Pre-screening Method Research Articles

Effects of prescreening for likelihood ratio approaches in the forensic identification of source problems

Source prescreening is a methodology where forensic examiners select samples that are similar to given trace evidence to represent the background population. This background evidence helps assign a value of evidence using a likelihood ratio or Bayes factor. A potential benefit of prescreening is a mitigation of effects from subpopulation structures within the alternative source population by isolating the relevant subpopulation. This paper examines the impact of prescreening before assigning evidence value. Extensive simulations with synthetic and real data, including trace element and fingerprint score examples, were conducted. The findings indicate that when the subpopulation structure is known, prescreening can provide an accurate evidence value in cases of subpopulation structures. The study suggests that prescreening can be beneficial, but the prescreening method and level should be transparently reported alongside the evidence value.

Raman Microscopic Investigation of Organic Solvent Concentration Profiles in Proton Exchange Membranes and Their Permeability

Direct alcohol fuel cells (DAFCs) suffer from severe problems related to the employed polymer electrolyte membranes, such as fuel crossover, resulting in low faradaic efficiency and performance of the fuel cell.1,2,3 We introduce a 3D-printed diffusion cell setup, which is capable of determining the interaction of membranes and organic solvents via confocal Raman microscopy.4 Thus, the suitability of proton exchange membranes (PEMs) for usage in DAFCs can be evaluated. In this setup, the membrane is clamped between two flow channels, similar to the flow field plates of a fuel cell. The donor channel is filled with an organic solvent-water mixture, and the receptor channel is filled with water. Depth scans through the membrane and the two flow channels provide concentration profiles of the organic solvent within the membrane, which are used to calculate membrane properties such as permeability and sorption coefficient (see Fig. 1).In this study, the membrane-solvent interactions between standard unreinforced and composite PEMs and various organic solvent solutions, such as 2-propanol, acetone, and ethanol, are investigated. All membranes analyzed show a preference for organic solvent absorption from 1 M solutions, with sorption coefficients ranging from 2.5 to 3.0. In addition to the absorption, the organic solvents permeate through the membrane. The corresponding permeabilities range from 10-5 to 10-4 cm² s-1. However, the permeabilities of the membranes for different solvents do not scale with their sorption coefficients, in contrast to the membrane swelling.This diffusion cell setup is an accessible and fast pre-screening method, which can enhance the development and evaluation of new ion exchange membranes for use in DAFCs and related applications.

Assessing spatial distribution of bioindicator elements in various cutaneous tumors using correlative imaging with laser-ablation-based analytical methods

Correlative imaging of cutaneous tumors provides additional information to the standard histopathologic examination. However, the joint progress in the establishment of analytical techniques, such as Laser-Induced Breakdown Spectroscopy (LIBS) and Laser Ablation Inductively Coupled Plasma Mass Spectrometry (LA-ICP-MS) in clinical practice is still limited. Their combination provides complementary information as it is also shown in our study in terms of major biotic (Ca, Mg, and P) and trace (Cu and Zn) elements. To elucidate changes in the elemental composition in tumors, we have compiled a set of malignant tumors (Squamous Cell Carcinoma, Basal Cell Carcinoma, Malignant Melanoma, and Epithelioid Angiosarcoma), one benign tumor (Pigmented Nevus) and one healthy-skin sample. The data processing was based on a methodological pipeline involving binary image registration and affine transformation. Thus, our paper brings a feasibility study of a practical methodological concept that enables us to compare LIBS and LA-ICP-MS results despite the mutual spatial distortion of original elemental images. Moreover, we also show that LIBS could be a sufficient pre-screening method even for a larger number of samples according to the speed and reproducibility of the analyses. Whereas LA-ICP-MS could serve as a ground truth and reference technique for preselected samples.

Semi-rational design of nitroarene dioxygenase for catalytic ability toward 2,4-dichloronitrobenzene.

