Since the initial landmark study on the chiral induced spin selectivity (CISS) effect in 1999, considerable experimental and theoretical efforts have been made to understand the physical underpinnings and mechanistic features of this interesting phenomenon. As first formulated, the CISS effect refers to the innate ability of chiral materials to act as spin filters for electron transport; however, more recent experiments demonstrate that displacement currents arising from charge polarization of chiral molecules lead to spin polarization without the need for net charge flow. With its identification of a fundamental connection between chiral symmetry and electron spin in molecules and materials, CISS promises profound and ubiquitous implications for existing technologies and new approaches to answering age old questions, such as the homochiral nature of life. This review begins with a discussion of the different methods for measuring CISS and then provides a comprehensive overview of molecules and materials known to exhibit CISS-based phenomena before proceeding to identify structure–property relations and to delineate the leading theoretical models for the CISS effect. Next, it identifies some implications of CISS in physics, chemistry, and biology. The discussion ends with a critical assessment of the CISS field and some comments on its future outlook.
Despite its simplicity and strong theoretical guarantees, adiabatic state preparation has received considerably less interest than variational approaches for the preparation of low-energy electronic structure states. Two major reasons for this are the large number of gates required for Trotterising time-dependent electronic structure Hamiltonians, as well as discretisation errors heating the state. We show that a recently proposed randomized algorithm, which implements exact adiabatic evolution without heating and with far fewer gates than Trotterisation, can overcome this problem. We develop three methods for measuring the energy of the prepared state in an efficient and noise-resilient manner, yielding chemically accurate results on a 4-qubit molecule in the presence of realistic gate noise, without the need for error mitigation. These findings suggest that adiabatic approaches to state preparation could play a key role in quantum chemistry simulations both in the era of noisy as well as error-corrected quantum computers.
Christian Guzman Ruiz, Mario Acosta, Oriol Jorba
et al.
Earth system models (ESM) demand significant hardware resources and energy consumption to solve atmospheric chemistry processes. Recent studies have shown improved performance from running these models on GPU accelerators. Nonetheless, there is room for improvement in exploiting even more GPU resources. This study proposes an optimized distribution of the chemical solver's computational load on the GPU, named Block-cells. Additionally, we evaluate different configurations for distributing the computational load in an NVIDIA GPU. We use the linear solver from the Chemistry Across Multiple Phases (CAMP) framework as our test bed. An intermediate-complexity chemical mechanism under typical atmospheric conditions is used. Results demonstrate a 35x speedup compared to the single-CPU thread reference case. Even using the full resources of the node (40 physical cores) on the reference case, the Block-cells version outperforms them by 50%. The Block-cells approach shows promise in alleviating the computational burden of chemical solvers on GPU architectures.
Supernova remnants (SNRs) exert strong influence on the physics and chemistry of the nearby molecular clouds (MCs) through shock waves and the cosmic rays (CRs) they accelerate. To investigate the SNR-cloud interaction in the prototype interacting SNR W28 (G6.4$-$0.1), we present new observations of $\rm HCO^+$, HCN and HNC $J=1\text{--}0$ lines, supplemented by archival data of CO isotopes, $\rm N_2H^+$ and $\rm H^{13}CO^+$. We compare the spatial distribution and spectral line profiles of different molecular species. Using local thermodynatic equilibrium (LTE) assumption, we obtain an abundance ratio $N({\rm HCO^+})/N({\rm CO})\sim10^{-4}$ in the northeastern shocked cloud, which is higher by an order of magnitude than the values in unshocked clouds. This can be accounted for by the chemistry jointly induced by shock and CRs, with the physical parameters previously obtained from observations: preshock density $n_{\rm H}\sim 2\times 10^{5}\rm \ cm^{-3}$, CR ionization rate $ζ=2.5\times 10^{-15} \rm \ s^{-1}$ and shock velocity $V_{\rm s}=15\text{--}20\rm \ km\ s^{-1}$. Towards a point outside the northeastern boundary of W28 with known high CR ionization rate, we estimate the abundance ratio $ N({\rm HCO^+})/N({\rm N_2H^+}) \approx 0.6\text{--}3.3$, which can be reproduced by a chemical simulation if a high density $n_{\rm H}\sim 2\times 10^5 \ \rm cm^{-3}$ is adopted.
