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At least 235 records · Page 13

Self‐Stabilized Heterometallic Pair Sites for Selective Ethanol Dehydrogenation on Pt–Cr–Ag Alloy Catalysts

Self‐stabilized, heterometallic pair‐sites can enable fine‐tuning of catalytic functionality while also mitigating dynamic structural changes that degrade catalytic performance. This study demonstrates the development and characterization of trimetallic Pt x Cr x Ag 1‐2x ( x ≤ 0.1) alloys with active Pt–Cr pair‐ensembles for non‐oxidative ethanol dehydrogenation, leveraging predictions that favorable bonding stabilizes Pt–Cr pairs diluted in Ag. Operando X‐ray absorption spectroscopy confirms the preferential formation and stability of Pt–Cr pairings dispersed throughout the Ag matrix, and ambient‐pressure X‐ray photoelectron spectroscopy shows that Pt–Cr sites have significant activity for ethanol dehydrogenation, while suppressing reaction processes that deactivate binary Pt–Ag and Cr–Ag alloys. This work demonstrates that stabilizing heterometallic pair sites within trimetallic alloys provides a new avenue for designing catalysts with discrete active sites that are durable and highly selective.

36 MATERIALS SCIENCE↗

Super resolution for root imaging

Premise High‐resolution cameras are very helpful for plant phenotyping as their images enable tasks such as target vs. background discrimination and the measurement and analysis of fine above‐ground plant attributes. However, the acquisition of high‐resolution images of plant roots is more challenging than above‐ground data collection. An effective super‐resolution (SR) algorithm is therefore needed for overcoming the resolution limitations of sensors, reducing storage space requirements, and boosting the performance of subsequent analyses. Methods We propose an SR framework for enhancing images of plant roots using convolutional neural networks. We compare three alternatives for training the SR model: (i) training with non‐plant‐root images, (ii) training with plant‐root images, and (iii) pretraining the model with non‐plant‐root images and fine‐tuning with plant‐root images. The architectures of the SR models were based on two state‐of‐the‐art deep learning approaches: a fast SR convolutional neural network and an SR generative adversarial network. Results In our experiments, we observed that the SR models improved the quality of low‐resolution images of plant roots in an unseen data set in terms of the signal‐to‐noise ratio. We used a collection of publicly available data sets to demonstrate that the SR models outperform the basic bicubic interpolation, even when trained with non‐root data sets. Discussion The incorporation of a deep learning–based SR model in the imaging process enhances the quality of low‐resolution images of plant roots. We demonstrate that SR preprocessing boosts the performance of a machine learning system trained to separate plant roots from their background. Our segmentation experiments also show that high performance on this task can be achieved independently of the signal‐to‐noise ratio. We therefore conclude that the quality of the image enhancement depends on the desired application.

Ruiz‐Munoz, Jose F.↗

Development of inducible promoters for regulating gene expression in Clostridium tyrobutyricum for biobutanol production

Abstract Clostridium tyrobutyricumis an anaerobe known for its ability to produce short‐chain fatty acids, alcohols, and esters. We aimed to develop inducible promoters for fine‐tuning gene expression inC. tyrobutyricum. Synthetic inducible promoters were created by employing anEscherichia coli lacoperator to regulate the thiolase promoter (PCathl) fromClostridium acetobutylicum, with the best one (LacI‐Pto4s) showing a 5.86‐fold dynamic range with isopropyl β‐d‐thiogalactoside (IPTG) induction. A LT‐Pt7 system with a dynamic range of 11.6‐fold was then created by combining LacI‐Pto4s with a T7 expression system composing of RNA polymerase (T7RNAP) and Pt7lacpromoter. Furthermore, two inducible expression systems BgaR‐PbgaLA and BgaR‐PbgaLB with a dynamic range of ~40‐fold were developed by optimizing a lactose‐inducible expression system fromClostridium perfringenswith modified 5′ untranslated region (5′ UTR) and ribosome‐binding site (RBS). BgaR‐PbgaLB was then used to regulate the expressions of a bifunctional aldehyde/alcohol dehydrogenase encoded byadhE2 and butyryl‐CoA/acetate Co‐A transferase encoded bycat1 inC. tyrobutyricumwild type and Δcat1::adhE2, respectively, demonstrating its efficient inducible gene regulation. The regulatedcat1 expression also confirmed that the Cat1‐catalyzed reaction was responsible for acetate assimilation inC. tyrobutyricum. The inducible promoters offer new tools for tuning gene expression inC. tyrobutyricumfor industrial applications.