Rieske non-heme dioxygenase family enzymes play an important role in the aerobic biodegradation of nitroaromatic pollutants, but no active dioxygenases are available in nature for initial reactions in the degradation of many refractory pollutants like 2,4-dichloronitrobenzene (24DCNB). Here, we report the engineering of hotspots in 2,3-dichloronitrobenzene dioxygenase from Diaphorobacter sp. strain JS3051, achieved through molecular dynamic simulation analysis and site-directed mutagenesis, with the aim of enhancing its catalytic activity toward 24DCNB. The computationally predicted activity scores were largely consistent with the detected activities in wet experiments. Among them, the two most beneficial mutations (E204M and M248I) were obtained, and the combined mutant reached up to a 62-fold increase in activity toward 24DCNB, generating a single product, 3,5-dichlorocatechol, which is a naturally occurring compound. In silico analysis confirmed that residue 204 affected the substrate preference for meta-substituted nitroarenes, while residue 248 may influence substrate preference by interaction with residue 295. Overall, this study provides a framework for manipulating nitroarene dioxygenases using computational methods to address various nitroarene contamination problems.IMPORTANCEAs a result of human activities, various nitroaromatic pollutants continue to enter the biosphere with poor degradability, and dioxygenation is an important kickoff step to remove toxic nitro-groups and convert them into degradable products. The biodegradation of many nitroarenes has been reported over the decades; however, many others still lack corresponding enzymes to initiate their degradation. Although rieske non-heme dioxygenase family enzymes play extraordinarily important roles in the aerobic biodegradation of various nitroaromatic pollutants, prediction of their substrate specificity is difficult. This work greatly improved the catalytic activity of dioxygenase against 2,4-dichloronitrobenzene by computer-aided semi-rational design, paving a new way for the evolution strategy of nitroarene dioxygenase. This study highlights the potential for using enzyme structure-function information with computational pre-screening methods to rapidly tailor the catalytic functions of enzymes toward poorly biodegradable contaminants.

The Application of Fluorescence In Situ Hybridization in the Prescreening of Veronica Hybrids.

Fluorescence in situ hybridization (FISH), a molecular cytogenetic technique that enables the visualization and identification of specific DNA sequences within chromosomes, has emerged as a pivotal tool in plant breeding programs, particularly in the case of Veronica species. Veronica, a genus with a complex reproductive system, often poses challenges in accurately identifying hybrids because of its tendency to hybridize, which leads to intricate genetic variation. This study focused on the use of FISH as a prescreening method to identify true hybrids in Veronica breeding programs. FISH analysis was first performed on the parents to identify their 45S and 5S rDNA signals, along with their respective chromosome numbers. The signals were then compared with those of the twenty progenies with reference to their supposed parents. Five true hybrids, seven self-pollinated progenies, and eight false hybrids were identified through FISH. The findings highlight the significance of FISH as a screening method that contributes significantly to the efficiency of Veronica breeding programs by ensuring the preservation of desired genetic traits and minimizing the inadvertent inclusion of misidentified hybrids. To conclude, this study underscores the vital role of FISH in enhancing the precision and success of breeding programs and opens new avenues for improved breeding strategies and crop development.

Enhanced Detection of Estrogen-like Compounds by Genetically Engineered Yeast Sensor Strains.

The release of endocrine-disrupting compounds (EDCs) to the environment poses a health hazard to both humans and wildlife. EDCs can activate or inhibit endogenous endocrine functions by binding hormone receptors, leading to potentially adverse effects. Conventional analytical methods can detect EDCs at a high sensitivity and precision, but are blind to the biological activity of the detected compounds. To overcome this limitation, yeast-based bioassays have previously been developed as a pre-screening method, providing an effect-based overview of hormonal-disruptive activity within the sample prior to the application of analytical methods. These yeast biosensors express human endocrine-specific receptors, co-transfected with the relevant response element fused to the specific fluorescent protein reporter gene. We describe several molecular manipulations of the sensor/reporter circuit in a Saccharomyces cerevisiae bioreporter strain that have yielded an enhanced detection of estrogenic-like compounds. Improved responses were displayed both in liquid culture (96-well plate format) as well as in conjunction with sample separation using high-performance thin-layer chromatography (HPTLC). The latter approach allows for an assessment of the biological effect of individual sample components without the need for their chemical identification at the screening stage.

