Additional Step Research Articles

Study on selective emitter fabrication through an innovative pre‐diffusion process for enhanced efficiency in TOPCon solar cells

AbstractTOPCon (tunnel oxide passivated contact) solar cell is the mainstream high‐efficiency crystalline silicon solar cell structure. However, the lack of efficient passivation contact mechanisms on the front surface restricts the electrical performance ability to improve further. Selective emitter (SE) technology, considered a potential solution, needs to be more mature. This work provides a unique thermal pre‐diffusion approach combined with laser treatment and post‐oxidation annealing to create SE structures in TOPCon solar cells. Times for the high‐temperature process are equivalent to those for a traditional homogenous emitter. The innovative thermal pre‐diffusion process created a unique boron doping profile, achieving a high surface concentration of nearly 1 × 1020 cm−3 with a shallow junction depth of approximately 0.25 μm. Laser treatment further activated boron and facilitated its diffusion, influenced by the boron silicate glass layer and surface boron atoms. Adjustments were made to improve the pre‐diffusion recipe, including an additional boron deposition step, increasing non‐activated boron atoms. Introducing larger pyramidal microstructures also improved the junction depth and surface concentration in the heavily doped region. Compared to homogeneous emitters, the SE structures exhibited lower surface concentration in the lightly doped region, reducing the recombination current density in the passivation region J0,pass values. The SE structures achieved higher junction depths, limiting metal atom diffusion and reducing the current recombination density in the metal contact region J0,metal values. The contact resistivity between metal and silicon was also decreased. Overall, introducing SE structures resulted in a batch‐average efficiency improvement of 0.26%, reaching an average efficiency of 25.22% for TOPCon solar cells, and has industrial mass‐producible.

RAFT Polymerisation by the Radical Decarboxylation of Carboxylic Acids.

The controlled grafting of polymers from small- and macro-molecular substrates is an essential process for many advanced polymer applications. This usually requires the pre-functionalisation of substrates with an appropriate functional group, such as a RAFT agent or ATRP initiator, which requires additional synthetic steps. In this paper, we describe the direct grafting of RAFT polymers from carboxylate containing small molecules and polymers via photochemical radical decarboxylation. This method utilises the innate functional groups present in the substrates, and achieves efficient polymer initiation in a single step with excellent control of molecular weight and dispersity.

An Automated Capillary Electrophoresis Based Method for Drug Release Profiling of Liposomal Doxorubicin

Liposomal doxorubicin hydrochloride is an antineoplastic agent widely used against human cancers. The data from in vitro drug release test (IVRT) is essential for quality and/or bioequivalence evaluation in drug approval and post-approval regulation of liposomal drug products. However, most of the currently available IVRT methods for liposomal doxorubicin hydrochloride have experimental deficiencies associated with liposomal rupture during the separation process which is needed for selective quantification of released drug from liposomal-bound drug. In addition, many of the methods are time consuming, requiring bulk quantities of liposomal drug product, and lack of automation. We have developed a selective, sensitive, and automated capillary electrophoresis (CE)-based IVRT method, measuring released doxorubicin without additional sampling and separation steps. This method requires a small volume of sample compared to currently available methods. The IVRT release study with liposomal doxorubicin was conducted at different temperatures and pH conditions. It was observed that the release profiles obtained for five formulations including the reference listed drug were similar at pH 6.50 and 47.0 °C. The drug release increased with the increase of media pH and temperature. Complete doxorubicin release (100 %) was obtained in 7 h at pH 6.50 and 47.0 °C, and in less than 3 h at pH 6.50 and 52.0 °C. This CE-based method can be extended for determination of the IVRT profiling of other liposomal drug products.

RAFT Polymerisation by the Radical Decarboxylation of Carboxylic Acids**

AbstractThe controlled grafting of polymers from small‐ and macro‐molecular substrates is an essential process for many advanced polymer applications. This usually requires the pre‐functionalisation of substrates with an appropriate functional group, such as a RAFT agent or ATRP initiator, which requires additional synthetic steps. In this paper, we describe the direct grafting of RAFT polymers from carboxylate containing small molecules and polymers via photochemical radical decarboxylation. This method utilises the innate functional groups present in the substrates, and achieves efficient polymer initiation in a single step with excellent control of molecular weight and dispersity.

