bluuebunny/crossref_metadata_2025_split huggingface dataset:
Climate uncertainty impacts on optimal mitigation pathways and social cost of carbon
Authors: Smith Christopher J , Al Khourdajie Alaa , Yang Pu , Folini Doris 
Published: August 2023

Abstract
Emissions pathways used in climate policy analysis are often derived from integrated assessment models. However, such emissions pathways do not typically include climate feedbacks on socioeconomic systems and by extension do not consider climate uncertainty in their construction. We use a well-known cost-benefit integrated assessment model, the Dynamic Integrated Climate-Economy (DICE) model, with its climate component replaced by the Finite-amplitude Impulse Response (FaIR) model (v2.1). The climate uncertainty in FaIR is sampled with an ensemble that is consistent with historically observed climate and Intergovernmental Panel on Climate Change (IPCC) assessed ranges of key climate variables such as equilibrium climate sensitivity (ECS). By varying discounting assumptions, three scenarios are produced: a pathway similar to the ‘optimal welfare’ scenario of DICE that has similar warming outcomes to current policies, and pathways that limit warming to ‘well-below’ 2

∘

C

and 1.5

∘

C

with a short-term overshoot, aiming to meet Paris Agreement long-term temperature goals. Climate uncertainty alone is responsible for a factor of five variation (5%–95% range) in the social cost of carbon (SCC) in the 1.5

∘

C

overshoot scenario, with the spread in SCC increasing in relative terms with increasing stringency of climate target. CO2 emissions trajectories resulting from the optimal level of emissions abatement in all pathways are also sensitive to climate uncertainty, with 2050 emissions ranging from −12 to +14 GtCO2 yr−1 in the 1.5

∘

C

scenario. ECS and the strength of present-day aerosol effective radiative forcing are strong determinants of SCC and mid-century CO2 emissions. This shows that narrowing climate uncertainty leads to more refined estimates for the social cost of carbon and provides more certainty about the optimal rate of emissions abatement. Including climate and climate uncertainty in integrated assessment model derived emissions scenarios would address a key missing feedback in scenario construction.
Preparation of Titanium Dioxide Nanoparticles Films by Air-Compressed Spray Method for Supercapacitor Device
Authors: Tunhoo Benchapol , Apiruktatarn Kanyawee , Onlaor Korakot , Thiwawong Thutiyaporn 
Published: August 2023

Abstract
In this work, the titanium dioxide (TiO2) nanoparticle films were prepared from commercial Degussa P25-TiO2 nanoparticles by air-compressed spray. The properties of the prepared films were characterized by X-ray diffraction, FTIR, Raman spectroscopy, and scanning electron microscope, respectively. In addition, the prepared TiO2 film on a metal substrate (stainless steel, SS304) was used to fabricate the electrode of the electrochemical supercapacitor device. The electrochemical storage performance of the fabricated device was obtained by cyclic voltammetry measurement in aqueous 1 M Na2SO4 with an Ag/AgCl reference electrode (platinum as a counter electrode). The optimum specific capacitance of the fabricated device was found at 3.014 F/g at a scan rate of 50 mV/s.
Preparation and Characterization of Ag Nanoparticle/SiO2 Composite for Optical Application
Authors: Manivong Aphisit , Mekprasart Wanichaya , Boonyarattanakalin Kanokthip , Chongsri Krisana , Pecharapa Wisanu 
Published: August 2023

