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The robust and automated determination of crystal symmetry is of utmost importance in material characterization and analysis. Recent studies have shown that deep learning (DL) methods can effectively reveal the correlations between X-ray or electron-beam diffraction patterns and crystal symmetry. Despite their promise, most of these studies have been limited to identifying relatively few classes into which a target material may be grouped. On the other hand, the DL-based identification of crystal symmetry suffers from a drastic drop in accuracy for problems involving classification into tens or hundreds of symmetry classes (e.g., up to 230 space groups), severely limiting its practical usage. Here, we demonstrate that a combined approach of shaping diffraction patterns and implementing them in a multistream DenseNet (MSDN) substantially improves the accuracy of classification. Even with an imbalanced dataset of 108,658 individual crystals sampled from 72 space groups, our model achieves 80.12 ± 0.09% space group classification accuracy, outperforming conventional benchmark models by 17–27 percentage points (%p). The enhancement can be largely attributed to the pattern shaping strategy, through which the subtle changes in patterns between symmetrically close crystal systems (e.g., monoclinic vs. orthorhombic or trigonal vs. hexagonal) are well differentiated. We additionally find that the MSDN architecture is advantageous for capturing patterns in a richer but less redundant manner relative to conventional convolutional neural networks. The proposed protocols in regard to both input descriptor processing and DL architecture enable accurate space group classification and thus improve the practical usage of the DL approach in crystal symmetry identification.

Monitoring the effects of mild hydrothermal conditions, in situ, on siliceous materials remains challenging using surface science techniques, which often require electrically conductive substrates....

Colonization and maintenance of mosquitoes in the laboratory is required to study physiology, ecology, and behavior of mosquitoes and interactions between mosquito and pathogens. Artificial blood feeding systems have been widely used to maintain the laboratory colony of Aedes albopictus. In this study, we investigated the effects of mating period (1, 3, 6, and 10 days) and time‐of‐day for bloodmeal (08:00, 13:00, and 18:00) in the use of an artificial feeding system on blood‐feeding rate, female fecundity, egg hatching rate, and developmental time of the Asian tiger mosquito, A. albopictus. Younger females mated for three or fewer days reproduced more eggs compared to those of oldest females mated for ten days. Similar to the result for eggs laid, the mean egg‐hatching rate was significantly higher from the offspring of younger females than from those of older females. However, mating period and time‐of‐day for bloodmeal had no effect on the blood feeding rate and developmental time. Taken together, we suggest that three‐day mating with bloodmeal at 18:00 is optimal for maintaining colonies of A. albopictus in laboratory conditions.

Brookite, the least studied crystalline phase of TiO2, recently has been found to have excellent photocatalytic activities, comparable to that of anatase TiO2. However, its activity is highly depen...

The development of catalysts for the electrochemical N2 reduction reaction (NRR) with a low limiting potential and high Faradaic efficiency is highly desirable but remains challenging. Here, to ach...

The point kinetics model for an initially subcritical system such as the accelerator-driven system (ADS) requires the adjoint-weighted kinetics parameters to be estimated in its reference subcritic...

As an alternative to the conventional Haber–Bosch process for NH₃ synthesis that operates under harsh conditions, an electrochemical process has recently been pursued. Here, using a joint experiment–density functional calculation approach, we determine the activity trend of four transition metals (TMs) (Fe, Ru, Rh, and Pd) for N₂ reduction to NH₃: Fe > Ru > Pd > Rh, where the protonation step of *N₂ to form *N₂H (* indicates surface sites) is a potential determining step (PDS). The activity trend of the electrocatalysts is determined by the ability of the adsorbate (*N₂) over the catalyst surfaces to easily obtain electrons at the PDS with an assumption of a scaling relationship between the activation energy barrier and the free energy difference. In electronic structures, the ability can be estimated by the energy difference between the lowest unoccupied molecular orbital (LUMO) of the adsorbed N₂ on the TM surfaces and the fermi energy (E_F). For early TMs (e.g., Sc and Ti) where the PDS is *NH protonation reaction to form *NH₂, the activity of the TMs can be similarly explained with an electronic structural feature that is the energy difference between the LUMO of the *NH and the E_F. Based on the origin, we additionally consider 10 TMs (Ni, Cr, Mn, Co, Cu, Mo, Ag, W, Pt, and Au) and then determine the activity trend of the total 16 diverse TMs for NH₃ synthesis. We expect that this work could pave the way to novel alloy catalysts with a high activity for electrochemical NH₃ synthesis.

