17 research outputs found
Photoenhanced Electrochemical Interaction between <i>Shewanella</i> and a Hematite Nanowire Photoanode
Here we report the investigation
of interplay between
light, a
hematite nanowire-arrayed photoelectrode, and <i>Shewanella oneidensis</i> MR-1 in a solar-assisted microbial photoelectrochemical system (solar
MPS). Whole cell electrochemistry and microbial fuel cell (MFC) characterization
of <i>Shewanella oneidensis</i> strain MR-1 showed that
these cells cultured under (semi)anaerobic conditions expressed substantial <i>c</i>-type cytochrome outer membrane proteins, exhibited well-defined
redox peaks, and generated bioelectricity in a MFC device. Cyclic
voltammogram studies of hematite nanowire electrodes revealed active
electron transfer at the hematite/cell interface. Notably, under a
positive bias and light illumination, the hematite electrode immersed
in a live cell culture was able to produce 150% more photocurrent
than that in the abiotic control of medium or dead culture, suggesting
a photoenhanced electrochemical interaction between hematite and <i>Shewanella</i>. The enhanced photocurrent was attributed to
the additional redox species associated with MR-1 cells that are more
thermodynamically favorable to be oxidized than water. Long-term operation
of the hematite solar MPS with light on/off cycles showed stable current
generation up to 2 weeks. Fluorescent optical microscope and scanning
electron microscope imaging revealed that the top of the hematite
nanowire arrays were covered by a biofilm, and iron determination
colorimetric assay revealed 11% iron loss after a 10-day operation.
To our knowledge, this is the first report on interfacing a photoanode
directly with electricigens in a MFC system. Such a system could open
up new possibilities in solar-microbial device that can harvest solar
energy and recycle biomass simultaneously to treat wastewater, produce
electricity, and chemical fuels in a self-sustained manner
Additional file 1 of Genome-wide identification and stress response analysis of BcaCPK gene family in amphidiploid Brassica carinata
Supplementary Material
Additional file 1: of Huge fetal hepatic Hemangioma: prenatal diagnosis on ultrasound and prognosis
Description of the data and materials of 6 cases with huge hepatic hemangiomas. (XLSX 21Â kb
Computational and Photoelectrochemical Study of Hydrogenated Bismuth Vanadate
We
demonstrate hydrogenation as a facile method to significantly enhance
the performance of BiVO<sub>4</sub> films for photoelectrochemical
water oxidation. Hydrogenation was performed for BiVO<sub>4</sub> films
by annealing them in hydrogen atmosphere at elevated temperatures
between 200 and 400 °C. Hydrogen gas can reduce BiVO<sub>4</sub> to form oxygen vacancies as well as hydrogen impurities. DFT calculation
predicted that both oxygen vacancies and hydrogen impurities are shallow
donors for BiVO<sub>4</sub> with low formation energies. These defects
could increase the donor densities of BiVO<sub>4</sub> without introducing
deep trap states. Electrochemical impedance measurements showed that
the donor densities of BiVO<sub>4</sub> films were significantly enhanced
upon hydrogenation. Hydrogen-treated BiVO<sub>4</sub> (H-BiVO<sub>4</sub>) photoanodes achieved a maximum photocurrent density of 3.5
mA/cm<sup>2</sup> at 1.0 V vs Ag/AgCl, which is 1 order of magnitude
higher than that of air-annealed BiVO<sub>4</sub> obtained at the
same potential. The enhanced photoactivities were attributed to increased
donor densities of H-BiVO<sub>4</sub>, which facilitates the charge
transport and collection
Supercapacitors Based on Three-Dimensional Hierarchical Graphene Aerogels with Periodic Macropores
Graphene is an atomically thin, two-dimensional
(2D) carbon material
that offers a unique combination of low density, exceptional mechanical
properties, thermal stability, large surface area, and excellent electrical
conductivity. Recent progress has resulted in macro-assemblies of
graphene, such as bulk graphene aerogels for a variety of applications.
However, these three-dimensional (3D) graphenes exhibit physicochemical
property attenuation compared to their 2D building blocks because
of one-fold composition and tortuous, stochastic porous networks.
