Printed Circuit Boards Samples Research Articles

Design of Deep Learning Techniques for PCBs Defect Detecting System based on YOLOv10

As Printed Circuit Boards (PCBs) are critical components in electronic products, their quality inspection is crucial. This study focuses on quality inspection to detect PCB defects using deep learning techniques. Traditional widely used quality control methods are time-consuming, labor-intensive, and prone to human errors, making the manufacturing process inefficient. This study proposes a deep-learning approach using YOLOv10. Through the incorporation of architectural improvements such as CSPNet and PANet that improve feature extraction and fusion, as well as a dual assignments mechanism that increases localization accuracy, YOLOv10 offers significant improvements over earlier versions, such as YOLOv5 and YOLOv8, and Faster R-CNN models. These innovations allow YOLOv10 to deliver superior performance in terms of both speed and precision. The experiments used a custom dataset consisting of 1,260 PCB samples collected from the industry. The dataset was partitioned into 80% for model training and 20% for testing. The model was trained for 100 epochs with a batch size of 32 to evaluate its performance in identifying various PCB defects. YOLOv10, with its optimized architecture, fully utilized its capabilities while requiring less computational power than YOLOv5 and YOLOv8, especially in resource-constrained environments. Despite resource constraints, YOLOv10 achieved high accuracy, with a precision of at least 96% and a recall of 97%, surpassing earlier YOLO models and Faster R-CNN. It also achieved 99% mAP and more than 96% F1 score. These improvements in speed and accuracy make YOLOv10 a highly efficient solution for automated PCB inspection, reducing manual effort and offering fast and accurate classification adaptable to various applications.

Elemental Analysis of Discarded Printed Circuit Boards using Different Analytical Techniques and Recovery of Silver

The rapid advancement of technology and the demands of our consumer-driven society significantly contribute to the growing volume of e-waste generated annually. This category includes all discarded components and products from electronic and electrical equipment that are not intended for reuse. Among these, Printed Circuit Boards (PCBs) are essential elements in all electrical and electronic devices and are difficult to reuse or recycle considering the complex structure of PCB. This study focuses on analyzing PCBs for their elemental content and the recovery of precious metal, silver. Elemental concentration was analysed using Neutron Activation Analysis (NAA), nano-colorimetry, potentiometry and Atomic Absorption Spectrometry(AAS) techniques .The findings reveal that Non -metallic part in analysed PCBs is about 80-84 % while among metallic concentrations, copper concentration was found to be highest (42.88-90.80 mg/g) and that of lead lowest (0.26-0.75mg/g).The total amount of silver in three PCB samples is approximately 2 mg/g, with a recovery rate was about 66%. However, the fourth sample contains a lower total amount of silver, with a recovery rate of 52.79%

Using deep learning to automatic inspection system of printed circuit board in manufacturing industry under the internet of things

Industry 4.0 is currently the goal of many factories, promoting manufacturing factories and sustainable operation. Automated Optical Inspection (AOI) is a part of automation. Products in the production line are usually inspected visually by operators. Due to human fatigue and inconsistent standards, product inspections still have defects. In this study, the sample component assembly printed circuit board (PCB), PCB provided by the company was tested for surface components. The types of defects on the surface of the PCB include missing parts, multiple parts, and wrong parts. At present, the company is still using visual inspection by operators, the PCB surface components are more complex. In order to reduce labor costs and save the development time required for different printed circuit boards. In the proposed method, we use digital image processing, positioning correction algorithm, and deep learning YOLO for identification, and use 450 images and 10500 components of the PCB samples. The result and contribution of this paper shows the total image recognition rate is 92% and the total component recognition rate reaches 99%, and they are effective. It could use on PCB for different light, different color backplanes, and different material numbers, and the detection compatibility reaches 98%.

Distributed Activation Energy Modeling and Py-GC/MS Studies on Pyrolysis of Different Printed Circuit Boards for Resource Recovery.