Although the thermal mass of floors in buildings has been demonstrated to help shift cooling load, there is still a lack of information about how floor covering can influence the floor’s load shifting capability and buildings’ demand flexibility. To fill this gap, we estimated demand flexibility based on the daily peak cooling load reduction for different floor configurations and regions, using EnergyPlus simulations. As a demand response strategy, we used precooling and global temperature adjustment. The result demonstrated an adverse impact of floor covering on the building’s demand flexibility. Specifically, under the same demand response strategy, the daily peak cooling load reductions were up to 20–34% for a concrete floor whereas they were only 17–29% for a carpet-covered concrete floor. This is because floor covering hinders convective coupling between the concrete floor surface and the zone air and reduces radiative heat transfer between the concrete floor surface and the surrounding environment. In hot climates such as Phoenix, floor covering almost negated the concrete floor’s load shifting capability and yielded low demand flexibility as a wood floor, representing low thermal mass. Sensitivity analyses showed that floor covering’s effects can be more profound with a larger carpet-covered area, a greater temperature adjustment depth, or a higher radiant heat gain. With this effect ignored for a given building, its demand flexibility would be overestimated, which could prevent grid operators from obtaining sufficient demand flexibility to maintain a grid. Our findings also imply that for more efficient grid-interactive buildings, a traditional standard for floor design could be modified with increasing renewable penetration.

Within first-principles density functional theory (DFT) frameworks, it is challenging to predict the electronic structures of nanoparticles (NPs) accurately but fast. Herein, a machine-learning architecture is proposed to rapidly but reasonably predict electronic density of states (DOS) patterns of metallic NPs via a combination of principal component analysis (PCA) and the crystal graph convolutional neural network (CGCNN). With the PCA, a mathematically high-dimensional DOS image can be converted to a low-dimensional vector. The CGCNN plays a key role in reflecting the effects of local atomic structures on the DOS patterns of NPs with only a few of material features that are easily extracted from a periodic table. The PCA-CGCNN model is applicable for all pure and bimetallic NPs, in which a handful DOS training sets that are easily obtained with the typical DFT method are considered. The PCA-CGCNN model predicts the R2 value to be 0.85 or higher for Au pure NPs and 0.77 or higher for Au@Pt core@shell bimetallic NPs, respectively, in which the values are for the test sets. Although the PCA-CGCNN method showed a small loss of accuracy when compared with DFT calculations, the prediction time takes just ~ 160 s irrespective of the NP size in contrast to DFT method, for example, 13,000 times faster than the DFT method for Pt147. Our approach not only can be immediately applied to predict electronic structures of actual nanometer scaled NPs to be experimentally synthesized, but also be used to explore correlations between atomic structures and other spectrum image data of the materials (e.g., X-ray diffraction, X-ray photoelectron spectroscopy, and Raman spectroscopy).

Today, multimetallic nanoparticles (NPs) are extensively studied to search for high-performance catalysts. Unfortunately, a huge material space of NPs composed of immiscible elements has been gener...

Summary Herbivore‐induced plant volatiles (HIPVs) are widely recognized as an ecologically important defensive response of plants against herbivory. Although the induction of this ‘cry for help’ has been well documented, only a few studies have investigated the inhibition of HIPVs by herbivores and little is known about whether herbivores have evolved mechanisms to inhibit the release of HIPVs.To examine the role of herbivore effectors in modulating HIPVs and stomatal dynamics, we conducted series of experiments combining pharmacological, surgical, genetic (CRISPR‐Cas9) and chemical (GC‐MS analysis) approaches.We show that the salivary enzyme, glucose oxidase (GOX), secreted by the caterpillar Helicoverpa zea on leaves, causes stomatal closure in tomato (Solanum lycopersicum) within 5 min, and in both tomato and soybean (Glycine max) for at least 48 h. GOX also inhibits the emission of several HIPVs during feeding by H. zea, including (Z)‐3‐hexenol, (Z)‐jasmone and (Z)‐3‐hexenyl acetate, which are important airborne signals in plant defenses.Our findings highlight a potential adaptive strategy where an insect herbivore inhibits plant airborne defenses during feeding by exploiting the association between stomatal dynamics and HIPV emission.