Dephosphorylation Switch DNAzyme-RCA Circuit: A Robust Strategy for the Homogeneous and Reliable Detection of FTO Demethylase.

Fat mass and obesity-associated protein (FTO) plays a crucial role in regulating the dynamic modification of N6-methyladenosine (m6A) in eukaryotic mRNA. Sensitive detection of the FTO level and efficient evaluation of the FTO demethylase activity are of great importance to early cancer diagnosis and anticancer drug discovery, which are currently challenged by limited sensitivity/precision and low throughput. Herein, a robust strategy based on the dephosphorylation switch DNAzyme-rolling circle amplification (RCA) circuit, termed DSD-RCA, is developed for highly sensitive detection of FTO and inhibitor screening. Initially, the catalytic activity of DNAzyme is silenced by engineering with an m6A modification in its catalytic core. Only in the presence of target FTO can the methyl group on DNAzyme be eliminated, resulting in the activation of the catalytic activity of DNAzyme and thus cleaving the hairpin substrate to release numerous primers. Different from the conventional methods that use the downstream cleavage primer with the original 3'-hydroxyl end directly as the RCA primer with the problem of high background signal, which should be compensated by additional separation and wash steps in heterogeneous format, our DSD-RCA assay uses the upstream cleavage primer with a 2',3'-cyclic phosphate terminus at the 3'-end serving as an intrinsically blocked 3' end. Only after a dephosphorylation reaction mediated by T4 polynucleotide kinase can the upstream cleavage primers with a resultant 3'-hydroxyl end be extended by RCA. With the high signal-to-noise ratio and homogeneous property, the proposed platform can sensitively detect FTO with a limit of detection of 31.4 pM, and the relative standard deviations (RSDs %) ranging from 0.8 to 2.0% were much lower than the heterogeneous methods. The DSD-RCA method was applied for analyzing FTO in cytoplasmic lysates from different cell lines and tissues of breast cancer patients and further used for screening FTO inhibitors without the need for separation or cleaning, providing an opportunity for achieving high throughput and demonstrating the potential applications of this strategy in disease diagnostics, drug discovery, and biological applications.

Social media and nonprofit fundraising: the influence of Facebook likes

PurposeThis paper aims to demonstrate that Facebook likes affect outcomes in nonprofit settings. Specifically, Facebook likes influence affinity to nonprofits, which, in turn, affects fundraising outcomes.Design/methodology/approachThe authors report three studies that establish that relationship. To examine social contagion, Study 1 – an auction field study – relies on selling artwork created by underprivileged youth. To isolate signaling, Study 2 manipulates the number of total Facebook likes on a page. To isolate commitment escalation, Study 3 manipulates whether a participant clicks a Facebook like.FindingsThe results show that Facebook likes increase willingness to contribute in nonprofit settings and that the process goes through affinity, as well as through Facebook impressions and bidding intensity. The total number of Facebook likes has a direct signaling effect and an indirect social contagion effect.Research limitations/implicationsThe effectiveness of the proposed mechanisms is limited to nonprofit settings and only applies to short-term effects.Practical implicationsFacebook likes serve as both a quality signal and a commitment mechanism. The magnitude of commitment escalation is larger, and the relationship is moderated by familiarity with the organization. Managers should target Facebook likes at those less familiar with the organization and should prioritize getting a potential donor to leave a like as a step leading to donation, in essence mapping a donor journey from prospective to active, where Facebook likes play an essential role in the journey. In a charity auction setting, the donor journey involves an additional step of bidder intensity.Social implicationsThe approach the authors study is shown effective in nonprofit settings but does not appear to extend to corporate social responsibility more broadly.Originality/valueTo the best of the authors’ knowledge, this study is the first investigation to map Facebook likes to a seller’s journey through signals and commitment, as well as the only investigation to map Facebook likes to charity auctions and show the effectiveness of this in the field.

Efficient VOC removal over grape-like GdCoO3 perovskite prepared from the viscous mixture

Grape-like GdCoO3 perovskite was synthesized through a straightforward one-step method. In this process, the combined raw materials were lightly dampened with a minute quantity of water to form a viscous state and subsequently subjected to a single-step calcination, resulting in the formation of the perovskite structure, all without the need for additional procedural steps. The characterization results indicated the presence of abundant reactive oxygen species distributed on the surface of GdCoO3. Most notably, the catalyst prepared in this manner exhibited remarkable catalytic activity for VOC oxidation.