Biotechnology & Applied Microbiology↗

Poly(acrylic acid) Enabling the Synthesis of Highly Uniform Silica Nanoparticles of sub-100 nm

Robust synthesis of small colloidal silica (SiO 2 ) nanoparticles with narrow size distributions (<5%) is challenging because the possible involvement of continuous nucleation and growth of SiO 2 in the widely used Stöber process usually results in silica nanoparticles with large sizes and broad size distributions. Promoting heterogeneous nucleation and growth of silica is promising to tackle the challenges by in-situ forming transient colloidal seeds that provide nucleation surfaces for silica condensate. Here in this work, poly(acrylic acid) (PAA) is introduced to the Stöber synthesis solutions, which are usually composed of ethanol (containing a trace amount of water), tetraethyl orthosilicate (TEOS), and ammonia, to mediate the nucleation of TEOS hydrolysates and growth of SiO 2 nanoparticles. PAA reacts with ammonia to form soft PAA-NH 3 complex colloids that serve as transient seeds to facilitate heterogeneous nucleation and growth of SiO 2 nanoparticles after adding TEOS to the synthesis solution. The number of PAA-NH 3 complex colloidal particles, which strongly depends on the molar ratio of PAA and ammonia, determines the number of the resulting SiO 2 nanoparticles and thus the size of the SiO 2 nanoparticles correspondingly according to the supply of TEOS. Highly uniform SiO 2 nanoparticles with finely tuned diameters in the sub-100 region have been successfully synthesized, and the synthesis protocol is feasible to be scaled up while maintaining the high quality of SiO 2 nanoparticles.

heterogeneous nucleation and growth of nanoparticl↗

Surface Segregation Studies in Ternary Noble Metal Alloys: Comparing DFT and Machine Learning with Experimental Data

Abstract Surface segregation, whereby the surface composition of an alloy differs systematically from the bulk, has historically been hard to study, because it requires experimental and modeling methods that span alloy composition space. In this work, we study surface segregation in catalytically relevant noble and platinum‐group metal alloys with a focus on three ternary systems: AgAuCu, AuCuPd, and CuPdPt. We develop a data set of 2478 fcc slabs with those compositions including all three low‐index crystallographic orientations relaxed with Density Functional Theory using the PBEsol functional with D3 dispersion corrections. We fine‐tune a machine learning model on this data and use the model in a series of 1800 Monte Carlo simulations spanning ternary composition space for each surface orientation and ternary chemical system. The results of these simulations are validated against prior experimental surface segregation data collected using composition spread alloy films for AgAuCu and AuCuPd. Our findings reveal that simulations conducted using the (110) orientation most closely match experimentally observed surface segregation trends, and while predicted trends qualitatively match observation, biases in the PBEsol functional limit numeric accuracy. This study advances understanding of surface segregation and the utility of computational studies and highlights the need for further improvements in simulation accuracy.

Chemistry↗

Rational optimization of substituted α–MnO 2 cathode for aqueous zinc–ion battery

Density functional theory (DFT) is utilized to explore the effects of increasing concentrations of vanadium (V) and chromium (Cr) substitution on the discharge of α-MnO 2 cathode in hydrated zinc-ion batteries (ZIBs). During H + -intercalation and Zn 2+ -intercalation, Cr-substitution proves to be more effective than V-substitution in promoting discharge behaviors. Transitioning from Mn 0.875 Cr 0.125 O 2 , Mn 0.75 Cr 0.25 O 2 to Mn 0.625 Cr 0.375 O 2 is found to consistently enhance the discharge voltage, along with improved tunnel structure retention and volume expansion suppression. In comparison, the promoting effect of increasing V-substitution is relatively small at initial discharge stages and leads to degradation at later stages, primarily due to an increased concentration of unstable Mn 2+ ion. The superior effect of Cr-substitution is attributed to the unique atomic and electronic structures of substituted Cr 4+ and reduced Cr 3+ ions during discharge. These ions serve as active electron acceptors to limit the formation of Mn 3+ and Mn 2+ ions, and as anchors to stabilize the α-MnO 2 framework and intercalated H + /Zn 2+ ions, respectively. Finally, our study highlights fine-tuning through substitution to enhance the performance of α-MnO 2 -based cathode materials in ZIBs.