Black phosphorus has recently attracted significant attention as an anode material for sodium-ion batteries (SIBs); however, the material suffers from a severe volume change during charge/discharge processes, leading to poor cycle life. To overcome this drawback of black phosphorus, a phosphorene/graphene (P/G) heterostructure was recently proposed, but no atomistic understanding of the sodiation mechanism has yet been reported. In this work, we report an atomistic mechanism for the sodiation of the P/G hybrid material based on first-principles calculations. The layered structure of P/G is maintained up to the composition of Na0.25P/G, which can be referred to as an intercalation process; however, above that composition, further sodiation leads to the dissociation of P−P bonds and the formation of an amorphous NaxP/G structure where the graphene layers are not broken and no Na atoms intercalate into the spaces between two graphene layers, which is referred to as an alloying process. According to our first...

The fruit fly, Drosophila melanogaster, is predominantly found in overripe, rotten, fermenting, or decaying fruits and is constantly exposed to chemical stressors such as acetic acid, ethanol, and 2-phenylethanol. D. melanogaster has been employed as a model system for studying the molecular bases of various types of chemical-induced tolerance. Expression profiling using Illumina sequencing has been performed for identifying changes in gene expression that may be associated with evolutionary adaptation to exposure of acetic acid, ethanol, and 2-phenylethanol. We identified a total of 457 differentially expressed genes that may affect sensitivity or tolerance to three chemicals in the chemical treatment group as opposed to the control group. Gene-set enrichment analysis revealed that the genes involved in metabolism, multicellular organism reproduction, olfaction, regulation of signal transduction, and stress tolerance were over-represented in response to chemical exposure. Furthermore, we also detected a coordinated upregulation of genes in the Toll- and Imd-signaling pathways after the chemical exposure. Quantitative reverse transcription PCR analysis revealed that the expression levels of nine genes within the set of genes identified by RNA sequencing were up- or downregulated owing to chemical exposure. Taken together, our data suggest that such differentially expressed genes are coordinately affected by chemical exposure. Transcriptional analyses after exposure of D. melanogaster with three chemicals provide unique insights into subsequent functional studies on the mechanisms underlying the evolutionary adaptation of insect species to environmental chemical stressors.

Directly synthesized zeolite MFI nanosheets are promising building blocks for MFI thin films with large and oriented grains. The secondary growth of MFI nanosheets on Si wafers in tetraethylammonium hydroxide (TEAOH) silica sols was investigated, and conditions that result in well-oriented and intergrown film microstructure were established. This has enabled the fabrication of thin (∼300 nm) b-oriented MFI films with large grain-size (>2 μm) from seed-removed nanosheet monolayer coatings. Moreover, the faceted and anisotropic shape of MFI nanosheets allowed the measurement of MFI growth in different crystallographic directions and confirmed the twinning-free preferential growth along the c-axis (a lateral direction of the nanosheet), compared to the b-axis (the direction normal to the nanosheet basal plane), with ratios in a range between 4 and 11.

Understanding the origins of the excessive Stokes shift in the lead chalcogenides family of colloidal quantum dots (CQDs) is of great importance at both the fundamental and applied levels; however, our current understanding is far from satisfactory. Here, utilizing a combination of ab initio computations and UV–vis and photoluminescence measurements, we investigated the contributions to the Stokes shift from polydispersity, ligands, and defects in PbS CQDs. The key results are as follows: (1) The size and energetic disorder of a polydisperse CQD film increase the Stokes shift by 20 to 50 meV compared to that of an isolated CQD; (2) Franck–Condon (FC) shifts increase as the electronegativities of the ligands increase, but the variations are small (

The control of membrane morphology and microstructure is crucial to improve the separation performance of molecular-sieve membranes. This can be enabled by making thin, dense, and uniform seed-crystal coatings, which are then intergrown into continuous membranes. Herein, we show a novel and simple floating particle coating method can give closely packed monolayers of zeolite nanosheets on nonporous or porous supports. The zeolite nanosheet monolayer is formed at the air-water interface in a conical Teflon trough. As the water in the trough is drained, the monolayer is deposited on a support placed below. Membranes prepared by gel-free secondary growth of the nanosheets deposited by this method show unprecedented ultra-selective performance for separation of para- from ortho-xylene (separation factor >10 000).

We propose a recently discovered material, namely, β-CuGaO2 [T. Omata et al., J. Am. Chem. Soc. 2014, 136, 3378] as a strong candidate material for efficient ferroelectric photovoltaics (FPVs). According to first-principles predictions exploiting hybrid density functional, β-CuGaO2 is ferroelectric with a remarkably large remanent polarization of 83.80 μC/cm2, even exceeding that of the prototypic FPV material, BiFeO3. Quantitative theoretical analysis further indicates the asymmetric Ga 3dz2–O 2pz hybridization as the origin of the Pna21 ferroelectricity. In addition to the large displacive polarization, unusually small band gap (1.47 eV) and resultantly strong optical absorptions additionally differentiate β-CuGaO2 from conventional ferroelectrics; this material is expected to overcome critical limitations of currently available FPVs.