Mechanistic Study of B(C6F5)3-Catalyzed Transfer Hydrogenation of Aldehydes/Ketones with PhSiH3 and Stoichiometric Water.

A DFT study was performed on the mechanisms of B(C6F5)3-catalyzed transfer hydrogenation of aldehydes/ketones, using PhSiH3 and stoichiometric water. Path B2 includes a stepwise Piers SN2-Si process, H- transfer, and hydrolysis desilylation of siloxane, in which the hydrolysis desilylation step is rate-determining. Path C1 is first determined, involving a B(C6F5)3-catalyzed concerted addition step of 2H2O to carbonyl generating R1R2C(OH)2, a subsequent SN2-Si dehydroxylation step of R1R2C(OH)2 giving R1R2C=OH+ and (C6F5)3B-H-, and final H- transfer producing the respective alcohol R1R2CHOH. A B(C6F5)3-catalyzed H2 generation process (Path H0) is determined. Path B2 is the only mechanism for the stepwise method. Using a one-time one-pot feeding method, alkyl/aryl aldehydes, dialkyl ketones, and alkyl aryl ketones (1a-g) can be reduced into alcohols chemoselectively and effectively at room temperature. More than 1 equiv of water over substrates is necessary. Herein, Path C1 is the dominant transfer hydrogenation pathway, and the H2 generation is efficiently inhibited, by the competitive advantage of Path C1 and initial dominant existence of the complexes IM0 and IM1-x. The diaryl ketones (1h,1i) cannot be efficiently reduced into the respective alcohols using the one-time feeding one-pot method. The barriers of C-TS1-h/i are obviously higher than those of C-TS1-a-g, attributed to the electron-donating and space effects of the two aryls on carbonyl C. The possible Paths B2 and C1 of transfer hydrogenation have no competitive advantage with Path H0. The DFT results are consistent with the experiments.

Research on efficient detection network method for remote sensing images based on self attention mechanism

Remote sensing images are widely used in aerial drones, satellites, and other fields. However, traditional object detection methods face challenges of low efficiency and accuracy due to the diverse scales, numerous target types, and complex backgrounds of remote sensing images. To address these issues, this paper proposes a remote sensing image detection network based on self-attention, which adaptively learns the relative importance of pixels to achieve more precise and efficient object detection in remote sensing images, meeting the demands of large-scale remote sensing image processing. Firstly, through end-to-end training, the object detection network is optimized as a whole to directly output detection results from the input remote sensing images, eliminating the need for additional intermediate steps. This not only reduces the impact of information loss and inconsistency but also simplifies the entire detection process. Secondly, we integrate the Faster R-CNN architecture as the foundation, combining region extraction and object classification into a unified process. Lastly, we embed self-attention mechanisms at different levels of the Faster R-CNN to progressively extract multi-scale and multi-level feature information, enhancing the network's ability to learn the correlation of information from different positions in the image, automatically capturing the relationships between objects, and improving the accuracy of object detection. This significantly reduces redundant computation, making it more efficient for large-scale remote sensing image processing. Experimental verification demonstrates that this approach outperforms traditional methods in terms of detection accuracy and efficiency, better addressing the particularities of remote sensing images, and providing an efficient and precise solution for aerial drone and satellite image processing. Remote sensing image detection has become one of the research hotspots in the field of remote sensing, holding significant theoretical significance and practical application value.