25 ENERGY STORAGE↗

Electronic structure of Li 1,2,3 +,0,– and nature of the bonding in Li 2,3 +,0,–

Abstract The current study of the small lithium molecules Li 2 +,0,− and Li 3 +,0,− focuses on the nature of the bonding in these molecules as well as their structures and energetics (bond energies, ionization energies, and electron affinities). Valence CASSCF (2s,2p) calculations incorporate nondynamical electron correlation in the calculations, while the corresponding multireference configuration interaction and coupled cluster calculations incorporate dynamical electron correlation. Treatment of nondynamical correlation is critical for properly describing the Li 2,3 +,0,− molecules as well as the Li − anion with dynamical correlation, in general, only fine‐tuning the predictions. All lithium molecules and ions are bound, with the Li 3 + and Li 2 + ions being the most strongly bound, followed by Li 3 − , Li 2 , Li 2 − and Li 3 . The minimum energy structures of Li 3 +,0,− are, respectively, an equilateral triangle, an isosceles triangle, and a linear structure. The results of SCGVB calculations are analyzed to obtain insights into the nature of the bonding in these molecules. An important finding of this work is that interstitial orbitals, a concept first put forward by McAdon and Goddard in 1985, play an essential role in the bonding of all lithium molecules considered here except for Li 2 . The interstitial orbitals found in the Li 3 +,0 molecules likely give rise to the non‐nuclear attractors/maxima observed in these molecules.

Chemistry↗

Classifying metal‐binding sites with neural networks

Abstract To advance our ability to predict impacts of the protein scaffold on catalysis, robust classification schemes to define features of proteins that will influence reactivity are needed. One of these features is a protein's metal‐binding ability, as metals are critical to catalytic conversion by metalloenzymes. As a step toward realizing this goal, we used convolutional neural networks (CNNs) to enable the classification of a metal cofactor binding pocket within a protein scaffold. CNNs enable images to be classified based on multiple levels of detail in the image, from edges and corners to entire objects, and can provide rapid classification. First, six CNN models were fine‐tuned to classify the 20 standard amino acids to choose a performant model for amino acid classification. This model was then trained in two parallel efforts: to classify a 2D image of the environment within a given radius of the central metal binding site, either an Fe ion or a [2Fe‐2S] cofactor, with the metal visible (effort 1) or the metal hidden (effort 2). We further used two sub‐classifications of the [2Fe‐2S] cofactor: (1) a standard [2Fe‐2S] cofactor and (2) a Rieske [2Fe‐2S] cofactor. The accuracy for the model correctly identifying all three defined features was >95%, despite our perception of the increased challenge of the metalloenzyme identification. This demonstrates that machine learning methodology to classify and distinguish similar metal‐binding sites, even in the absence of a visible cofactor, is indeed possible and offers an additional tool for metal‐binding site identification in proteins.

59 BASIC BIOLOGICAL SCIENCES↗

A Versatile Approach to Stabilize Liquid–Liquid Interfaces using Surfactant Self-Assembly

Stabilizing liquid–liquid interfaces, whether between miscible or immiscible liquids, is crucial for a wide range of applications, including energy storage, microreactors, and biomimetic structures. Here, in this study, a versatile approach for stabilizing the water-oil interface is presented using the morphological transitions that occur during the self-assembly of anionic, cationic, and nonionic surfactants mixed with fatty acid oils. The morphological transitions underlying this approach are characterized and extensively studied through small-angle X-ray scattering (SAXS), rheometry, and microscopy techniques. Dissipative particle dynamics (DPD) as a simulation tool is adopted to investigate these morphological transitions both in the equilibrium ternary system as well as in the dynamic condition of the water-oil interface. Such a versatile strategy holds promise for enhancing applications such as liquid-in-liquid 3D printing. Moreover, it has the potential to revolutionize a wide range of fields where stabilizing liquid–liquid interfaces not only offers unprecedented opportunities for fine-tuning nanostructural morphologies but also imparts interesting practical features to the resulting liquid shapes. These features include perfusion capabilities, self-healing, and porosity, which could have significant implications for various industries.