Li4Ti5O12 (LTO) has attracted tremendous attention as a stationary Li-ion battery anode material due to its excellent stability. However, the poor rate capability caused by the low electrical conductivity limits its practical use. Previously, Mg-doping in LTO has been used to improve the electrical conductivity and electrochemical properties, but the Mg-doped LTO system generally exhibits large anomalies in the electrical properties and capacities, which limits the reliable mass-production of engineered LTO. In this study, on the basis of first-principles calculations and related experiments, we systematically study the effects of charge-compensating point defects of the Mg-doped LTO on the electrical properties. A combination of first-principles calculations with thermodynamic modeling shows that high-temperature annealing under reducing conditions could effectively alter the Mg-doping site from a Ti4+ to Li+ site and increase the electrical conductivity significantly due to reduced electron effective ma...

Understanding the roles of point defects in optical transitions is a key to the desirable engineering of photochemical materials. In this study, the origins of the significantly varying optical and photochemical properties of Nb-doped anatase TiO2 were systematically investigated, using density functional theory (DFT) calculations and experimental verifications. We found from DFT calculations that the desirable band gap reduction of anatase TiO2 by ∼0.1 eV reported in many of experimental reports and the resultant improvements of photocatalytic and photovoltaic efficiencies of Nb5+-doped anatase TiO2 are due to the formation of complex (NbTi-VTi)3− as the compensator of NbTi+. Our experiments demonstrated that the O2-rich annealing, which is expected to increase the concentration of desirable (NbTi-VTi)3− complex, narrows band gap of TiO2 and strongly enhances the photocatalytic activity of Nb-doped TiO2 particle. On the contrary, pure TiO2 showed rather worse photocatalytic performances when annealed in O2-rich atmosphere, which is due to the formation of deep level by O-interstitial defect (Oi). Theoretically obtained charge effective masses could further explain the different photocatalytic activities of undoped and Nb-doped TiO2.

Nanoscale crystal growth control is crucial for tailoring two-dimensional (2D) zeolites (crystallites with thickness less than two unit cells) and thicker zeolite nanosheets for applications in separation membranes and as hierarchical catalysts. However, methods to control zeolite crystal growth with nanometer precision are still in their infancy. Herein, we report solution-based growth conditions leading to anisotropic epitaxial growth of 2D zeolites with rates as low as few nanometers per day. Contributions from misoriented surface nucleation and rotational intergrowths are eliminated. Growth monitoring at the single-unit-cell level reveals novel nanoscale crystal-growth phenomena associated with the lateral size and surface curvature of 2D zeolites.

In this work, we use Computational Fluid Dynamics (CFD) to generate the distributed dynamic responses of temperature and moisture in a restaurant that correspond to perturbations of input variables. The CFD model is validated with experimental data of the roof top units return temperatures. A Reduced Order Model (ROM) is developed by approximating the responses to these perturbations using a linear time-invariant model. The resulting indoor-air model is coupled to a dynamic envelope model with longer time scales. Assuming the outside temperature distribution and the occupants' loads are known throughout the day, optimal control is applied to our coupled model to minimize the cooling power subject to local and global comfort constraints. The results show that the applied method is more efficient and comfortable than the results given by the experimentally measured response of a conventional set-point control strategy.

Small and medium sized commercial buildings, such as retail stores, restaurants and factories, often utilize multiple packaged air conditioners, i.e. roof top units (RTUs), to provide cooling and heating for open spaces. A conventional control approach for these buildings relies on local feedback control, where each unit is cycled on and off using its own thermostat. The lack of coordination between RTUs represents a missed opportunity for operating more efficient units when there is strong coupling between the spaces they serve and can lead to unnecessarily high electrical demand due to the inherent randomness of unit cycling. This paper presents an overall model-based predictive control (MPC) approach for RTU coordination that includes a description of the control architecture, modeling approach, implementation, and assessment. We provide results of laboratory and field tests that demonstrate the short-term and long-term performance of the MPC solution in terms of energy and demand savings.