Abstract 7314: Technological and economic values of an AI-based pre-screening multi-cancer early detection (MCED) liquid-biopsy test as a companion system for a cancer screening solution

Abstract Multi-cancer early detection (MCED) liquid biopsy screening tests provide great promise for both first-time cancer detection, and recurrence monitoring applications. The prominent feature of asymptomatic cancer screening solutions is their demand for ultra-high specificity (e.g., circa 99.99%), while maintaining a reasonable sensitivity. However, the challenges of screening are not limited to these technical specifications. Among other challenges, psychological and economical barriers-to-entry can be mentioned. In this work, we discuss the value of adding a high-sensitivity liquid-biopsy prescreening system to a screening solution. Such a tandem system can retain the high specificity offered by the screening solution, while relieving some of the economical concerns in cancer screening, by providing an opportunity to forgo screening in individuals with negative prescreening results. In order to serve in a general multi-cancer scheme, the prescreening solution must be MCED in nature. While our screening solution is primarily single-cell genomics/proteomics-based, we adopted a predominantly proteomics-based solution for prescreening. A proteomics solution has the potential to be notably more economical due to its simplicity/maturity. This is in contrast to the relatively high cost of the ′omics-based screening methods. Nevertheless, proteomics-based solutions, historically, have suffered from limited sensitivity and low specificity. In order to attain a high sensitivity while maintaining a workable specificity, we have used a complex Artificial Intelligence (AI) based system, comprising machine learning (ML) and Expert Systems (ES). Our MCED blood-based prescreening solution has been successfully tested on a series of cancers, including (among others) the cancers of breast, lung, prostate, ovary, uterus, and cervix, with cancer signal detection sensitivity of ≈92% for early stages (Stage I or II). While a tandem prescreening-screening system is designed primarily for testing asymptomatic individuals for early detection of cancer, a prescreening method has other potential uses. For instance, it can be used for: 1) minimal residual disease (MRD) detection after treatment; 2) recurrence monitoring after achieving remission. In particular, in cases where the original cancer is aggressive and has a high chance of recurrence, e.g., triple-negative breast cancer (TNBC), such a prescreening test can be repeated several times in the course of a year, providing the patient with measurements that are affordable and near real-time. In conclusion, the introduced proteomics/AI-based prescreening system could provide an economical, yet high-sensitivity solution for general adoption, acting primarily as a molecular risk assessment tool, to be performed prior to using a high-specificity screening modality. Citation Format: Bahram G. Kermani. Technological and economic values of an AI-based pre-screening multi-cancer early detection (MCED) liquid-biopsy test as a companion system for a cancer screening solution [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7314.

SurfFlow: High-throughput surface energy calculations for arbitrary crystals

We introduce SurfFlow, an open-source high-throughput workflow package designed for automated first-principles calculations of surface energies in arbitrary crystals. Our package offers a comprehensive solution capable of handling multi-element crystals, nonstoichiometric compositions, and asymmetric slabs, for all potential terminations. To streamline the computational process, SurfFlow employs an efficient pre-screening method that discards surfaces with suspected high surface energy before conducting resource-intensive density functional theory computations. The results generated are seamlessly compiled into an optimade-compliant database, ensuring easy access and compatibility. Additionally, a user-friendly web interface facilitates workflow submission and management, provides result visualization, and enables the examination of Wulff shapes. SurfFlow represents a valuable tool for researchers looking to explore surface energies and their implications in a diverse range of systems.

A fresh perspective on infrared spectroscopy as a prescreening method for molecular and stable isotopes analyses on ancient human bones

Following the development of modern genome sequencing technologies, the investigation of museum osteological finds is increasingly informative and popular. Viable protocols to help preserve these collections from exceedingly invasive analyses, would allow greater access to the specimens for scientific research. The main aim of this work is to survey skeletal tissues, specifically petrous bones and roots of teeth, using infrared spectroscopy as a prescreening method to assess the bone quality for molecular analyses. This approach could overcome the major problem of identifying useful genetic material in archaeological bone collections without resorting to demanding, time consuming and expensive laboratory studies. A minimally invasive sampling of archaeological bones was developed and bone structural and compositional changes were examined, linking isotopic and genetic data to infrared spectra. The predictive model based on Infrared parameters is effective in determining the occurrence of ancient DNA (aDNA); however, the quality/quantity of aDNA cannot be determined because of the influence of environmental and local factors experienced by the examined bones during the burial period.

