carbon nanotubes and non-volatile phase change materials. We develop a software infrastructure based on high-performance machine learning frameworks, including differentiable programming and derivative-free optimization, to efficiently optimize the tunability of both reciprocal and nonreciprocal circular dichroism responses, which are experimentally validated. Further, we demonstrate the heterostructure scalability regarding stacking layers and the dual roles of aligned carbon nanotubes - the layer to produce chiroptical responses and the Joule heating electrode to electrically program phase change materials. This heterostructure platform is versatile and expandable to a library of one-dimensional nanomaterials, phase change materials, and electro-optic materials for exploring novel chiral phenomena and photonic and optoelectronic devices.

A centipede-inspired robot with passive terrain adaptation: optimized design and performance analysis

Dissociation and radiative stabilization of the indene cation: The nature of the C–H bond and astrochemical implications

Indene (C9H8) is the only polycyclic pure hydrocarbon identified in the interstellar medium to date, with an observed abundance orders of magnitude higher than predicted by astrochemical models. The dissociation and radiative stabilization of vibrationally hot indene cations are investigated by measuring the time-dependent neutral particle emission rate from ions in a cryogenic ion-beam storage ring for up to 100 ms. Time-resolved measurements of the kinetic energy released upon hydrogen atom loss from C9H8+, analyzed in view of a model of tunneling through a potential energy barrier, provide the dissociation rate coefficient. Master equation simulations of the dissociation in competition with vibrational and electronic radiative cooling reproduce the measured dissociation rate. We find that radiative stabilization arrests one of the main C9H8 destruction channels included in astrochemical models, helping to rationalize its high observed abundance.

Coulomb Field-Driven Desorption/Ionization by Femtosecond Laser for Mass Spectrometry Detection and Imaging

Se-mediated dry transfer of wafer-scale 2D semiconductors for advanced electronics

Neonatal factors impacting umbilical cord blood unit characteristics

Abstract A promising alternative to bone marrow in hematopoietic stem cell transplantation is umbilical cord blood (UCB). Major barrier to its use in transplantation is stem cell quantity and quality. It is crucial to determine the variables impacting the quality of these cells for bankability. The study aimed to investigate the impact of neonatal factors on UCB units. A total of 150 UCB units that were collected during the caesarean section were included in the study. The sex, birth order, gestational age, birth weight, chest circumference, head circumference, and Apgar score of the newborns were recorded after delivery. The cord blood volume was calculated. The numbers of CD34 + cells and total nucleated cells (TNCs) were determined. Univariate analysis revealed that larger babies, heavier placental weights, increased head and chest circumferences, and longer umbilical cords were associated with greater volumes of cord blood and higher CD34 + and TNC cell counts. A greater UCB volume and a higher CD34 + cell count was associated with a longer gestational duration. To determine the primary selection criteria and estimate the yield, a multivariate linear regression analysis was used. Heavier placentas had higher TNC and CD34 + cell counts and greater cord blood volumes. Larger babies gave UCB units with increased volume. Longer gestational-age newborns had a higher CD34 + cell count in their UCB unit. Our findings suggest that placental weight is the key predictive variable influencing the quantity and quality of UCB units, which is essential for successful cord blood transplantation and bankability.

Revisiting crosslinking density effects on pNIPAM microgel properties: Size, electrophoretic mobility, and transition temperatures

Poly(N-isopropylacrylamide) (pNIPAM) microgels exhibit a reversible thermoresponsive behavior, undergoing a volume phase transition. This property makes pNIPAM microgels highly appealing for diverse applications, including drug delivery, tissue engineering, and sensors, where temperature-triggered changes in size, charge, and mechanical properties are advantageous. However, a plethora of data available in the literature regarding the relationship between the crosslinking density and the above-mentioned properties of pNIPAM microgels necessitates a consolidation and re-examination. This study aims to address two key objectives: (1) elucidate the relationship between the crosslinking density and size/electrophoretic mobility of pNIPAM microgels, building upon existing knowledge, and (2) examine the influence of crosslinking density on transition temperatures, particularly the electrokinetic transition temperature, which is not well explored and understood. To achieve these objectives, we synthesized 20 batches of pNIPAM microgels using two distinct synthesis routes: 18 batches via conventional one-pot synthesis, with triplicate replicates for six crosslinking densities, and two batches of pNIPAM microgels via semi-batch synthesis, with a duplicate replicate for one crosslinking density. These microgels were characterized using a combination of dynamic light scattering to determine the size and thermoresponsive behavior, electrophoretic light scattering to analyze electrophoretic mobility, and atomic force microscopy to evaluate the structural morphology and assess stiffness. The insights from the characterization techniques enhance our understanding of how the crosslinking density influences the physical and electrokinetic properties of pNIPAM microgels, potentially creating a pathway for rational design of microgels tailored for specific applications.