Printed circuit boards (PCBs) constitute an important segment of electronic waste that can be effectively utilized to recover valuable metals and organics. The present work is focused on the kinetics and product distribution from pyrolysis of three different PCB samples, viz., television PCB (TV PCB), motherboard PCB (MB PCB), and hard disk PCB (HD PCB). The PCBs were pretreated to eliminate most of the metallic constituents. Kinetic analysis was performed using Vyazovkin’s isoconversional method and distributed activation energy model (DAEM). The average apparent activation energies obtained from the Vyazovkin method were 207.2, 158.9, and 179.7 kJ mol–1 for the TV PCB, MB PCB, and HD PCB, respectively. The DAEM with five, four, and four pseudo-components was used to describe the decomposition kinetics of the TV PCB, MB PCB, and HD PCB, respectively. Importantly, two types of distributions, viz., Gaussian and Weibull, were utilized to effectively model the nonisothermal data obtained from thermogravimetric analysis at 10 and 20 °C min–1. The evolution of pyrolysates belonging to functional groups such as phenolics, aromatics, aliphatics, halogenated compounds, N-containing compounds, and oxygenates was studied at two different temperatures (500 and 700 °C) using analytical pyrolysis–gas chromatograph/mass spectrometry (Py-GC/MS). The Py-GC/MS results demonstrated an increase in selectivity to aromatics and straight-chain aliphatics at 700 °C with a concomitant decrease in selectivity to phenols and oxygenates.

Chemical and Microbial Leaching of Valuable Metals from PCBs and Tantalum Capacitors of Spent Mobile Phones.

We compared chemical and microbial leaching for multi-metal extraction from printed circuit boards (PCBs) and tantalum capacitor scrap. A mixed consortium of acidophiles and heterotrophic fungal strains were used in the experiments and compared to chemical leaching using specific acids (sulfuric, citric and oxalic acids). Under optimum conditions, 100% extraction efficiency of Cu, and nearly 85% of Zn, Fe, Al and Ni were achieved from PCB and tantalum capacitor scrap samples using sulfuric acid. The mixed consortium of acidophiles successfully mobilized, Ni and Cu (99% and 96%, respectively) while Fe, Zn, Al and Mn reached an extraction yield of 89, 77, 70 and 43%, respectively, from the PCB samples. For the tantalum capacitor samples, acidophiles mobilized 92% Cu, 88% Ni, 78% Fe, 77% Al, 70% Zn and 57% Mn. Metal mobilization from PCBs and tantalum capacitor scrap by A. niger filtrate showed efficient solubilization of Cu, Fe, Al, Mn, Ni, Pb and Zn at an efficiency of 52, 29, 75, 5, 61, 21 and 35% from PCB samples and 61, 25, 69, 23, 68, 15 and 45% from tantalum capacitor samples, respectively. Microbial leaching proved viable as a method to extract base metals but was less specific for tantalum and precious metals in electronic waste. The implications of these results for further processing of waste electronic and electrical equipment (WEEE) are considered in potential hybrid treatment strategies.

A Comparison of Methods for the Characterisation of Waste-Printed Circuit Boards

Electronic waste is a growing waste stream globally. With 54.6 million tons generated in 2019 worldwide and with an estimated value of USD 57 billion, it is often referred to as an urban mine. Printed circuit boards (PCBs) are a major component of electronic waste and are increasingly considered as a secondary resource for value recovery due to their high precious and base metals content. PCBs are highly heterogeneous and can vary significantly in composition depending on the original function. Currently, there are no standard methods for the characterisation of PCBs that could provide information relevant to value recovery operations. In this study, two pre-treatments, smelting and ashing of PCB samples, were investigated to determine the effect on PCB characterisation. In addition, to determine the effect of particle size and element-specific effects on the characterisation of PCBs, samples were processed using four different analytical methods. These included multi-acid digestion followed by inductively coupled plasma optical emission spectrometry (ICP-OES) analysis, nitric acid digestion followed by X-ray fluorescence (XRF) analysis, multi-acid digestion followed by fusion digestion and analysis using ICP-OES, and microwave-assisted multi-acid digestion followed by ICP-OES analysis. In addition, a mixed-metal standard was created to serve as a reference material to determine the accuracy of the various analytical methods. Smelting and ashing were examined as potential pre-treatments before analytical characterisation. Smelting was found to reduce the accuracy of further analysis due to the volatilisation of some metal species at high temperatures. Ashing was found to be a viable pre-treatment. Of the four analytical methods, microwave-assisted multi-acid digestion offered the most precision and accuracy. It was found that the selection of analytical methods can significantly affect the accuracy of the observed metal content of PCBs, highlighting the need for a standardised method and the use of certified reference material.