An Ensemble CNOP Method Based on a Pre-Screening Mechanism for Targeted Observations in the South China Sea

The uncertainty in the initial condition seriously affects the forecasting skill of numerical models. Targeted observations play an important role in reducing uncertainty in numerical prediction. The conditional nonlinear optimal perturbation (CNOP) method is a useful tool for studying adaptive observation. However, the traditional CNOP method highly relies on the adjoint model, and it is difficult to find the global optimal solution. In this paper, a pre-screening and ensemble CNOP hybrid method called PECNOP is proposed to identify optimal sensitive areas in targeted observations. PECNOP is an adjoint-free method that captures global CNOP with high probability, which can effectively solve the two major problems faced by traditional CNOP methods. We evaluated the performance of PECNOP by building an observation simulation system consisting of an ocean model and data assimilation. One of the assimilation experiments was dedicated to evaluating the stability and effectiveness of PECNOP in extreme events. The results show that, compared with traditional methods, PECNOP can stably capture the global CNOP. Extra observations and assimilation in the optimal sensitive areas identified by PECNOP can effectively improve forecasting by about 20% within 30 days. Therefore, PECNOP has potential to reduce the initial error of numerical models, which is important for improving forecasting.

Ntsm: an alignment-free, ultra-low-coverage, sequencing technology agnostic, intraspecies sample comparison tool for sample swap detection.

Due to human error, sample swapping in large cohort studies with heterogeneous data types (e.g., mix of Oxford Nanopore Technologies, Pacific Bioscience, Illumina data, etc.) remains a common issue plaguing large-scale studies. At present, all sample swapping detection methods require costly and unnecessary (e.g., if data are only used for genome assembly) alignment, positional sorting, and indexing of the data in order to compare similarly. As studies include more samples and new sequencing data types, robust quality control tools will become increasingly important. The similarity between samples can be determined using indexed k-mer sequence variants. To increase statistical power, we use coverage information on variant sites, calculating similarity using a likelihood ratio-based test. Per sample error rate, and coverage bias (i.e., missing sites) can also be estimated with this information, which can be used to determine if a spatially indexed principal component analysis (PCA)-based prescreening method can be used, which can greatly speed up analysis by preventing exhaustive all-to-all comparisons. Because this tool processes raw data, is faster than alignment, and can be used on very low-coverage data, it can save an immense degree of computational resources in standard quality control (QC) pipelines. It is robust enough to be used on different sequencing data types, important in studies that leverage the strengths of different sequencing technologies. In addition to its primary use case of sample swap detection, this method also provides information useful in QC, such as error rate and coverage bias, as well as population-level PCA ancestry analysis visualization.

Clinical researchers’ insights on key data for eligibility screening in clinical studies

Abstract Introduction: Clinical research is critical for healthcare advancement, but participant recruitment remains challenging. Clinical research professionals (CRPs; e.g., clinical research coordinator, research assistant) perform eligibility prescreening, ensuring adherence to study criteria while upholding scientific and ethical standards. This study investigates the key information CRP prioritizes during eligibility prescreening, providing insights to optimize data standardization, and recruitment approaches. Methods: We conducted a freelisting survey targeting 150 CRPs from diverse domains (i.e., neurological disorders, rare diseases, and other diseases) where they listed essential information they look for from medical records, participant/caregiver inquiries, and discussions with principal investigators to determine a potential participant’s research eligibility. We calculated the salience scores of listed items using Anthropac, followed by a two-level analytic procedure to classify and thematically categorize the data. Results: The majority of participants were female (81%), identified as White (44%) and as non-Hispanic (64.5%). The first-level analysis universally emphasized age, medication list, and medical history across all domains. The second-level analysis illuminated domain-specific approaches in information retrieval: for instance, history of present illness was notably significant in neurological disorders during participant and principal investigator inquiries, while research participation was distinctly salient in potential participant inquiries within the rare disease domain. Conclusion: This study unveils the intricacies of eligibility prescreening, with both universal and domain-specific methods observed. Variations in data use across domains suggest the need for tailored prescreening in clinical research. Incorporating these insights into CRP training and refining prescreening tools, combined with an ethical, participant-focused approach, can advance eligibility prescreening practices.

PseudoCell: Hard Negative Mining as Pseudo Labeling for Deep Learning-Based Centroblast Cell Detection.