These limitations can be offset by developing a graphene composite
material with an engineered porous architecture. Here, we report the
fabrication of 3D periodic graphene composite aerogel microlattices
for supercapacitor applications, via a 3D printing technique known
as direct-ink writing. The key factor in developing these novel aerogels
is creating an extrudable graphene oxide-based composite ink and modifying
the 3D printing method to accommodate aerogel processing. The 3D-printed
graphene composite aerogel (3D-GCA) electrodes are lightweight, highly
conductive, and exhibit excellent electrochemical properties. In particular,
the supercapacitors using these 3D-GCA electrodes with thicknesses
on the order of millimeters display exceptional capacitive retention
(ca. 90% from 0.5 to 10 A·g<sup>–1</sup>) and power densities
(>4 kW·kg<sup>–1</sup>) that equal or exceed those
of
reported devices made with electrodes 10–100 times thinner.
This work provides an example of how 3D-printed materials, such as
graphene aerogels, can significantly expand the design space for fabricating
high-performance and fully integrable energy storage devices optimized
for a broad range of applications
Additional file 1: of Genomics of NSCLC patients both affirm PD-L1 expression and predict their clinical responses to anti-PD-1 immunotherapy
Table S1. Molecules with immunosuppressive functions used in simulation models to predict PD-1 drug responder status. (DOCX 55Â kb
Additional file 6: of Genomics of NSCLC patients both affirm PD-L1 expression and predict their clinical responses to anti-PD-1 immunotherapy
Table S5. Analysis of the Discovery and Validation datasets was performed using Weka 3. The first number in each column represented the number of patient treatment responses correctly classified by the model. The second number represented the number of incorrectly classified patient treatment responses. The GOAL row at the bottom of each column described the number of correctly and incorrectly classified patients in the simulation models. The Test Set columns described the output from applying the model trained on the Discovery set to the Validation set. The âTest and Trainâ columns described test set accuracy (test set column) plus the training error (results obtained by applying the model to the training set, i.e. training error). (DOCX 19Â kb
Controlled Synthesis of AlN/GaN Multiple Quantum Well Nanowire Structures and Their Optical Properties
We report the controlled synthesis of AlN/GaN multi-quantum
well
(MQW) radial nanowire heterostructures by metal–organic chemical
vapor deposition. The structure consists of a single-crystal GaN nanowire
core and an epitaxially grown (AlN/GaN)<sub><i>m</i></sub> (<i>m</i> = 3, 13) MQW shell. Optical excitation of individual
MQW nanowires yielded strong, blue-shifted photoluminescence in the
range 340–360 nm, with respect to the GaN near band-edge emission
at 368.8 nm. Cathodoluminescence analysis on the cross-sectional MQW
nanowire samples showed that the blue-shifted ultraviolet luminescence
originated from the GaN quantum wells, while the defect-associated
yellow luminescence was emitted from the GaN core. Computational simulation
provided a quantitative analysis of the mini-band energies in the
AlN/GaN superlattices and suggested the observed blue-shifted emission
corresponds to the interband transitions between the second subbands
of GaN, as a result of quantum confinement and strain effect in these
AlN/GaN MQW nanowire structures
Additional file 7: of Genomics of NSCLC patients both affirm PD-L1 expression and predict their clinical responses to anti-PD-1 immunotherapy
Figure S2. An example of the relationship between PD-L1 expression and predicted TGFB1 expression using Weka 3 algorithms for all patients in the dataset. Similar trends were seen when comparing the PD-L1 expression level to the other 13 predicted molecules. For this, the number of gene mutations identified for each patient ranged from 2 to 36 with a total of 264 unique genes between all patients. This categorical data was preprocessed and expanded into a gene vector of length 264 to represent each of the unique genes. For each gene in the vector, the data was represented in binary; a 1 was assigned if the patient had a mutation in this gene, a 0 otherwise. Two datasets, one including gene mutations (Molecules and Gene Mutations) and one without (Molecules), were both used to learn prediction models. The Discovery and Validation datasets were determined based on the split provided to allow for comparable results. The performance of a subset of these models on the testing and training sets for both Molecules and Molecules and Gene Mutations datasets are shown. The SMO support vector machine with a normalized polynomial kernel had the best performance when applied to the molecule dataset. This model correctly identified 24 out of 29 patients whereas the simulation models correctly identified 25 of 29. This was only a difference of one match between the two prediction methods. Still, several other methods, while not performing as well overall, were able to identify 9 patients in the test dataset accurately. This was near the computational simulation model prediction capability in which 10 patients were successfully identified in the test dataset. In general, adding the gene mutation data to the molecule data either maintained or decreased the performance of a model. (DOCX 4114Â kb