Abstract
A facile method to fabricate volumetric light-guide plate (LGP) by micro-nanostructure of Ag/SiO2 composite was presented in this study. The Ag NPs/SiO2 composite were first prepared from a mixture of yellowish-brown Ag NPs colloids and SiO2 powder by electrostatic self-assembly method. Its chemical and physical properties were investigated by scanning transmission microscopy (TEM), scanning electron microscopy (SEM) and Energy dispersive X-ray spectroscopy (EDX). The small particles (Ag/SiO2 composite) in the LGP could produce multiple scattering effect with different size of micro-nanoparticles, which could improve the light-scattering performance of the LGP. The effective light scattering of the material into LGP was obtained from the increased amount of the particles. Meanwhile, surface plasmon resonance effect of Ag NPs also improves the performance of LGP, which could be further applied to design device in optical application. In additional, optical properties and light scattering mechanism of the Ag NPs/SiO2 composite in the LGP are discussed and presented.
Effect of pH of Ganoderma lucidum aqueous extract on green synthesis of silver nanoparticles
Authors: Smirnov Oleksandr , Dzhagan Volodymyr , Yeshchenko Oleg , Kovalenko Mariia , Kapush Olga , Vuichyk Mykola , Dzhagan Veronika , Mazur Nazar , Kalynovskyi Vitalii , Skoryk Mykola , Yukhymchuk Volodymyr 
Published: August 2023

Abstract
Perspective applications of Ag nanoparticles (NPs) stimulate intense research on their affordable synthesis, including green routes. The use of fungi extracts has advantages over other organisms, because of their relatively easy isolation and higher efficiency in a reduction of metal ions and NP formation. Here we report mycosynthesis of AgNPs based on Ganoderma lucidum fruit body extract with different pH values as the bioreducing and stabilising agent. Stable NPs colloids with distinct plasmonic resonance peaking at 408–418 nm are obtained by using photoreduction in a broad pH range (5 to 11). Synthesis efficiency drops only at very acidic conditions, pH = 2.5. The NP size and morphology are studied by dynamic light scattering and scanning electron microscopy. An analysis of FTIR spectra of pure analyte and NP sample indicates that stabilisation of the AgNPs by the components of Ganoderma lucidum extract may take place via forming chemical bonds with the NP surface. These bio-friendly AgNPs are both optically and chemically active, as inferred from surface-enhanced Raman scattering of a standard dye analytes and charge transfer-induced quenching of the photoluminescence of both dye and inorganic NPs, and can be studied for various applications which require direct access to the AgNP surface.
Analyzing the role of Ni dopant to change the structural, optical and photocatalytic properties of SnO2 nanoparticles
Authors: Pradhan Lipan , Rout Shantinarayan , Parida Bipin Kumar , Mohapatra S R , Sakthivel R , Behera Debadhyan 
Published: August 2023

Abstract
SnO2 and 5 wt% Ni doped SnO2 nanoparticles (SnO2:Ni NPs) were successfully synthesised by a template-free hydrothermal method. X-ray diffraction (XRD) patterns depicted polycrystalline nature of the NPs in rutile-type cassiterite phase with dominant (110) and (101) Bragg diffraction peaks. Rietveld refinement of XRD patterns supported single phase tetragonal crystal structure having space group P42/m n m. With Ni doping, crystallite size of NPs decreased from 39 nm to 35 nm whereas lattice strain increased from 3.56 × 10−3 to 3.99 × 10−3. This is attributed to the substitution of Sn4+ ion by Ni2+ ions. The morphology of the SnO2 NPs also changed from regular spherical shape to elongated irregular shape upon Ni doping. The dominant Raman peak obtained at 634 cm−1 matched with the signature peak for rutile SnO2 (Raman A1g mode). Further, we observed disappearance of E

g
mode due to Ni doping, which indicated the formation of oxygen vacancies. Also, XPS analysis indicated an increase of oxygen vacancy concentration in the doped NPs due to charge imbalance between Sn4+ and Ni2+. The direct optical band gap of SnO2 increased from 3.97 eV to 4.11 eV when doped with 5 wt% Ni and it is ascribed to Burstein–Moss effect. Irrespective of higher optical band gap of SnO2:Ni NPs, they showed enhanced photocatalytic activity to degrade Rhodamine B (RhB) dye molecules under UV-visible irradiation. The first order kinetic reaction rate constants for degradation of RhB were found to be 0.014 min−1 and 0.045 min−1 in case of SnO2 and SnO2:Ni NPs respectively. The enhanced photocatalytic activity in SnO2:Ni NPs is explained by relating to the formation of more oxygen vacancies and chemisorptions of O2 and H2O molecules followed by generation of radicals. This work demonstrates the superiority of SnO2:Ni NPs for use as photocatalytic material for industrial waste water treatment.
The effect of heat input in multi-pass GMAW of S960QL UHSS based on weaving and stringer bead procedure on microstructure and mechanical properties of HAZ
Authors: Mert Tolga , Gurol Ugur , Tumer Mustafa 
Published: August 2023