Chemical inspection and elemental analysis of electronic waste using data fusion - Application of complementary spectroanalytical techniques

This study is focused on the development of analytical methods for characterization of printed circuit boards (PCBs) from mobile phones by direct analysis using three complementary spectroanalytical techniques: laser ablation-inductively coupled plasma-mass spectrometry (LA-ICP-MS), laser-induced breakdown spectroscopy (LIBS), and micro X-ray fluorescence spectroscopy (micro-XRF). These techniques were combined with principal component analysis (PCA) to investigate the chemical composition on the surface and depth profiling of PCB samples. The spatial distribution of important base metals (e.g. Al, Au, Ba, Cu, Fe, Mg, Ni, Zn), toxic elements (e.g. Cd, Cr, Pb) as well as the non-metallic fraction (e.g. P, S and Si) from conductive tracks, solder mask and integrated components were detected within the PCB samples. Univariate and multivariate approaches were also performed to obtain calibration models for Cu determination. The results were compared to reference concentrations obtained by inductively coupled plasma-optical emission spectrometry (ICP-OES) after microwave-assisted acid leaching using aqua regia. To this end, two PCB samples (50 × 34 mm2) were cut into small parts of 40 subsamples (10 × 8.5 mm2) and analyzed by ICP-OES and the Cu concentrations ranged from 13 to 45% m m−1. Partial least squares (PLS) regression was used to data fusion of analytical information from LIBS and micro-XRF analysis. The proposed calibration methods for LIBS and micro-XRF were tested for the 40 PCB subsamples, in which the best results were obtained combining both data sources though a low-level data fusion. Root mean square error of cross validation (RMSEC) and recoveries were 3.23% m m−1 and 81–119% using leave-one-out cross validation.

Direct determination of Al and Pb in waste printed circuit boards (PCB) by laser-induced breakdown spectroscopy (LIBS): Evaluation of calibration strategies and economic - environmental questions

Matrix-matching calibration (MMC), two-point calibration transfer (TP CT), one-point and multi-line calibration (OP MLC), single-sample calibration (SSC) and calibration free (CF) were evaluated in order to overcome matrix effects in laser-induced breakdown spectroscopy (LIBS). These calibration strategies were evaluated for direct determination of Al and Pb in waste printed circuit boards (PCB) using direct solids analysis by LIBS. Each strategy has limitations and advantages of its implementation, for the correction of matrix effects, so that it allows elementary determination with adequate accuracy. The MMC and CF proved to be excellent calibration strategies for the determination of strategic (Al) and toxic (Pb) elements by LIBS, with good recoveries (ranging from 80–120%) and low relative standard deviation (RSD%) values. A detailed discussion of the advantages and limitations of each of these five calibration strategies evaluated for LIBS is presented in this study. Lead concentrations in waste PCB samples are 5–12 times higher than established by Directive 2011/65/EU, and the samples analyzed contain between 3 and 55 g kg−1 Al, being an interesting economic and recycling source for this metal.

High-resolution continuum source graphite furnace atomic absorption spectrometry determination of ecotoxic and precious metals in printed circuit boards of waste mobile phones after selective sorption

Waste printed circuit boards (PCBs) contain high amounts of valuable precious metals (PMs) as well as ecotoxic elements (TE). Recycling waste PCBs enables the recovery of precious metals minimize harm to the environment caused by discarded electronics.This study established a benign and effective process for selective recovery of Au, Pt, Pd and ecotoxic elements such as As, Cd, Cr, Pb, Sb from waste PCBs. Pre-treating waste PCBs with a mixture of HCl and H2O2 in autoclave was fast and efficient decomposition. In order to recover PMs and TE from obtained PCB solutions, two sorbents were synthesized. 0.1 M HCl is sufficient for extraction of 96–100 % of TE and 3 M HCl is used for extraction of 100 % of PMs from PCB-solutions. The developed approach of transferring analyte into a solution is characterized as highly productive and make it possible to avoid any impact of matrices upon selectivity extraction and to determine analyte with high accuracy.The concentrations of analytes in five PCB samples were found in range of 10−6 - 10-1 % wt by HR-CS GFAAS method. HR-CS-GFAAS limits of determination and upper limits of analyte content determination were (μg·mL-1) 0.0008 and 0.1 (Au); 0.0008 and 0.1 (Pd); 0.001 and 0.8 (Pt); 0.0005 and 0.3 (As); 0.00006 and 0.01 (Cd); 0.0002 and 0.03 (Cr); 0.0005 and 0.1 (Pb); 0.0005 and 0.25 (Sb).The obtained data will be useful for the development of processes for the recycling of analytes from waste mobile phones.