Scientific discovery plays a pivotal role in advancing human society, and recent progress in large language models (LLMs) suggests their potential to accelerate this process. However, it remains unclear whether LLMs can autonomously generate novel and valid hypotheses in chemistry. In this work, we investigate whether LLMs can discover high-quality chemistry hypotheses given only a research background-comprising a question and/or a survey-without restriction on the domain of the question. We begin with the observation that hypothesis discovery is a seemingly intractable task. To address this, we propose a formal mathematical decomposition grounded in a fundamental assumption: that most chemistry hypotheses can be composed from a research background and a set of inspirations. This decomposition leads to three practical subtasks-retrieving inspirations, composing hypotheses with inspirations, and ranking hypotheses - which together constitute a sufficient set of subtasks for the overall scientific discovery task. We further develop an agentic LLM framework, MOOSE-Chem, that is a direct implementation of this mathematical decomposition. To evaluate this framework, we construct a benchmark of 51 high-impact chemistry papers published and online after January 2024, each manually annotated by PhD chemists with background, inspirations, and hypothesis. The framework is able to rediscover many hypotheses with high similarity to the groundtruth, successfully capturing the core innovations-while ensuring no data contamination since it uses an LLM with knowledge cutoff date prior to 2024. Finally, based on LLM's surprisingly high accuracy on inspiration retrieval, a task with inherently out-of-distribution nature, we propose a bold assumption: that LLMs may already encode latent scientific knowledge associations not yet recognized by humans.
ConspectusThe ever-growing energy crisis and the deteriorated environment caused by carbon energy consumption motivate the exploitation of alternative green and sustainable energy supplies. Because of the unique advantages of zero-emission, no moving parts, accurate temperature control, a long steady-state operation period, and the ability to operate in extreme situations, thermoelectrics, enabling the direct conversion between heat and electricity, is a promising and sustainable option for power generation and refrigeration. However, with increasing application potentials, thermoelectrics is now facing a major challenge: developing high-performance, Pb-free, and low-toxic thermoelectric materials and devices.As one group of promising candidates, GeTe derivatives have the potential to replace the widely used thermoelectric materials containing highly toxic elements. In this Account, we summarize our recent progress in developing high-performance GeTe-based thermoelectric materials via exploring innovative strategies to enhance electron transports and dampen phonon propagations. First, we fundamentally illustrate the underlying chemistry and physical reason for an intrinsically high carrier concentration in GeTe, which enormously restrains the thermoelectric performance of GeTe. From our theoretical calculations, the formation energy of Ge vacancy is the lowest among the defects in GeTe, energetically favoring Ge vacancies in the lattice and leading to intrinsically high carrier concentrations. Accordingly, aliovalent doping/alloying is proposed to increase the formation energy of Ge vacancies and decrease the carrier concentration to the optimal level. We then outline the newly developed method to refine the band structures of GeTe with tuned electronic transport. On the basis of the molecular orbital theory, the energy offset between two valence band edges at the L and Σ points in GeTe should be ascribed to the slightly different Ge_4s orbital characters at these two points, which guides the screening of dopants for band convergence. Besides, the Rashba spin splitting is explored to increase the band degeneracy of GeTe. Afterward, we analyze the dampened phonon propagation in GeTe to minimize its lattice thermal conductivity. Alloying with the heavy Sb atoms can shift the optical phonon modes toward low frequency and reinforce the interaction of optical and acoustic phonon modes so that the inherent phonon scattering is enhanced. In addition, planar vacancies and superlattice precipitates can significantly strengthen phonon scattering to result in ultralow lattice thermal conductivity. After that, we overview the finite elemental analysis simulations to optimize the device geometry for maximizing the device performance and introduce the as-developed prototype GeTe-based thermoelectric device. In the end, we point out future directions in the development of GeTe for device applications. The strategies summarized in this Account can serve as references for developing wide materials with enhanced thermoelectric performance.