Abstract
Quenched and tempered S960QL (yield strength ≥ 960 MPa) ultra-high strength steel (UHSS) thick plates were joined by multi-pass robotic gas metal arc welding (GMAW) using weaving and stringer bead techniques. The effects of microstructural changes in heat-affected zone (HAZ) of the joint on toughness and hardness were examined. Weaving and stringer bead techniques applied for the multi-pass welding procedure altered average peak temperatures and exposure time to those temperatures. Mechanical properties of HAZs were evaluated by utilizing notch impact and hardness tests, and these results were correlated with microstructural characterizations using optical (OM) and scanning electron microscopes (SEM). Prior austenite grain (PAG) coarsening occurred because of increased exposure time to peak temperature in coarse-grained HAZ (CGHAZ) of the W-5 (weaving pass) joint. CGHAZs at the face pass, which have not been subjected to a second thermal cycle, have the highest hardness in both joints. Hardness of SCHAZ and CGHAZ of S-12 joint was 7% and 1% higher compared with W-5 joint, respectively. Weld metal hardness of W-5 joint was 15% lower than that of S-12 joint. Both joints not only fulfilled the requirements of minimum 50 J per EN ISO 10025-6 at −20 °C but exceeded this limit by 50% (W-5) and 200% (S-12). Lateral expansions for impact toughness specimens were around 17.5% for S-12 joint, whereas it was 4% for W-5 joint. Since HAZ in the S-12 (stringer bead) joint is narrow compared with the one in the W-5 joint, impact toughness values were higher with the S-12 joint due to the locations of the notches of the impact specimens.
Synthesis of uniformly ordered single-crystalline TiO2 nanorods on flexible Ti substrate
Authors: Zhong Dong , Zheng Mingxi 
Published: August 2023

Abstract
With high conductivity and ionic mobility, one-dimensional (1D) titanium dioxide (TiO2) nanoarrays have attracted great interest in the photoanode of solar cells and the cathode of lithium-ion/sodium-ion batteries. However, the synthesis of TiO2 nanoarrays often requires highly toxic or corrosive solution circumstances. Meanwhile, the case that most TiO2 nanoarrays are grown on rigid conductive substrate can’t satisfy the demand for application in the space of complex shape. In this work, we developed a simple method to prepare highly ordered single crystalline rutile TiO2 nanorod arrays with high density on flexible metal Ti substrate in relatively weak acid solution, and the orientation of the nanorods were more uniform than those by other means ever before. Based on detailed analysis, an ion-assisted and concentration-limited epitaxial growth mechanism by H+ and

SO

4

2
−

is proposed for this uniformly ordered rutile TiO2 nanorod array. This work can promote the research and application of flexible solar cells and lithium-ion batteries, especially their integration.
Nano-based drug delivery system for therapeutics: a comprehensive review
Authors: Prakash Satyendra 
Published: August 2023

Abstract
Nanomedicine and nano-delivery systems hold unlimited potential in the developing sciences, where nanoscale carriers are employed to efficiently deliver therapeutic drugs at specifically targeted sites in a controlled manner, imparting several advantages concerning improved efficacy and minimizing adverse drug reactions. These nano-delivery systems target-oriented delivery of drugs with precision at several site-specific, with mild toxicity, prolonged circulation time, high solubility, and long retention time in the biological system, which circumvent the problems associated with the conventional delivery approach. Recently, nanocarriers such as dendrimers, liposomes, nanotubes, and nanoparticles have been extensively investigated through structural characteristics, size manipulation, and selective diagnosis through disease imaging molecules, which are very effective and introduce a new paradigm shift in drugs. In this review, the use of nanomedicines in drug delivery has been demonstrated in treating various diseases with significant advances and applications in different fields. In addition, this review discusses the current challenges and future directions for research in these promising fields as well.
Green synthesis of highly luminescent CsPbBr3 quantum dots through silica coating and post-treatment with ligands
Authors: Wu Xianyao , Yin Kai , Yang Mu , Hu Yufeng , Peng Hongshang 
Published: August 2023