An adaptive clustering-based genetic algorithm for the dual-gantry pick-and-place machine optimization

This research proposes an adaptive clustering-based genetic algorithm (ACGA) to optimize the pick-and-place operation of a dual-gantry component placement machine, which has two independent gantries that alternately place components onto a printed circuit board (PCB). The proposed optimization problem consists of several highly interrelated sub-problems, such as component allocation, nozzle and feeder setups, pick-and-place sequences, etc. In the proposed ACGA, the nozzle and component allocation decisions are made before the evolutionary search of a genetic algorithm to improve the algorithm efficiency. First, the nozzle allocation problem is modeled as a nonlinear integer programming problem and solved by a search-based heuristic that minimizes the total number of the dual-gantry cycles. Then, an adaptive clustering approach is developed to allocate components to each gantry cycle by evaluating the gantry traveling distances over the PCB and the component feeders. Numerical experiments compare the proposed ACGA to another clustering-based genetic algorithm LCO and a heuristic algorithm mPhase in the literature using 30 industrial PCB samples. The experiment results show that the proposed ACGA algorithm reduces the total gantry moving distance by 5.71% and 4.07% on average compared to the LCO and mPhase algorithms, respectively.

Drill-Path Optimization with Time Limit and Thermal Protection

The drilling of the printed circuit board (PCB) is a critical process due to occupying a third of the total PCB production time. This study integrates the K-nearest neighbor (K-NN) algorithm and the bi-objective multi-population genetic algorithm (BMGA) for deriving a near-optimal drilling path to reduce the drilling time, to protect product from overheating, and to balance the loading of the drill axes. Firstly, the holing coordinates are parsed from a CNC drilling program, clustered with the K-NN algorithm, which divides the global holes into several areas with local holes. Next, the local holes are classified and reconnected using 1-NN algorithm and BMGA, respectively. The BMGA fitness function is adjusted to eliminate overheating and unbalancing through drill jumping to ensure product quality. Finally, the near-optimal drilling paths can be derived within the time limit by checking a stable factor. The practical values of BMGA have already been demonstrated in 20 PCB samples. By including the thermal protection and the load balancing constraints and resolving the optimal path within an acceptable time limit, i.e. 30 min, this method can reduce the drilling length by an average of 15.5% compared to the original drilling length if the number of holes to be drilled is less than 5k. For cases with the number of holes between 5k and 20k, the average length of the drilling paths can be reduced to 25.6% of the original length. Moreover, the thermal protection maintains the samples with mean yield 99.8%.

Space Dependence of Divided Ground Patterns on FM-Band Cross-Talk Characteristics between Two Parallel Signal Traces on Printed Circuit Boards for Vehicles

Electromagnetic disturbances in vehicle-mounted FM radios are mainly caused by conducted noise currents flowing through wiring-harnesses from printed circuit boards (PCBs) with the slits of ground patterns. To suppress these kinds of noise currents from PCBs, we previously performed the FDTD simulation using eight simple two-layer PCB models in which the ground patterns were divided into two parts with different widths, which revealed that the cross-talk cannot always be reduced by widening the width of divided ground patters. In the present study, to confirm this finding, we made actual PCB samples, having the same geometry as that described above, and measured cross-talks between the two parallel signal traces with respect to the different widths of ground patterns. As a result, we confirmed that the measured results agree with the FDTD simulation, and also that the cross-talks have the smallest values at specific spaces between the divided ground patterns. In order to clarify the latter result, we calculated cross-talks from a simplified equivalent circuit model consisting of inductances, which were derived from their geometrical mean distance for the traces and ground patterns, and found that the circuit model can qualitatively explain the dependence of ground pattern width on cross-talks. The minimum reduction mechanism was found to come from the fact that a specific width of the ground pattern provides the smallest interlinking magnetic flux between the trace and the ground pattern.