Species transport models typically combine partial differential equations (PDEs) with relations from hindered transport theory to quantify electromigrative, convective, and diffusive transport through complex nanoporous systems; however, these formulations are frequently substantial simplifications of the governing dynamics, leading to the poor generalization performance of PDE-based models. Given the growing interest in deep learning methods for the physical sciences, we develop a machine learning-based approach to characterize ion transport across nanoporous membranes. Our proposed framework centers around attention-enhanced neural differential equations that incorporate electroneutrality-based inductive biases to improve generalization performance relative to conventional PDE-based methods. In addition, we study the role of the attention mechanism in illuminating physically-meaningful ion-pairing relationships across diverse mixture compositions. Further, we investigate the importance of pre-training on simulated data from PDE-based models, as well as the performance benefits from hard vs. soft inductive biases. Our results indicate that physics-informed deep learning solutions can outperform their classical PDE-based counterparts and provide promising avenues for modelling complex transport phenomena across diverse applications.
R. Schürmann, Alessandro Nagel, S. Juergensen
et al.
Surface-enhanced Raman scattering (SERS) is an effective and widely used technique to study chemical reactions induced or catalyzed by plasmonic substrates, since the experimental setup allows us to trigger and track the reaction simultaneously and identify the products. However, on substrates with plasmonic hotspots, the total signal mainly originates from these nanoscopic volumes with high reactivity and the information about the overall consumption remains obscure in SERS measurements. This has important implications; for example, the apparent reaction order in SERS measurements does not correlate with the real reaction order, whereas the apparent reaction rates are proportional to the real reaction rates as demonstrated by finite-difference time-domain (FDTD) simulations. We determined the electric field enhancement distribution of a gold nanoparticle (AuNP) monolayer and calculated the SERS intensities in light-driven reactions in an adsorbed self-assembled molecular monolayer on the AuNP surface. Accordingly, even if a high conversion is observed in SERS due to the high reactivity in the hotspots, most of the adsorbed molecules on the AuNP surface remain unreacted. The theoretical findings are compared with the hot-electron-induced dehalogenation of 4-bromothiophenol, indicating a time dependency of the hot-carrier concentration in plasmon-mediated reactions. To fit the kinetics of plasmon-mediated reactions in plasmonic hotspots, fractal-like kinetics are well suited to account for the inhomogeneity of reactive sites on the substrates, whereas also modified standard kinetics model allows equally well fits. The outcomes of this study are on the one hand essential to derive a mechanistic understanding of reactions on plasmonic substrates by SERS measurements and on the other hand to drive plasmonic reactions with high local precision and facilitate the engineering of chemistry on a nanoscale.
The chemical elements are the “conserved principles” or “kernels” of chemistry that are retained when substances are altered. Comprehensive overviews of the chemistry of the elements and their compounds are needed in chemical science. To this end, a graphical display of the chemical properties of the elements, in the form of a Periodic Table, is the helpful tool. Such tables have been designed with the aim of either classifying real chemical substances or emphasizing formal and aesthetic concepts. Simplified, artistic, or economic tables are relevant to educational and cultural fields, while practicing chemists profit more from “chemical tables of chemical elements.” Such tables should incorporate four aspects: (i) typical valence electron configurations of bonded atoms in chemical compounds (instead of the common but chemically atypical ground states of free atoms in physical vacuum); (ii) at least three basic chemical properties (valence number, size, and energy of the valence shells), their joint variation across the elements showing principal and secondary periodicity; (iii) elements in which the (sp)8, (d)10, and (f)14 valence shells become closed and inert under ambient chemical conditions, thereby determining the “fix-points” of chemical periodicity; (iv) peculiar elements at the top and at the bottom of the Periodic Table. While it is essential that Periodic Tables display important trends in element chemistry we need to keep our eyes open for unexpected chemical behavior in ambient, near ambient, or unusual conditions. The combination of experimental data and theoretical insight supports a more nuanced understanding of complex periodic trends and non-periodic phenomena.
Uniaxial cold pressing was used to fabricate GaAs0,9Bi0,1 targets with 10% Bi content. Thin films of GaAs1-x-yNxBiy onto a GaAs (100) substrate were obtained from the formed GaAs0,9Bi0,1 target by pulsed laser deposition in an argon-nitrogen gas atmosphere, and their structure and composition were studied. It is shown that on the surface of the film there are predominantly small microdroplets with a diameter of less than 0,5 μm, formed by Bi atoms. Large microdroplets with a diameter of 2 to 6 μm consist partly of Bi and Ga. No microdroplets formed only from Ga were found. It is noted that small Ga microdroplets are adsorbed on the surface of large Bi microdroplets without forming a GaBi alloy. It was also found that the formation of Bi microdroplets also occurs due to the segregation of Bi atoms on the film surface. The energy-dispersive spectroscopy data make it possible to characterize the resulting thin films as GaAs0,995N0,015Bi0,03. The mean square roughness of the film surface was 12,2 nm. The resulting GaAs0,995N0,015Bi0,03 film has a polycrystalline structure. An analysis of the X-ray diffraction data showed that the film grew according to the Volmer-Weber law, when islands are nucleated and their sizes subsequently increase. The nuclei are most likely formed by GaAs, GaN, GaAsN, GaAsBi, and GaAsNBi. The calculated full width at half height for GaAs0,995N0,015Bi0,03 was –0,8656ʺ, and the average crystallite size was 1,6 nm.