36 MATERIALS SCIENCE↗

Scalable Graphene Oxide Hollow Fiber Membranes for Dye Desalination Enabled by Multi‐Purpose Polyamine Functionalization

2D nanosheets such as graphene oxide (GO) can be stacked to construct membranes with fine-tuned nanochannels to achieve molecular sieving ability. These membranes are often thin to achieve high water permeance, but their fabrication with consistent nanostructures on a large scale presents an enormous challenge. Herein, GO-based hollow fiber membranes (HFMs) are developed for dye desalination by synergistically combining chemical etching to form in-plane nanopores (10–30 nm) to increase water permeance and polyamine functionalization to improve underwater stability and enable facile large-scale production using existing membrane manufacturing processes. HFM modules with areas of 88 cm 2 and GO layer thicknesses of ≈500 nm are fabricated, and they exhibited a stable dye water permeance of 75 L m −2 h −1 bar −1 , rejection of >99.5% for Direct red and Congo red, and Na 2 SO 4 /dye separation factor of 300–500, superior to state-of-the-art commercial membranes. Furthermore, the versatility of this approach is also demonstrated using different short polyamines and porous substrates. This study reveals a scalable way of designing 2D materials into high-performance robust membranes for practical applications.

amine functionalization↗

Layer-by-Layer-Processed Organic Solar Cells with 18.02% Efficiency Enabled by Regulating the Aggregation of Bottom Polymers

The fabrication of organic solar cells (OSCs) by a layer-by-layer (LBL) method has attracted growing attention in recent years. As already known, the pre-aggregates of conjugated polymers in solution have a profound impact on their microstructure morphology in films. Herein, by simply controlling the solution temperature and annealing processes, the pre-aggregation behavior of D18 polymer in solution can be fine-tuned and the microstructure of D18 bottom layer is well manipulated. The optimized D18 bottom layer can effectively regulate L8-BO upper-layer-forming suitable networks for efficient charge transportation. In addition, a vertical phase separation with a special D/D:A/A structure (P-i-N-type component distribution) is also formed. As a result, compared to the 16.43% power conversion efficiency (PCE) of the bulk heterojunction devices, such control enables bilayer OSC devices based on the polymer D18 and L8-BO to deliver an enhanced PCE of 18.02% with simultaneously improved short-circuit current density, open-circuit voltage, and fill factor. Importantly, it is also demonstrated in these results that the LBL deposition process utilizing the pre-aggregation of polymer and its fiber-network-forming ability is a very promising approach to improve charge dynamics, suppress carrier recombination, and fabricate highly efficient OSCs.

14 SOLAR ENERGY↗

Diversity in Photoprotection and Energy Balancing in Terrestrial and Aquatic Phototrophs