Biolixiviação de cobre de sucata eletrônica

O presente trabalho investigou a influência da suplementação de ferro e da adaptação bacteriana sobre a biolixiviação de cobre de placas de circuito impresso usando a bactéria Acidithiobacillusferrooxidans-LR. Placas de circuito impresso de computadores obsoletos foram mecanicamente processadas através de cominuição seguida de separação magnética. A bactéria Acidithiobacillusferrooxidans-LR foi crescida e adaptada à presença de amostras de placas de circuito impresso cominuídas. Um estudo de frascos agitados foi realizado usando um shaker a 30ºC e 185 rpm. Os parâmetros analisados foram pH, concentração de íon ferroso e concentração de metais (ICP-OES). Os resultados demonstram que o processo de adaptação bacteriana aumentou a taxa de extração de cobre. A biolixiviação com Acidithiobacillusferrooxidans-LR adaptada lixiviou 56% do cobre das placas de circuito impresso (fração não-magnética) no meio suplementado pelo íon ferroso após 30 dias.

Reducing Solder Paste Inspection in Surface-Mount Assembly Through Mahalanobis–Taguchi Analysis

Increased functional density and reduced input/output (I/O) spacing are the market trends in the electronics manufacturing industry. Industry reports indicate that approximately 50%-70% of soldering defects are attributed to the solder paste printing process for printed circuit board (PCB) assembly. Hence, after the printing process, a solder paste inspection (SPI) system is generally used to examine the amount of solder paste deposition. Effective selection of components and bonding pads during solder inspection is extremely important in achieving desired process cycle times and ensuring assembly yield. This paper uses the Mahalanobis-Taguchi system to establish a systematic approach to determining guidelines for solder paste inspection. Among a total of 203 bonding pads on the board for a GPS product, the optimal model suggests that the solder deposition of 121 bonding pads be inspected. The reduction ratio is 40.4%, and the feasibility of the proposed model is verified. Also, for those bonding pads to be inspected for their solder paste deposition, this study uses empirical data to define the specifications to effectively distinguish acceptable PCB samples from defective. The threshold is within the 100% capability for judgment of solder paste printing quality in the surface mount assembly process.

Misalignment inspection of multilayer PCBs with an automated X-ray machine vision system

In recent times, multilayer printed circuit boards (PCBs) have been extensively applied in the electronics industry owing to their high capacities for complex and densely packed circuit layouts arranged in a limited space. The inspection of fabricated multilayer PCBs is thus important in order to ensure quality control and improve the fabrication process. In this paper, an automated X-ray machine vision system was developed exclusively for the inspection of the layer-to-layer misalignment of laminated multilayer PCBs. Based on a mechatronics system and X-ray image processing techniques, an automated misalignment inspection process was established. Experiments meant to inspect three critical layer-to-layer misalignment modes, expansion, contraction, and offset, found within ten-layer PCB samples, were conducted to test the feasibility of the developed machine inspection system. The experimental results show that the developed X-ray machine vision system, combined with the automated misalignment inspection process, was able to undertake misalignment inspection of certain multilayer PCBs.

Thiosulfate leaching of gold from waste mobile phones

The present communication deals with the leaching of gold from the printed circuit boards (PCBs) of waste mobile phones using an effective and less hazardous system, i.e., a copper-ammonia-thiosulfate solution, as an alternative to the conventional and toxic cyanide leaching of gold. The influence of thiosulfate, ammonia and copper sulfate concentrations on the leaching of gold from PCBs of waste mobile phones was investigated. Gold extraction was found to be enhanced with solutions containing 15-20 mM cupric, 0.1-0.14 M thiosulfate, and 0.2-0.3 M ammonia. Similar trends were obtained for the leaching of gold from two different types of scraps and PCBs of waste mobile phones. From the scrap samples, 98% of the gold was leached out using a solution containing 20 mM copper, 0.12 M thiosulfate and 0.2 M ammonia. Similarly, the leaching of gold from the PCBs samples was also found to be good, but it was lower than that of scrap samples in similar experimental conditions. In this case, only 90% of the gold was leached, even with a contact time of 10h. The obtained data will be useful for the development of processes for the recycling of gold from waste mobile phones.