Abstract
All-inorganic perovskite quantum dots (PQDs) have received considerable attentions due to their fascinating optical properties. However, the mainstream preparation methods rely on toxic solvents, raising significant environmental and safety concerns. In this work, ethyl acetate was chosen as the environmentally friendly anti-solvent to prepare silica-coated CsPbBr3 PQDs using a aminoalkoxysilane-assisted reprecipitation–encapsulation method. The as-prepared PQDs exhibited tunable emission wavelength and a medium photoluminescence quantum yield (PLQY) of 60%. Furthermore, CsPbBr3 PQDs were post-treated by different ligands with the aim of passivating surface defects. This strategy successfully mitigated the presence of surface defects and minimized non-radiative recombination losses in PQDs. As a result, the PLQY (achieving up to 73%) and stability of PQDs were substantially enhanced (luminescence stability improved by about 40% under the same test conditions). Our studies offer a novel approach for the environmentally friendly large-scale production of PQDs, opening up new possibilities for their practical applications.
Predicting the sheet resistance of laser-induced graphitic carbon using machine learning
Authors: Le Hung , Minhas-Khan Aamir , Nambi Suresh , Grau Gerd , Shen Wen , Wu Dazhong 
Published: August 2023

Abstract
While laser-induced graphitic carbon (LIGC) has been used to fabricate cost-effective conductive carbon on flexible substrates for applications such as sensors and energy storage devices, predicting the resistance of the component fabricated via LIGC remains challenging. In this study, a two-step machine learning-based modeling framework is developed to predict the sheet resistance of the materials fabricated using LIGC. The two-step modeling framework consists of classification and regression. First, random forest (RF) is used to classify successful and failed trials. Second, extreme gradient boosting (XGBoost), RF, support vector machine with radial basis function, multivariate adaptive spline regression, and multilayer perceptron are used to predict the sheet resistance in each successful trial. In addition, an analysis of the change in sheet resistance with respect to laser energy per unit area is conducted to remove data points with high sheet resistance. XGBoost is also used to determine the importance of each process parameter. We demonstrate the modeling framework on datasets collected from experiments where LIGC lines (1D) and LIGC squares (2D) are engraved. For the 1D dataset, the RF classification model achieves a 95% accuracy. For both 1D and 2D datasets, a comparative study shows that XGBoost outperforms other algorithms. XGBoost predicts the sheet resistance of the LIGC lines and squares with a MAPE of 7.08% and 8.75%, respectively. XGBoost also identifies laser resolution as the most significant parameter. Moreover, experimental results show that models built on the dataset merging the 1D and 2D datasets result in lower prediction accuracy than those built on the 1D and 2D datasets separately. The modeling framework allows one to determine the sheet resistance of LIGC with varying laser processing conditions without conducting expensive and time-consuming experiments.
One-year retrospective analysis of anti-FXa apixaban and rivaroxaban levels demonstrates utility for management decisions in various urgent and nonurgent clinical situations
Authors: Lucas Fabienne , Lewis Joshua , Grandoni Jessica , Sylvester Katelyn W , Bernier Thomas D , Ting Clara , Sek Rebecca , Ballard Kathleen , Connors Jean M , Battinelli Elisabeth M 
Published: August 2023

Abstract

Objectives
Quantification of direct oral anticoagulant (DOAC) plasma levels can guide clinical management, but insight into clinical scenarios surrounding DOAC-calibrated anti-FXa assays is limited.

Methods
Apixaban- and rivaroxaban-calibrated chromogenic anti-Xa assays performed over a 1-year period were retrospectively analyzed. Patient demographics, DOAC history, concomitant medications, and renal/liver comorbidities were obtained. Indications for testing and associated clinical actions were reviewed. Machine learning (ML) models predicting clinical actions were evaluated.