The evolution of oxygenic photosynthesis enabled organisms to use sunlight as an energy source, allowing them to colonize new niches. At the same time, life (as we know it) places severe constraints on photosynthesis. For example, the initial reactions of photosynthesis involve highly energetic intermediates that, if not controlled, can generate highly toxic side products (especially reactive oxygen species, ROS), that can damage other essential components of the organisms it powers. Photosynthesis must therefore be tightly regulated to balance the need for efficient energy conversion with the necessity of avoiding photodamage (Gust D, Kramer D, Moore A, Moore T, Vermaas W, Mater Res Bull 33:383–389, 2008). A related constraint on photosynthesis is the need to precisely balance how much energy is stored in ATP and NADPH to precisely meet biochemical demands. If this balancing does not occur, the system will fail, leading to photodamage (Kramer DM, Evans JR, Plant Physiol 155:70–78, 2011). Consideration of these requirements is essential for efforts to improve the efficiency of photosynthesis by introducing CO 2 concentrating mechanisms, altering metabolism or biosynthetic pathways to shunt energy to alternative products (Kramer DM, Evans JR, Plant Physiol 155:70–78, 2011). These balancing processes must be extremely robust to contend with the rapid and unpredictable fluctuations in environmental conditions and metabolic demands that occur in nature. A large body of work has come from model systems, especially terrestrial higher plants and the green alga Chlamydomonas reinhardtii, leading to a model for the regulation of light reactions that involves 1) sensing of the pH gradient component of the thylakoid proton motive force (pmf), and 2) the redox state of the plastoquinone- and stromal pools. Over the short term, these sensors trigger regulation of light capture by altering the activity of ATP synthase leading to adjustments in lumen pH, which fine tunes light capture through nonphotochemical quenching (NPQ) and control of electron flow by adjusting the rate of PQH 2 oxidation at the b 6 f complex. Simultaneously, this system controls the balance of ATP/NADPH by adjusting electron flux to linear and cyclic electron flow pathways to balance ATP/NADPH. This integrated “pmf paradigm” model explains much of the existing data on plants and green algae, but may not extend to other diverse organisms. This review considers how advances in our understanding of photosynthesis over the past 7–8 years, particularly in the discoveries of diverse biochemical/biophysical mechanisms in aquatic photosynthetic species, affects the view of energy balance, including the shunting of electrons to O 2 through the flavodiiron proteins (FLV), the plastid terminal oxidase, the dissipation of electric field by ion movements, and the activation of alternative electron sinks. Furthermore, we will introduce the basic model that has been developed for higher plant chloroplasts, then contrast these with selected aquatic systems, focusing on how the differences impact the needs to re-balance both energy input and its partitioning into energy currencies.

Kanazawa, Atsuko↗

Discreteness and integrality in Conformal Field Theory

Various observables in compact CFTs are required to obey positivity, discreteness, and integrality. Positivity forms the crux of the conformal bootstrap, but understanding of the abstract implications of discreteness and integrality for the space of CFTs is lacking. We systematically study these constraints in two-dimensional, non-holomorphic CFTs, making use of two main mathematical results. First, we prove a theorem constraining the behavior near the cusp of integral, vector-valued modular functions. Second, we explicitly construct non-factorizable, non-holomorphic cuspidal functions satisfying discreteness and integrality, and prove the non-existence of such functions once positivity is added. Application of these results yields several bootstrap-type bounds on OPE data of both rational and irrational CFTs, including some powerful bounds for theories with conformal manifolds, as well as insights into questions of spectral determinacy. We prove that in rational CFT, the spectrum of operator twists t ≥ c/12 is uniquely determined by its complement. Likewise, we argue that in generic CFTs, the spectrum of operator dimensions Δ > c–1/12 is uniquely determined by its complement, absent fine-tuning in a sense we articulate. Finally, we discuss implications for black hole physics and the (non-)uniqueness of a possible ensemble interpretation of AdS 3 gravity.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

The scalar chemical potential in cosmological collider physics

Non-analyticity in co-moving momenta within the non-Gaussian bispectrum is a distinctive sign of on-shell particle production during inflation, presenting a unique opportunity for the “direct detection” of particles with masses as large as the inflationary Hubble scale ( H ). However, the strength of such non-analyticity ordinarily drops exponentially by a Boltzmann-like factor as masses exceed H . In this paper, we study an exception provided by a dimension-5 derivative coupling of the inflaton to heavy-particle currents, applying it specifically to the case of two real scalars. The operator has a “chemical potential” form, which harnesses the large kinetic energy scale of the inflaton, \( {\overset{\cdot }{\phi}}_0^{1/2}\approx 60H \) , to act as an efficient source of scalar particle production. Derivative couplings of inflaton ensure radiative stability of the slow-roll potential, which in turn maintains (approximate) scale-invariance of the inflationary correlations. We show that a signal not suffering Boltzmann suppression can be obtained in the bispectrum with strength f NL ~ \( \mathcal{O} \) (0 . 01–10) for an extended range of scalar masses \( \lesssim {\overset{\cdot }{\phi}}_0^{1/2} \) , potentially as high as 10 15 GeV, within the sensitivity of upcoming LSS and more futuristic 21-cm experiments. The mechanism does not invoke any particular fine-tuning of parameters or breakdown of perturbation-theoretic control. The leading contribution appears at tree-level , which makes the calculation analytically tractable and removes the loop-suppression as compared to earlier chemical potential studies of non-zero spins. The steady particle production allows us to infer the effective mass of the heavy particles and the chemical potential from the variation in bispectrum oscillations as a function of co-moving momenta. Our analysis sets the stage for generalization to heavy bosons with non-zero spin.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