Reflow ageing influences and wettability effects of immersion tin final finishes with lead‐free solder

PurposeThe purpose of this paper is to investigate the oxidation behaviour of an immersion tin final finish after multiple reflow ageing under air and nitrogen atmospheres and to study their influence on the wetting behaviour with lead‐free solder. To design a model that describes the degradation of wetting behaviour after reflow‐cycling of the immersion tin final finish.Design/methodology/approachA special printed circuit boards (PCB) demonstrator was created to investigate the immersion tin final finish with surface analysis methods and wetting tests. The PCB samples were aged by multiple reflow‐cycling under air and nitrogen atmospheres. The tin oxide formation behaviour of immersion tin was characterised using X‐ray photoelectron spectroscopy (XPS), transmission electron microscope (TEM) and SERA analysis.FindingsThe native oxide layer of the investigated immersion tin final finishes was approximately 7 nm on average. The TEM and XPS investigations indicated an amorphous structure of SnO and SnO2. The solder spread test showed significantly different results for PCBs in “as received” condition compared to those after one and two times reflow ageing under a nitrogen solder atmosphere. The analysis methods revealed a slight increase in the tin oxide layer thickness and small areas with semi‐crystalline structure. Reflow ageing under an ambient solder atmosphere induced considerably thicker oxide layers, which could be observed by a yellow discoloration of the surface.Research limitations/implicationsMeasures to improve the wetting behaviour can be derived from the described model (i.e. use of higher tin layer thickness or protective films to reduce the tin oxidation).Originality/valueA functional model for the solderability process of lead‐free solder on immersion tin PCB final finishes was derived and verified. By this, interactions between the state of the final finish and the solder can be described and potential solderability failures can be predicted.

Moisture diffusion in Printed Circuit Boards: Measurements and Finite- Element- Simulations

When printed circuit boards (PCBs) are exposed to humid ambient conditions an absorption of moisture will occur. This infects the reliability of the PCB and also delamination can occur during reflow. Copper layers in the PCB act as diffusion barrier and have an influence of the moisture distribution and out of this an influence on the moisture concentration in the PCB. Depending on the location the time to get dry PCB increases. In this paper the measurement of different PCB samples will be compared with concentration distribution out of simulations.

A quantile–quantile plot based pattern matching for defect detection

Pattern matching has been used extensively for many machine vision applications such as optical character recognition, face detection, object detection, and defect detection. The normalized cross correlation (NCC) is the most commonly used technique in pattern matching. However, it is computationally intensive, sensitive to environmental changes such as lighting and shifting, and suffers from false alarms for a complicated image that contains partial uniform regions. In this paper, a pattern-matching scheme based on the quantile–quantile plot (Q–Q plot) is proposed for defect detection applications. In a Q–Q plot, the quantiles of an inspection image are plotted against the corresponding quantiles of the template image. The p-value of Chi-square test from the resulting Q–Q plot is then used as the quantitative measure of similarity between two compared images. The quantile representation transforms the 2D gray-level information into the 1D quantile one. It can therefore efficiently reduce the dimensionality of the data, and accelerate the computation. Experimental results have shown that the proposed pattern-matching scheme is computationally fast and is tolerable to minor displacement and process variation. The proposed similarity measure of p-value has excellent discrimination capability to detect subtle defects, compared with the traditional measure of NCC. With a proper normalization of the Q–Q plot, the p-value measure can be tolerable to moderate light changes. Experimental results from assembled PCB (printed circuit board) samples, IC wafers, and liquid crystal display (LCD) panels have shown the efficacy of the proposed pattern-matching scheme for defect detection.

Selective Leaching of Valuable Metals from Waste Printed Circuit Boards

This study was carried out to recover valuable metals from the printed circuit boards (PCBs) of waste computers. PCB samples were crushed to smaller than 1 mm by a shredder and initially separated into 30% conducting and 70% nonconducting materials by an electrostatic separator. The conducting materials, which contained the valuable metals, were then used as the feed material for magnetic separation, where it was found that 42% of the conducting materials were magnetic and 58% were nonmagnetic. Leaching of the nonmagnetic component using 2 M H2SO4 and 0.2 M H2O2 at 85 °C for 12 hr resulted in greater than 95% extraction of Cu, Fe, Zn, Ni, and Al. Au and Ag were extracted at 40 °C with a leaching solution of 0.2 M (NH4)2S2O3, 0.02 M CuSO4, and 0.4 M NH4OH, which resulted in recovery of more than 95% of the Au within 48 hr and 100% of the Ag within 24 hr. The residues were next reacted with a 2 M NaCl solution to leach out Pb, which took place within 2 hr at room temperature.