Here, we propose the solid-solution alloy nanoparticles (NPs) composed of Pt and Au supported on TiO2 as an efficient catalyst for direct H2O2 synthesis, although Pt and Au are entirely immiscible in the bulk phases. Pt and Au atoms were homogeneously distributed in a wide composition range of PtxAu100-x NPs (10 ≤ x ≤ 80) supported on TiO2. The most Au-rich Pt10Au90 NPs/TiO2 exhibits the best catalytic performances, H2O2 selectivity (94.5 ± 0.6%) and productivity (959 ± 12 mmolH2O2•gmetal−1•h−1), even under mild conditions (10 °C, 1 atm) and without halide-ion additives. Density functional theory calculations reveal that the introduction of inactive Au atoms strongly suppresses both O-O bond scission and OH hydrogenation, thereby enabling near unity selectivity. The Pt-Au catalyst is Pd-absent, which is noteworthy given previous efforts in direct H2O2 synthesis are mostly limited to prototypical Pd or Pd-based modifications. This work will stimulate active utilization of immiscible elemental combinations for the direct H2O2 synthesis catalyst development.

An efficient propylene/propane separation is a very critical process for saving the cost of energy in the petrochemical industry. For separation based on the pressure-swing adsorption process, we have screened ∼1 million crystal structures in the Cambridge Structural Database and Inorganic Crystal Structural Database with descriptors such as the surface area of N2, accessible surface area of propane, and pore-limiting diameter. Next, grand canonical Monte Carlo simulations have been performed to investigate the selectivities and working capacities of propylene/propane under experimental process conditions. Our simulations reveal that the selectivity and the working capacity have a trade-off relationship. To increase the working capacity of propylene, porous materials with high largest cavity diameters (LCDs) and low propylene binding energies (Qst) should be considered; conversely, for a high selectivity, porous materials with low LCDs and high propylene Qst should be considered, which leads to a trade-of...

Recent advances in quantum dot surface passivation have led to a rapid development of high-efficiency solar cells. Another critical element for achieving efficient power conversion is the charge neutrality of quantum dots, as charge imbalances induce electronic states inside the energy gap. Here we investigate how the simultaneous introduction of metal cations and halide anions modifies the charge balance and enhances the solar cell efficiency. The addition of metal salts between QD deposition and ligand exchange with 1,3-BDT results in an increase in the short-circuit current and fill factor, accompanied by a distinct reduction in a crossover between light and dark current density-voltage characteristics.

Due to its high redox potential, zinc sulfide (ZnS) is considered an excellent semiconductor photocatalyst. However, the rapid recombination rate of the photogenerated electron-hole pairs limits the efficiency of ZnS for photocatalytic reactions. Herein, we suggest a design rule of heterojunction structure of ZnS for improvement of its photocatalytic performance. Two specific properties are specially emphasized: phase immiscibility and the different growth rates of the component materials. The phase immiscibility not only guarantees a well-separated interface, it also enables the technical convenience of one-pot synthesis. The different growth rates help form wide heterojunctions that foster the efficient consumption of materials. We found that the ZnS/NixSy composite not only meets the aforementioned requirements but also has proper band alignment. Ready-to-use heterojunction photocatalysts (ZnS/NixSy) were obtained via one-pot synthesis, thanks to the different growth rates and the immiscibility of the two sulfides. Combining ZnS with NixSy resulted in substantially improved photocatalytic activity in regard to dye decomposition and H2 production via water splitting. Both band position measurements and DFT simulations indicated that NixSy is a co-catalyst for ZnS, allowing sufficient band offset for electron-hole separation. Abundant and mass-producible, the ZnS/NixSy composite can effectively substitute for noble metal photocatalysis when it comes to organic pollutant degradation and water splitting.

A challenge in the synthesis of single-wall carbon nanotubes (SWCNTs) is the lack of control over the formation and evolution of catalyst nanoparticles and the lack of control over their size or chirality. Here, zeolite MFI nanosheets (MFI-Ns) are used to keep cobalt (Co) nanoparticles stable during prolonged annealing conditions. Environmental transmission electron microscopy (ETEM) shows that the MFI-Ns can influence the size and shape of nanoparticles via particle/support registry, which leads to the preferential docking of nanoparticles to four or fewer pores and to the regulation of the SWCNT synthesis products. The resulting SWCNT population exhibits a narrow diameter distribution and SWCNTs of nearly all chiral angles, including sub-nm zigzag (ZZ) and near-ZZ tubes. Theoretical simulations reveal that the growth of these unfavorable tubes from unsupported catalysts leads to the rapid encapsulation of catalyst nanoparticles bearing them; their presence in the growth products suggests that the MFI-Ns prevent nanoparticle encapsulation and prologue ZZ and near-ZZ SWCNT growth. These results thus present a path forward for controlling nanoparticle formation and evolution, for achieving size- and shape-selectivity at high temperature, and for controlling SWCNT synthesis.