Circuit implementation of on-chip trainable spiking neural network using CMOS based memristive STDP synapses and LIF neurons

Computation on a large volume of data at high speed and low power requires energy-efficient architectures for edge computing applications. As a result, scientists focus on memristive circuits and systems for area and energy efficiency. Spiking neural network (SNN) with bio-inspired spike-timing-dependent plasticity learning (STDP) is a promising solution for energy-efficient neuromorphic systems than conventional artificial neural network (ANN). Previous works on SNN with STDP learning primarily use memristor macro models, which are software-based and cannot give complete insight into circuit implementation challenges. Some reported works on SNN use memristive devices, which require additional fabrication steps. This article presents a full circuit-level implementation of the SNN system featuring on-chip training and classification using memristive STDP synapse in standard CMOS technology. A new learning rule using the modified STDP is implemented to simplify the weight modification process. It does not involve FPGAs, CPUs, or GPUs to train the neural network. The approach used in this paper to modify the weights does not require any additional combinational or digital circuits attached to the memristive synapse resulting in less consumption of area, energy and time. We demonstrated the complete circuit-level design, implementation and simulation of SNN with on-chip training and pattern classification using 180 nm CMOS technology. A comprehensive comparison of the proposed SNN circuit with the previous related work is also presented. To demonstrate the versatility of the CMOS synapse circuit for application scenarios requiring rate-based learning, we have tuned the pair-based STDP circuit to obtain Bienenstock–Cooper–Munro (BCM) characteristics and applied it to heart rate classification.

Loss-guided stability selection

AbstractIn modern data analysis, sparse model selection becomes inevitable once the number of predictor variables is very high. It is well-known that model selection procedures like the Lasso or Boosting tend to overfit on real data. The celebrated Stability Selection overcomes these weaknesses by aggregating models, based on subsamples of the training data, followed by choosing a stable predictor set which is usually much sparser than the predictor sets from the raw models. The standard Stability Selection is based on a global criterion, namely the per-family error rate, while additionally requiring expert knowledge to suitably configure the hyperparameters. Model selection depends on the loss function, i.e., predictor sets selected w.r.t. some particular loss function differ from those selected w.r.t. some other loss function. Therefore, we propose a Stability Selection variant which respects the chosen loss function via an additional validation step based on out-of-sample validation data, optionally enhanced with an exhaustive search strategy. Our Stability Selection variants are widely applicable and user-friendly. Moreover, our Stability Selection variants can avoid the issue of severe underfitting, which affects the original Stability Selection for noisy high-dimensional data, so our priority is not to avoid false positives at all costs but to result in a sparse stable model with which one can make predictions. Experiments where we consider both regression and binary classification with Boosting as model selection algorithm reveal a significant precision improvement compared to raw Boosting models while not suffering from any of the mentioned issues of the original Stability Selection.

Prediction of residual strain/stress validated with neutron diffraction method for wire-feed hybrid additive/subtractive manufacturing

Hybrid manufacturing integrates additive manufacturing (AM) and subtractive manufacturing in the same machine. The integration of the two processes enables the creation of complex geometry, further reduces material waste, and decreases lead time. Moreover, this in-envelop method enables an interleaved printing strategy in which the part is partially AM printed and then machined repeatedly. This method is not available in either traditional manufacturing or AM technologies. Nevertheless, measuring and controlling of distortion and residual stress are still challenging due to highly varying process conditions and limited access to the machine during hybrid additive/subtractive manufacturing processes. Moreover, the residual stress measurement is ex-situ process, so it only gives information at the final stage of stress evolution. Therefore, it’s nearly impossible to know the AM induced residual stress distribution before the machining process in hybrid additive/subtractive process. In this prospective, numerical modeling can be an effective tools to overcome those challenges. However, the modelling/simulation methods for fully-coupled additive and subtractive operation has not reported to the best of our knowledge. In this work, we developed a comprehensive simulation method using finite element method (FEM) for fully-coupled additive and subtractive manufacturing process. The results were validated with strain measurements by neutron diffraction in two different machining conditions: hot-machining and cold-machining. The validated model was used to investigate how machining affects part distortion and evolution of residual strain/stress during fabrication. Alternating tensile-compression longitudinal (ε11, x-direction) strains and stresses along the build direction showed the physical interaction of additive and subtractive steps in the interleaved strategy. The alternating pattern was found in both the model and neutron diffraction measurements. This work’s findings can be used to develop strategies to control of part distortion and residual stresses in the hybrid additive/subtractive manufacturing process.

Collision-Induced Unfolding, Tandem MS, Bottom-up Proteomics, and Interactomics for Identification of Protein Complexes in Native Surface Mass Spectrometry.