Background: Deep learning models for patch classification in whole-slide images (WSIs) have shown promise in assisting follicular lymphoma grading. However, these models often require pathologists to identify centroblasts and manually provide refined labels for model optimization. Objective: To address this limitation, we propose PseudoCell, an object detection framework for automated centroblast detection in WSI, eliminating the need for extensive pathologist's refined labels. Methods: PseudoCell leverages a combination of pathologist-provided centroblast labels and pseudo-negative labels generated from undersampled false-positive predictions based on cell morphology features. This approach reduces the reliance on time-consuming manual annotations. Results: Our framework significantly reduces the workload for pathologists by accurately identifying and narrowing down areas of interest containing centroblasts. Depending on the confidence threshold, PseudoCell can eliminate 58.18-99.35% of irrelevant tissue areas on WSI, streamlining the diagnostic process. Conclusion: This study presents PseudoCell as a practical and efficient prescreening method for centroblast detection, eliminating the need for refined labels from pathologists. The discussion section provides detailed guidance for implementing PseudoCell in clinical practice.

Comparing the effectiveness of a brief observational study protocol versus a telephone call for Alzheimer’s disease prevention trial prescreening

AbstractBackgroundMany Alzheimer’s disease (AD) clinical trials suffer from a high screen‐fail rate despite telephone pre‐screening, leading to a significant drain of time and resources. Thus, more efficient prescreening methods are required. Here, we examined whether prescreening through a brief on‐site observational study protocol could reduce clinical trial screen failure rate compared to telephone prescreening.MethodDe‐identified screening data from the AHEAD study (AD prevention trial) at the Brigham and Women’s Hospital (BWH) Center for Alzheimer Research and Treatment were used. Participants were referred for the AHEAD study screen either from a brief observational study protocol (Davis Memory and Aging Cohort [MAC]) or a telephone screen. MAC collects medical history, Mini‐Mental State Exam, Quick Dementia Rating System, and blood samples through a 60‐minute on‐site visit. Telephone screen uses medical history survey and Telephone Interview for Cognitive Status. We compared AHEAD screen failure rate due to (1) clinical exclusion criteria and (2) (for those with no clinical exclusion) amyloid‐β biomarker criteria (plasma, PET) between MAC and telephone screen referrals, using Fisher’s exact test.ResultThere was no significant difference in age, sex, race, or ethnicity between the MAC and Telephone prescreen groups (Table 1, MAC n = 61, telephone n = 55). The odds ratio (OR) for clinical screen‐fail for MAC (n = 9 out of 61, 15%) compared to telephone (n = 20 out of 55, 36%) was 0.31 (95% CI 0.12 to 0.80, p = 9.7×10−3). The OR for biomarker screen‐fail for MAC (n = 49 out of remaining 52, 94%) compared to telephone (n = 27 out of remaining 35, 77%) was 4.7 (95% CI 1.0 to 30.1, p = 0.024). The OR for randomization from MAC (n = 3 out of 61, 5%) compared to telephone (n = 8 out of 55, 15%) was not significantly different from 1 (p = 0.11).ConclusionOur findings show that, compared to a telephone screen, an on‐site prescreen assessing cognition, medical history, and venous access may prevent clinical trial screen fails attributable to clinical exclusion criteria and save participant/staff time and resources. However, an unexplained higher biomarker screen fail rate for the on‐site group offset this benefit, requiring further investigation.

A drainage network-based impact matrix to support targeted blue-green-grey stormwater management solutions

Urban floods will continue to be an alarming issue worldwide due to climate change and urban expansion. The costly and less environmentally friendly grey infrastructure is not always the most adequate solution to resolve urban pluvial flooding issues. The combination of grey and blue-green infrastructures, also called hybrid infrastructure, has been considered a promising solution for urban stormwater management. Existing approaches for identifying suitable hybrid solutions frequently rely on global multi-objective optimization algorithms. We developed a pre-screening method that decomposes a drainage network into clusters of pipes connected to sub-catchments, based on pipe hydraulic characteristic that allows for the impact of infrastructure combinations (blue-green and grey) to be mapped. Four impact matrices are proposed to map the total, local, upstream, and downstream flood reduction of all possible blue-green, grey, and hybrid solutions. Using an urban catchment in Guangzhou (China) as a case study, results showed that such an exercise could identify prime candidate locations for blue-green and grey infrastructure while filtering out ineffective locations for flood reduction. Furthermore, the impact matrices enabled the identification of flood zones where blue-green infrastructure could handle flood mitigation without the need of local grey infrastructure upgrades. As such, they are not only useful for quick screening of suitable interventions for each flooded zone, but can also potentially serve as a priori knowledge before diving into the data and computationally expensive process of finding the most effective flood mitigation solutions.