Practical catalysts with a porous framework, such as zeolites, host catalytic reactions at active sites engrained in the pores and channels of the scaffold. The mechanism of interaction at these active sites, defining catalyst performance, remains elusive, in large part, due to the lack of surface characterization methods available for thick films or powders. Here, we present thin film analogs of practical catalysts that allow for the implementation of surface characterization tools, including advanced microscopy and operando spectroscopy methodologies. Specifically, we investigated bilayer silica, MFI nanosheets, and UiO-66 thin films using a multi-modal approach addressing film growth, characterization, and gas adsorption aimed at understanding catalytic activity, reactivity, and selectivity properties, as defined by molecular-level changes in the reaction mechanism.

The synthesis, characterization, and electrocatalytic properties of mesoporous carbon materials doped with nitrogen atoms and iron are reported and compared for the catalyzed reduction of oxygen gas at fuel cell cathodes. Mixtures of common and inexpensive organic precursors, melamine, and formaldehyde were pyrolyzed in the presence of transition-metal salts (e.g., nitrates) within a mesoporous silica template to yield mesoporous carbon materials with greater extents of graphitization than those of others prepared from small-molecule precursors. In particular, Fe,N-doped carbon materials possessed high surface areas (∼800 m2/g) and high electrical conductivities (∼19 S/cm), which make them attractive for electrocatalyst applications. The surface compositions of the mesoporous Fe,N-doped carbon materials were postsynthetically modified by acid washing and followed by high-temperature thermal treatments, which were shown by X-ray photoelectron spectroscopy to favor the formation of graphitic and pyridinic n...

Qualitative and quantitative determination of total arsenic content and arsenic species in rice is very important because rice is one of the main sources of human arsenic intake. However, extraction and determination of arsenic species in rice has been very difficult due to severe matrix interference. An alternative analytical method was developed in this study to determine arsenic species in rice by using ion chromatography coupled to inductively coupled plasma-mass spectrometry. Two internal standards were used. The first internal standard was injected before sample introduction to correct signal change with time. The second internal standard was spiked into the sample to reduce matrix interference. Using the developed method, recoveries of dimethylarsinic acid, monomethylarsonic acid, and inorganic arsenic compared to certified values (NIST SRM 1568b rice flour) were 116%, 107%, and 92%, respectively.

Despite the amorphous nature of glassy water, x-ray or neutron scattering experiments reveal sharp peaks in the structure factor, indicating the existence of medium-range order (MRO) in the system. However the real space origin of the peaks has yet to be disclosed. Herein, we use a combined approach of molecular dynamics simulations and persistent homology (PH) to investigate two types of glassy water, low-density amorphous (LDA) and high-density amorphous (HDA) ices. We present prominent MRO ring structures in each type of the ices, distinguished by their size and shape as well as the number of their components: MRO rings in HDA are observed smaller, less planar and more membered, compared to those in LDA. The PH-extracted MRO rings successfully reproduce the quantitative features, including the position and width, of the first sharp diffraction peaks in the structure factor, hence suitably serving as the origin of experimental MRO signatures in the amorphous ices. Our study supports that PH is an effective tool to identify hidden MRO in amorphous configurations.

A solution-processable electrode is a key component in flexible electronics. Despite recent studies on silver nanocrystal (Ag NC)-based electrodes with high conductivity, the absence of a strategy to control the work function has limited the development of low-cost and high-performance electronic devices. In this report, we introduce a chemical route to manipulate the work function of solution-processed Ag NC-based electrodes. The structural, chemical, optical, and electronic properties, as well as the work functions, of the Ag NC thin films treated with three types of halide ligands (Cl−, Br−, and I−) were investigated. Ultraviolet photoelectron spectroscopy analysis shows that the work functions are shifted to 4.76, 4.43, and 4.04 eV when the ligands are changed to Cl−, Br−, and I−, respectively. The trend in the shift induced by the halide ligands matches the results of atomistic density functional theory calculations and scales with the strength of the dipoles formed by the electron transfer at the NC/ligand interfaces. To demonstrate the impact of our strategy in device applications, we fabricated all-NC-based thin-film transistors and complementary metal-oxide-semiconductor inverters. This study provides a fundamental understanding of the surface states of nanomaterials and also offers technological benefits for the construction of low-cost, high-performance electronic devices.