The goal of this work was to develop an electrochemiluminescence (ECL) immunosensor based on the luminol-O2 technology for measuring serum insulin levels in athletes. For construction of the sandwich-configuration electrochemiluminescence immunoassay, the Au and CeO2 nanocomposite electrodeposited on functionalized CNTs (Au@CeO2/f-CNTs) and combined with luminol (Lu/Au@CeO2/f-CNTs), and the nanocomposite of dopamine@SiO2 (DA@SiO2) used as quencher of ECL signal. The primary antibody (Ab1) and secondary antibody (Ab2) modified the composite surface of DA@SiO2 and Au@CeO2/f-CNTs, respectively for enhancement of sensitivity of immunosensor. SEM and XRD analyses confirmed successful synthesis of DA@SiO2 and Au@CeO2/f-CNTs nanocomposites. The optimal condition were investigated for achievement of maximum ECL signal, and results showed under the optimal experimental conditions, immunosensor showed high sensitivity and anti-interference capability to determination of insulin, and the detection limit and linear range of immunosensor were obtained 17 ng/ml and 10-4 ng/ml to 50 ng/ml, respectively. The precision and applicability of the proposed ECL immunosensor to determination of insulin were investigated in blood serum specimens of athletes as real samples, and results indicated to the acceptable recovery (100.60 to 94.85%) and RSD (3.11 to 1.84 %) values, and verification of feasibility of the proposed ECL immunosensor for determination of serum insulin level in athletes.
Industrial electrochemistry, Physical and theoretical chemistry
We aim to investigate how the molecular chemistry in stripped-envelope supernovae (SESNe) affect physical conditions and optical spectra, and produce ro-vibrational emission in the mid-infrared (MIR). We also aim to assess the diagnostic potential of observations of such MIR emission with JWST. We coupled a chemical kinetic network including carbon, oxygen, silicon, and sulfur-bearing molecules into the nonlocal thermal equilibrium (NLTE) spectral synthesis code SUMO. We let four species - CO, SiO, SiS, and SO - participate in NLTE cooling of the gas to achieve self-consistency between the molecule formation and the temperature. We applied the new framework to model the spectrum of a Type Ic SN in the 100-600d time range. Molecules are predicted to form in SESN ejecta in significant quantities (typical mass $10^{-3}$ $M_\odot$) throughout the 100-600d interval. The impact on the temperature and optical emission depends on the density of the oxygen zones and varies with epoch. For example, the [O I] 6300, 6364 feature can be quenched by molecules from 200 to 450d depending on density. The MIR predictions show strong emission in the fundamental bands of CO, SiO, and SiS, and in the CO and SiO overtones. Type Ibc SN ejecta have a rich chemistry and considering the effect of molecules is important for modeling the temperature and atomic emission in the nebular phase. Observations of SESNe with JWST hold promise to provide the first detections of SiS and SO, and to give information on zone masses and densities of the ejecta. Combined optical, near-infrared, and MIR observations can break degeneracies and achieve a more complete picture of the nucleosynthesis, chemistry, and origin of Type Ibc SNe.