Abstract 17673: Sudden Cardiac Death as the First Manifestation of Heart Disease

Background: Clinical risk stratification is most challenging in the subgroup of individuals who present with sudden cardiac death (SCD) as the first manifestation of heart disease (SCD-FM) and there is a lack of current era population-based data. Hypothesis: Occurrence of SCD-FM SCD differs by ethnicity and access to health care. Methods: Consecutive out-of-hospital cases of SCD attended by emergency medical services were ascertained prospectively from a large ongoing population-based study in the Southwest US (population ~ 850K, 2015-2022) using an established multi-source ascertainment and adjudication process. A detailed review was conducted of the lifetime clinical record and available autopsy data, for each individual. SCD-FM cases were defined as SCD with no prior heart disease diagnosis. We calculated annual age-standardized incidence of overall SCD and SCD-FM per 100,000 residents using US census data. Ethnicity-stratified incidence of SCD-FM was calculated. Occurrence of all health care visits prior to SCD was evaluated. Results: From a total of 2681 SCD cases (age 71 ± 16, female 35%) age adjusted incidence of SCD was 40[38-41] per 100,000 out of which SCD-FM was 15[14-16] per 100,000 (n=1019 (38%), age 67±18, female 33%). There was no difference in SCD-FM incidence between Hispanic and non-Hispanic residents (15 [13-17] vs 15 [14-17], p=0.84). Individuals younger than 65 yr were more likely to have SCD-FM compared to those older than 65 yr (50% vs 33%, p <0.001). We observed that 91% of SCD-FM had visited healthcare providers and 3% had cardiac evaluations performed. Conclusion: A significantly higher proportion of SCD-FM were age <65 but the proportion of SCD-FM did not differ by ethnicity. The vast majority of SCD-FM (91%) underwent health care visits prior to SCD but cardiac evaluations were rare (3%). These findings highlight the need to discover and develop user-friendly, cost-effective pre-screening methods to reduce SCD-FM in the general population.

Novel Structural Aspects of Heavy-Crude-Derived Asphaltene Molecules for Investigating the Crude Mix Processability in Refinery Operation

Summary In this paper, we investigate the role of asphaltenes derived from heavy crudes, which dictates the behavior of crude mix properties for hassle-free downstream refinery operation. Combined characterization techniques such as proton nuclear magnetic resonance (1H-NMR), cross-polarization magic-angle-spinning carbon-13 (CP/MAS 13C)-NMR, heteronuclear single-quantum coherence (HSQC), Fourier transform infrared (FTIR), thermogravimetric analysis (TGA), and X-ray diffraction (XRD) are used for the detailted study of Ratwai and Ras Gharib (RG)-derived asphaltenes to validate their structural role in selecting the optimal crude mix. As per our investigation, when the polyaromatic core of asphaltene structures are less substituted, the availability of aromatic hydrogen is more; it exhibits a stable crude mix as compared to heavy crudes that have more aromatic core substitution, despite the crudes possessing similar asphaltene content and physicochemical properties. This finding is further extended to West Canadian (WC) and Belayim (BL) heavy crudes for operational suitability. In this study, the key feature is to develop a CP/MAS 13C-NMR-based robust and quick characterization technique that could potentially become a prescreening method to assess crude oil compatibility and its various blend processability in the refinery system. Other characterization techniques, such as 1H-NMR, HSQC, FTIR, TGA, and XRD, would corroborate and confirm the reliability of the data obtained by CP/MAS 13C-NMR.