Endogenously occurring salts and nonvolatile matrix components in untreated biological surfaces can suppress protein ionization and promote adduct formation, challenging protein identification. Characterization of labile proteins within biological specimens is particularly demanding because additional purification or sample treatment steps can be time-intensive and can disrupt noncovalent interactions. It is demonstrated that the combined use of collision-induced unfolding, tandem mass spectrometry, and bottom-up proteomics improves protein characterization in native surface mass spectrometry (NSMS). This multiprong analysis is achieved by acquiring NSMS, MS/MS, ion mobility (IM), and bottom-up proteomics data from a single surface extracted sample. The validity of this multiprong approach was confirmed by the successful characterization of nine surface-deposited proteins, with molecular weights ranging from 8 to 147 kDa, in two separate mixtures. Bottom-up proteomics provided a list of proteins to match against observed proteins in NSMS and their detected subunits in tandem MS. The method was applied to characterize endogenous proteins from untreated chicken liver samples. The subcapsular liver sampling for NSMS analysis allowed for the detection of endogenous proteins with molecular weights of up to ∼220 kDa. Moreover, using IM-MS, collision cross sections and collision-induced unfolding pathways of enzymatic proteins and protein complexes of up to 145 kDa were obtained.

Edge lithography based on aluminum dry etching

Traditional nanolithography methods, such as electron beam or ion beam lithography, can be expensive and slow, limiting their applications. Edge lithography offers a promising alternative for efficiently and effectively creating nanoscale patterns using lower-cost lithography equipment with higher throughput. Our paper presents a new edge lithography technique to pattern fine structures with coarse patterns utilizing aluminum plasma dry etching without thin film deposition. The aluminum oxide layer generated on the sidewall of the Al structure during the etching process defines the final nanostructures. Our experiments show that this layer is formed through the oxidation of the aluminum layer itself, providing a simple and practical approach to creating complex nanostructures without additional steps or materials. In addition, using the non-switching pseudo-Bosch etching process, we transferred the nano-edge pattern formed in aluminum oxide into the silicon substrate. Our technique allows for cost-effective and efficient nanoscale patterning for various applications.

Improvement in the Thermomechanical Properties and Adhesion of Wood Fibers to the Polyamide 6 Matrix by Sequential Ball Milling Technique

Improvement in the Thermomechanical Properties and Adhesion of Wood Fibers to the Polyamide 6 Matrix by Sequential Ball Milling Technique

Daily Physical Activity Measured by Wearable Smartwatch for Patients Undergoing Transcatheter Aortic Valve Replacement: Insights From the SMART TAVR Study.

This study aims to evaluate limited data about daily physical activity patterns, influential factors, and their association with 1-year mortality or rehospitalization after transcatheter aortic valve replacement (TAVR) through smartwatches. Consecutive severe aortic stenosis patients undergoing elective transfemoral TAVR in a Chinese tertiary hospital were enrolled from July 2021 to May 2022 and received a Huawei smartwatch at least 1 day before TAVR. The primary outcome was a composite of all-cause mortality or hospital readmission within 1 year. Linear mixed-effects models were applied to determine influential factors of daily step counts, and Cox proportional hazard regression models were to estimate the association between baseline step counts within 1 month since discharge and composite outcome from months 2 to 12. The dose-response association was assessed using restricted cubic spline curves. A total of 222 participants and 59 469 valid monitoring person-day records were included (mean age, 72.7 years; 61% women). Step counts increased rapidly within the first 2 months (P<0.001), followed by a slower increase for those without composite outcomes (P=0.029) and a gradual decrease for those who developed composite outcomes (P<0.001). In multivariate linear mixed models, a 1-m increase in baseline 6-minute walk test and a 1-month delay after discharge were associated with 4 (95% CI, 1-7) and 170 (95% CI, 145-194) additional step counts, respectively. In restricted cubic spline analysis, the hazard ratio declined progressively until ≈5000 steps per day, after which they leveled. Below 5000 steps, the adjusted hazard ratio of composite outcome associated with each 1000-step count increase was 0.67 (0.50-0.89; P=0.007). However, above 5000 steps, step counts were not significantly associated with the composite outcome (P=0.645), with a hazard ratio of 1.12 (0.70-1.79). Daily step counts rapidly increased within the first 2 months post-TAVR. Increased physical activity was associated with a lower risk of 1-year mortality or rehospitalization after TAVR for patients with daily step counts below 5000. URL: https://www.clinicaltrials.gov; Unique identifier: NCT04454177.