Post‐Li ion battery technologies are gaining importance due to their high theoretical energy density and high specific capacity of the electrode materials. Due to fundamental limitations, the existing Li‐ion batteries cannot fulfill rigorous requirements, like cost‐effectiveness and high storage capacities. Room‐temperature sodium‐sulfur battery (RT‐Na/S), in particular, is an emerging candidate with the high theoretical specific capacity of sodium (~1166 mAh/g) and sulfur (~1675 mAh/g) and naturally high abundance of both the electrode materials. Sodium metal, combined with sulfur, is a cheap and energy‐dense option to the existing battery technologies. In recent years, this has garnered much interest in the scientific community due to a wide range of possibilities for altering battery performance. With the invention of the high‐temperature sodium‐sulfur batteries, Na metal‐based chemistries remain in oblivion. However, due to increasing concerns over the safety of high‐temperature sodium‐sulfur batteries, Na metal anode is revived in recent years with the ever‐growing demands for high energy density and improved safety. Despite that current Na metal anode still lacks high‐reversibility, efficiency, and room‐temperature stability due to limited or no control over the interfacial chemistry of the Na metal anode. The electrochemical reduction of Na+ ions is accompanied by the inevitable reduction of organic species, which leads to the growth of the solid‐electrolyte interphase (SEI) with Na‐deposits. The SEI is inherently unstable due to the localized fluctuations in its chemical and physical properties. A deep understanding of challenges associated with the SEI's localized interfacial chemistry is of prime importance toward developing practical Na metal anodes for RT‐Na/S batteries. This minireview highlights critical challenges in developing a stable Na metal anode and further sheds light on its mechanistic aspects. In addition to that, novel approaches to precisely tune the interphase's physicochemical properties are highlighted to pave path for developing a stable and long‐life Na‐metal anode for RT‐Na/S batteries.
AbstractIn this study, density functional theory (DFT) studies were performed to evaluate the response of the BODIPY derivative (BNDP−) as a gas sensor toward CO2, COS, SO2, SO3, H2S, and NH3. The sensing ability of the BNDP−interacting with the gases was described in terms of geometric, electronic, and optical properties. Results showed that the strong interactions between BNDP−and the gases were ascribed to the short linkage distance and the large variation of geometries. The AIM analysis was used to shed light on the sensor mechanisms of the BNDP−toward the gases. The spectroscopic properties, UV‐vis absorption and fluorescence emission spectra, revealed the behavior of the interaction of BNDP−with these gases. These parameters, including the compositions of the frontier molecular orbitals and the values of orbital energy gaps, indicated that the BNDP−had good sensitivity toward CO2, COS, SO2, and SO3.
Silicon fertilizer and biochar have been widely used to remediate soil contaminated by heavy metals. The effects and mechanism of silicon fertilizer and biochar addition on the heavy metal availability, soil biological properties, and microbial community characteristics need further study in soils contaminated by heavy metals. Therefore, this research determined how silicon fertilizer, biochar, and their combined using affected microbial communities related with nitrogen and phosphorus cycling. The abundance and composition of the microbial community were evaluated by quantitative PCR and phospholipid fatty acid analysis, respectively. Results showed that silicon fertilizer and biochar addition significantly changed soil properties, including pH, total organic carbon, ammonium, nitrate. The Cd and Zn speciation were significantly reduced by silicon fertilizer, biochar, and their integrated application. Microbial community abundance and structure were also significantly changed. Principal component analysis shows that the difference in soil microbial community structure is the most obvious under the combined addition of biochar, silicon fertilizer and biochar. In addition, the results of fluorescence quantitative PCR showed that with biological addition, the number of soil bacteria was significantly reduced. This study reveals the influence of silicon fertilizer and biochar on bacterial and fungal communities in heavy metal soils and the effect of soil heavy metal availability.
Alhazmi Hassan A., Ahsan Waquar, Mangla Bharti
et al.
Graphene, owing to its unique chemical structure and extraordinary chemical, electrical, thermal, optical, and mechanical properties, has opened up a new vista of applications, specifically as novel sensing platforms. The last decade has seen an extensive exploration of graphene and graphene-based materials either alone or modified with nanoparticles and polymers for the fabrication of nanoscale biosensors. These biosensors displayed excellent conductivity, high sensitivity, and selectivity, good accuracy, and precision, rapid detection with low detection limits as well as long-term stability. The unmatched properties of graphene and graphene-based materials have been applied for the detection of a number of chemical and biological molecules successfully for the diagnosis of a variety of diseases, pathogens, and biomarkers of the diseases. This review is aimed to cover the fabrication methods, functionalization techniques, and biomedical applications along with the recent advancements in the field of development of graphene-based biosensors. Recent clinical trials and patents as well as market trends and opportunities associated with graphene-based biosensors are also summarized. The application of graphene-based biosensors in the detection of SARS-CoV-2 causing COVID-19 is also reviewed.