Preferred method and performance evaluation of heterogeneous composite phase change material (CPCM) wallboard in different seasons

The application of phase change materials (PCMs) in building envelopes has been developed in past decades and many strategies have been adopted to improve the heat resistance and capacity performance. However, rare studies focused on optimizing these characteristics of PCM wallboard in different seasons and providing preferred method for selecting the optimal PCMs. This study constructed a heterogeneous wall acting in different seasons and developed a pre-screening method of PCMs based on the analytic hierarchy process. CA-HD/EG with higher thermal conductivity (2.16 W/(m·K)) and lower melting point (25.4 °C) acted as the inner layer, and paraffin/porous silica with lower thermal conductivity (0.37 W/(m·K)) and higher melting point (29.3 °C) acted as the outer layer. Two experimental boxes were built to compare the thermal performance of CPCM and gypsum wallboard. The decrement factor in winter increased by 6.7 %, and the heat storage coefficient in summer increased by 0.97. The thermal comfort duration in winter was extended by 17.9 %, and the thermal load leveling in summer was reduced by 11.5 %. The heating/cooling water supply duration was shortened by 2.5 h, resulting in an energy-saving rate of 13.3 %. Consequently, the CPCM wallboard maintained a better indoor thermal environment with a lower energy consumption.

Hybrid Prediction-Driven High-Throughput Sustainability Screening for Advancing Waste-to-Dimethyl Ether Valorization.

Assessing the prospective climate preservation potential of novel, innovative, but immature chemical production techniques is limited by the high number of process synthesis options and the lack of reliable, high-throughput quantitative sustainability pre-screening methods. This study presents the sequential use of data-driven hybrid prediction (ANN-RSM-DOM) to streamline waste-to-dimethyl ether (DME) upcycling using a set of sustainability criteria. Artificial neural networks (ANNs) are developed to generate in silico waste valorization experimental results and ex-ante model the operating space of biorefineries applying the organic fraction of municipal solid waste (OFMSW) and sewage sludge (SS). Aspen Plus process flowsheeting and ANN simulations are postprocessed using the response surface methodology (RSM) and desirability optimization method (DOM) to improve the in-depth mechanistic understanding of environmental systems and identify the most benign configurations. The hybrid prediction highlights the importance of targeted waste selection based on elemental composition and the need to design waste-specific DME synthesis to improve techno-economic and environmental performances. The developed framework reveals plant configurations with concurrent climate benefits (-1.241 and -2.128 kg CO2-eq (kg DME)-1) and low DME production costs (0.382 and 0.492 € (kg DME)-1) using OFMSW and SS feedstocks. Overall, the multi-scale explorative hybrid prediction facilitates early stage process synthesis, assists in the design of block units with nonlinear characteristics, resolves the simultaneous analysis of qualitative and quantitative variables, and enables the high-throughput sustainability screening of low technological readiness level processes.

Predicting the physical and chemical properties of sustainable aviation fuels using elastic-net-regularized linear models based on extended-wavelength FTIR spectra

A Fourier transform infrared (FTIR) spectra-based prescreening approach was developed for estimating nine important physical and chemical properties of sustainable aviation fuels (SAFs): molecular weight (MW), hydrogen-to-carbon (H/C) ratio, density, net heat of combustion (NHC), derived cetane number (DCN), threshold sooting index (TSI), initial boiling point (IBP), flash point, and kinematic viscosity (KV). A training dataset containing the vapor-phase FTIR absorption spectra of pure hydrocarbons spanning four molecular classes, blends of neat hydrocarbons, conventional jet fuels and SAFs, across the 2–15.38 μm wavelength range, was compiled. The physical and chemical properties of the fuels in the training dataset were sourced either from experimental data reported in the literature or calculated using relevant property blending correlations. Elastic-net-regularized linear models were trained for each property by optimizing the model parameters using a cross-validated grid search. The results from these models were compared with the results from previous models (Lasso-regularized linear models developed previously by Wang et al. at Stanford), which were trained on FTIR absorption spectra across the limited wavelength range of 3.3–3.55 μm. Use of the extended wavelength range and the new model-parameter optimization strategy results in significant improvement in predictive performance of the current models compared to the previous models for all nine properties. The model performance is also evaluated on three candidate SAFs. The current models are found to achieve higher property prediction accuracy than the previous models for these test fuels. Overall, this work demonstrates the utility of predictive models based on extended-wavelength FTIR spectra and their potential use as a low-volume prescreening method for characterizing properties of new sustainable aviation fuel candidates.