A predictor-corrector phase unwrapping algorithm for temporally undersampled gradient-echo MRI.

To develop a method for unwrapping temporally undersampled and nonlinear gradient recalled echo (GRE) phase. Temporal unwrapping is performed as a sequential one step prediction of the echo phase, followed by a correction to the nearest integer wrap-count. A spatio-temporal extension of the 1D predictor corrector unwrapping (PCU) algorithm improves the prediction accuracy, and thereby maintains spatial continuity. The proposed method is evaluated using numerical phantom, physical phantom, and in vivo brain data at both 3 T and 9.4 T. The unwrapping performance is compared with the state-of-the-art temporal and spatial unwrapping algorithms, and the spatio-temporal iterative virtual-echo based Nyquist sampled (iVENyS) algorithm. Simulation results showed significant reduction in unwrapping errors at higher echoes compared with the state-of-the-art algorithms. Similar to the iVENyS algorithm, the PCU algorithm was able to generate spatially smooth phase images for in vivo data acquired at 3 T and 9.4 T, bypassing the use of additional spatial unwrapping step. A key advantage over iVENyS algorithm is the superior performance of PCU algorithm at higher echoes. PCU algorithm serves as a robust phase unwrapping method for temporally undersampled and nonlinear GRE phase, particularly in the presence of high field gradients.

Impact of saponification and silver-nitrate purification on lacustrine alkenone distributions and alkenone-based indices

A growing interest in lacustrine alkenones as a proxy for continental paleotemperature reconstructions accompanied important methodological improvements over the past two decades. New gas chromatography (GC) columns were used for alkenone analysis, that drastically improved alkenone separation, especially for freshwater lakes. However, these recent advances are sometimes not sufficient in separating compounds that interfere with alkenones in the resulting chromatograms and concurrently, new chemical procedures were implemented to further clean up the samples. Here we investigate the impact of two clean-up procedures, saponification and silver-nitrate purification, on alkenone distribution, alkenone quantification, and C37 alkenone-based indices, including the U37K index. The silver-nitrate purification modified the C37 alkenone distribution and thus the C37 alkenone-based indices, especially the U37K index, in 6 out of the 9 studied samples by further retaining alkenones with more double bonds. These changes would result on an average error of 3 °C in reconstructed temperatures. Saponification also influenced the C37 alkenone distribution mainly by removing co-eluting compounds, thereby improving the quality of the results. Both saponification and purification resulted in the reduction of the C37 alkenone concentration by almost half. Clean-up steps should thus be used carefully, paying particular attention to any change in alkenone distribution and concentration. Limiting the use of additional clean-up steps reduces the risk of modifying the alkenone distribution.

A test amplification bot for Pharo/Smalltalk

Test amplification exploits the knowledge embedded in an existing test suite to strengthen it. A typical test amplification technique transforms the initial tests into additional test methods that increase the mutation coverage. Although past research demonstrated the benefits, additional steps need to be taken to incorporate test amplifiers in the everyday workflow of developers. This paper describes a proof-of-concept bot integrating Small-Amp with GitHub-Actions. The bot decides for itself which tests to amplify and does so within a limited time budget. To integrate the bot into the GitHub-Actions workflow, we incorporate three special-purpose features: (i) prioritization (to fit the process within a given time budget), (ii) sharding (to split lengthy tests into smaller chunks), and (iii) sandboxing (to make the amplifier crash-resilient). We evaluate our approach by installing the proof-of-concept extension of Small-Amp on five open-source projects deployed on GitHub. Our results show that a test amplification bot is feasible at a project level by integrating it into the build system. Moreover, we quantify the impact of prioritization, sharding, and sandboxing so that other test amplifiers may benefit from these special-purpose features. Our proof-of-concept demonstrates that the entry barrier for adopting test amplification can be significantly lowered.

From the Chair

Documents to the People is coming up on the 5th anniversary of going open access, trying to live up to the meaning of our name, actually delivering documents to the people. In honor of that anniversary, we are looking to take an additional step in that journey and I want to talk a bit about why and what open access should mean to collection philosophy in the government documents world.