Author Correction: Increased but not pristine soil organic carbon stocks in restored ecosystems

Enhancing corrosion resistance with chemically modified aluminum oxide in UV-curable coatings applied to steel surfaces

Abstract This study introduces a novel, environmentally sustainable epoxidized soybean oil acrylate (ESOA) nanocomposite coating containing nAl2O3-silane nanoparticles (ESOA@TMPTA-nAl2O3-Silane), which was fabricated using ultraviolet (UV) curing technology. As far as we know, this is the first study to incorporate aluminum oxide nanoparticles (nAl2O3) modified through covalent bonding with a reactive diluent monomer, tripropylene glycol diacrylate (TPGDA), and a coupling agent to enhance their dispersibility and interaction within the polymer matrix. Comprehensive characterization techniques, including Fourier-transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), atomic force microscopy (AFM), X-ray diffraction (XRD), UV-spectroscopy, energy-dispersive X-ray spectroscopy (EDX), and transmission electron microscopy (TEM), confirmed the nanocomposite’s structural and polymer morphological enhancements. Electrochemical impedance spectroscopy (EIS) demonstrated a substantial increase in polarization resistance (R p), rising from 25.6 kΩ cm2 for the unmodified polymer to 288.7 kΩ cm2 upon the incorporation of (8 wt%) nAl2O3-Silane. In a similar vein, Potentiodynamic polarization (PDP) exhibited a significant decrease in corrosion current density (i corr), diminishing from 0.82 to 0.059 µA/cm2, thereby achieving an inhibition efficiency exceeding 99%. Additionally, the salt spray test data showed a considerable improvement in the rust degree from 3 to 8G under identical conditions. The data demonstrates the outstanding corrosion resistance characteristics that the nAl2O3-Silane nanoparticles provided when coupled with the steel substrate. This improvement is attributed to the excellent dispersion, excellent barrier properties, transparency of the resulting coatings and strong adhesion of nAl2O3-Silane dispersed in the polymer matrix.

A perspective marking 20 years of using permutationally invariant polynomials for molecular potentials

This Perspective is focused on permutationally invariant polynomials (PIPs). Since their introduction in 2004 and first use in developing a fully permutationally invariant potential for the highly fluxional cation CH5+, PIPs have found widespread use in developing machine learned potentials (MLPs) for isolated molecules, chemical reactions, clusters, condensed phase, and materials. More than 100 potentials have been reported using PIPs. The popularity of PIPs for MLPs stems from their fundamental property of being invariant with respect to permutations of like atoms; this is a fundamental property of potential energy surfaces. This is achieved using global descriptors and, thus, without using an atom-centered approach (which is manifestly fully permutationally invariant). PIPs have been used directly for linear regression fitting of electronic energies and gradients for complex energy landscapes to chemical reactions with numerous product channels. PIPs have also been used as inputs to neural network and Gaussian process regression methods and in many-body (atom-centered, water monomer, etc.) applications, notably for gold standard potentials for water. Here, we focus on the progress and usage of PIPs since 2018, when the last review of PIPs was done by our group.

Supramodal and cross-modal representations of working memory in higher-order cortex

Dimethyl fumarate abrogates hepatocellular carcinoma growth by inhibiting Nrf2/Bcl-xL axis and enhances sorafenib’s efficacy

Decay of quasibound states of multidimensional systems with a barrier: A semiclassical transfer matrix approach

We present a semiclassical method for calculating the positions and widths of tunneling resonances in multi-dimensional systems with a potential energy barrier. The treatment is applicable to arbitrary resonance states with no restrictions concerning anharmonicity, integrability, or resonance overlap. At energies below the barrier, the method is based on the choice of a particular surface that divides phase space into two regions, one including a potential energy well and the other including the barrier. Transfer matrices are constructed for each region from short, real-valued, classical trajectories confined to a single zone. Transitions between the regions are described by forming products of such matrices. These matrices are used to form the Green function for the system, and resonance positions and energies can be obtained, in principle, from its poles at complex energies. In practice, these resonance parameters are determined from simple formulas at real energies. The avoidance of general complex-valued trajectories in this approach greatly simplifies calculations. At energies above the barrier, we construct the transfer matrix for the well region from classical trajectories that travel to and from compound dividing surfaces. These combine surfaces at which these trajectories are classically reflected from the barrier with those at which they are classically transmitted across the barrier. Numerical results for model systems are presented.