The Higgs trilinear coupling and the scale of new physics

We consider modifications of the Higgs potential due to new physics at high energy scales. These upset delicate cancellations predicted by the Standard Model for processes involving Higgs bosons and longitudinal gauge bosons, and lead to a breakdown of the theory at high energies. We focus on modifications of the Higgs trilinear coupling and use the violation of tree-level unitarity as an estimate of the scale where the theory breaks down. We obtain a completely model-independent bound of $ \underset{\sim }{<} $ 13 TeV for an order-1 modification of the trilinear. We argue that this bound can be saturated only in fine-tuned models, and the scale of new physics is likely to be much lower. The most stringent bounds are obtained from amplitudes involving multiparticle states that are not conventional scattering states. Our results show that a future determination of the Higgs cubic coupling can point to a well-defined scale of new physics that can be targeted and explored at future colliders.

43 PARTICLE ACCELERATORS↗

The one-loop impact of a dependent mass: the role of m 3 in the C2HDM

In the complex 2-Higgs-Doublet Model (C2HDM), the mass m 3 of the heaviest neutral scalar h 3 is usually chosen as a derived parameter. We investigate one-loop corrections to m 3 and their impact on decays of h 3 . Very fine-tuned regions of the parameter space can be found where such corrections are large, not due to subtraction schemes, but rather due to the particular dependence of m 3 on the independent parameters. We show that even moderate corrections can have a significant impact on decays of h 3 , as they may be several times enhanced by leading-order factors.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Radiative natural supersymmetry emergent from the string landscape

In string theory with flux compactifications, anthropic selection for structure formation from a discretuum of vacuum energy values provides at present our only understanding of the tiny yet positive value of the cosmological constant. We apply similar reasoning to a toy model of the multiverse restricted to vacua with the MSSM as the low energy effective theory. Here, one expects a statistical selection favoring large soft SUSY breaking terms leading to a derived value of the weak scale in each pocket universe (with appropriate electroweak symmetry breaking) which differs from the weak scale as measured in our universe. In contrast, the SUSY preserving μ parameter is selected uniformly on a log scale as is consistent with the distribution of SM fermion masses: this favors smaller values of μ. An anthropic selection of the weak scale to within a factor of a few of our measured value — in order to produce complex nuclei as we know them (atomic principle) — provides statistical predictions for Higgs and sparticle masses in accord with LHC measurements. The statistical selection then more often leads to (radiatively-driven) natural SUSY models over the Standard Model or finely-tuned SUSY models such as mSUGRA/CMSSM, split, mini-split, spread, high scale or PeV SUSY. The predicted Higgs and superparticle spectra might be testable at HL-LHC or ILC via higgsino pair production but is certainly testable at higher energy hadron colliders with $ \sqrt{s}$ ~ 30–100 TeV.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Dark photon and dark Z mediated B meson decays

We study flavor changing neutral current decays of B and K mesons in the dark U(1) D model, with the dark photon/dark Z mass between 10 MeV and 2 GeV. Although the model provides an improved fit (compared to the standard model) to the differential decay distributions of B → K ($\ast$) ℓ + ℓ – , with ℓ = μ, e, and B s → $\phi$μ + μ – , the allowed parameter space is ruled out by measurements of atomic parity violation, K + → μ + + invisible decay, and ${B}_s-{\overline{B}}_s$ mixing, among others. To evade constraints from low energy data, we extend the model to allow for (1) additional invisible Z D decay, (2) a direct vector coupling of Z D to muons, and (3) a direct coupling of Z D to both muons and electrons, with the electron coupling fine-tuned to cancel the Z D coupling to electrons via mixing. We find that only the latter case survives all constraints.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