Results
In total, 371 anti-FXa apixaban and 89 anti-FXa rivaroxaban tests were performed for 259 and 67 patients in recurring urgent (acute bleeding, unplanned procedures) and nonurgent situations, including several scenarios not captured by existing testing recommendations (eg, drug monitoring, recurrent thromboembolic events, bleeding tendency). In urgent settings, andexanet reversal was guided by radiologic and clinical findings over DOAC levels in 14 of 32 instances, while 51% of apixaban patients qualified for nonreversal strategies through the availability of levels. Levels also informed procedure/intervention timing and supported management decisions when DOAC clearance or DOAC target levels were in question. The importance of clinical context was emphasized by exploratory ML models predicting particular clinical actions.

Conclusions
Although clinical situations are complex, DOAC testing facilitates clinical decision-making, including reversal, justifying more widespread implementation of these assays.
Hurdles and opportunities in implementing marine biosecurity systems in data-poor regions
Authors: Carvalho Susana , Shchepanik Hailey , Aylagas Eva , Berumen Michael L , Costa Filipe O , Costello Mark John , Duarte Sofia , Ferrario Jasmine , Floerl Oliver , Heinle Moritz , Katsanevakis Stelios , Marchini Agnese , Olenin Sergej , Pearman John K , Peixoto Raquel S , Rabaoui Lotfi J , Ruiz Greg , Srėbalienė Greta , Therriault Thomas W , Vieira Pedro E , Zaiko Anastasija 
Published: August 2023

Abstract
Managing marine nonindigenous species (mNIS) is challenging, because marine environments are highly connected, allowing the dispersal of species across large spatial scales, including geopolitical borders. Cross-border inconsistencies in biosecurity management can promote the spread of mNIS across geopolitical borders, and incursions often go unnoticed or unreported. Collaborative surveillance programs can enhance the early detection of mNIS, when response may still be possible, and can foster capacity building around a common threat. Regional or international databases curated for mNIS can inform local monitoring programs and can foster real-time information exchange on mNIS of concern. When combined, local species reference libraries, publicly available mNIS databases, and predictive modeling can facilitate the development of biosecurity programs in regions lacking baseline data. Biosecurity programs should be practical, feasible, cost-effective, mainly focused on prevention and early detection, and be built on the collaboration and coordination of government, nongovernment organizations, stakeholders, and local citizens for a rapid response.
FedEVCP: Federated Learning-Based Anomalies Detection for Electric Vehicle Charging Pile
Authors: Lin Zhaoliang , Li Jinguo 
Published: August 2023

Abstract
Vehicle-to-Grid (V2G) is a technology that enables electric vehicles to use smart charging methods to harness low-cost and renewable energy when it is available, and obtain income by feeding energy back into the grid. With the rise of V2G technology, the use of electric vehicles has begun to increase dramatically, which relies on the reliable Electric Vehicle Charging Pile (EVCP). However, most EVCPs are online and networked, introducing many potential network threats, such as Electricity Theft, Identity Theft and False Data Injection etc. Prior work has mostly focused on machine learning, which is not able to effectively capture the relationships and structures in network traffic, making it difficult to deal with the propagation and infection of the novel network attacks. Moreover, most neural network models collect and transfer data from EVCPs to the central server for training, which makes the central server attractive to attackers. It poses a serious threat to user privacy. To address these issues, propose an anomaly detection model that incorporates Federated Learning and Deep Autoencoder, which can increase the amount and diversity of data used to train deep learning models without compromising privacy. The proposed model forms a layer-by-layer unsupervised representation learning algorithm by autoencoder stacking, while batch normalization of hidden layers accelerates the convergence of the model to avoid overfitting and local optima, and introduces an attention mechanism to enhance key features of sequences composed of data vectors to improve the accuracy rate. To prevent the risk of user privacy leakage on the central server, EVCP is allowed to retain local data for model training and send model parameters to the central server for constructing new global models. Experimental results show that the proposed scheme achieves improved detection accuracy with superior performance than other similar models.