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Article

Thermodynamic Rarity Assessment of Mobile Phone PCBs: A Physical Criticality Indicator in Times of Shortage

Instituto CIRCE (Research Centre for Energy Resources and Consumption), Universidad de Zaragoza, 50018 Zaragoza, Spain
*
Author to whom correspondence should be addressed.
Entropy 2022, 24(1), 100; https://doi.org/10.3390/e24010100
Submission received: 25 November 2021 / Revised: 20 December 2021 / Accepted: 4 January 2022 / Published: 8 January 2022
(This article belongs to the Section Thermodynamics)

Abstract

:
Rising prices in energy, raw materials, and shortages of critical raw materials (CRMs) for renewable energies or electric vehicles are jeopardizing the transition to a low-carbon economy. Therefore, managing scarce resources must be a priority for governments. To that end, appropriate indicators that can identify the criticality of raw materials and products is key. Thermodynamic rarity (TR) is an exergy-based indicator that measures the scarcity of elements in the earth’s crust and the energy intensity to extract and refine them. This paper uses TR to study 70 Mobile Phone (MP) Printed Circuit Boards (PCBs) samples. Results show that an average MP PCB has a TR of 88 MJ per unit, indicating their intensive use of valuable materials. Every year the embedded TR increases by 36,250 GWh worldwide -similar to the electricity consumed by Denmark in 2019- due to annual production of MP. Pd, Ta and Au embedded in MP PCBs worldwide between 2007 and 2021 contribute to 90% of the overall TR, which account for 75, 600 and 250 tones, respectively, and increasing by 11% annually. This, coupled with the short lifespan of MP, makes PCBs an important potential source of secondary resources.

1. Introduction

The whole world is experiencing soaring energy and raw material costs. Europe is particularly vulnerable to this situation which must import a large part of the raw material domestically consumed by industry and households [1]. Rising energy prices -driven by fossil fuel prices [2]- (electricity [3,4,5,6], natural gas [7] and gasoline and diesel [8]), food [9,10] (fertilizers [11]) and livestock feed [12]), shipping [13,14] and even the lack of microchips for factories [15,16], are examples of this. These supply issues occur when the transformation to a low-carbon economy driven by renewables, electric vehicles, and digitization is beginning to accelerate. This situation could jeopardize the transition since a low-carbon economy requires a large quantity and variety of raw materials. For example, to produce one gigawatt (GW) of electrical power equivalent to that which a natural gas-fired power plant could supply would imply the use of approximately 160,000 tons of steel, 2000 of copper, 780 of aluminum, 110 of nickel, 85 of neodymium and 7 of dysprosium for its construction [17]. These are not negligible amounts if it is estimated that in the future, the power provided by wind turbines in 2050 could be around 2200 GW [18].
Another example is that demand for some minerals for batteries could increase dramatically by 2040 -with respect to 2020-lithium 42 times, cobalt 21 times, nickel 19 or Rare Earth Elements (REE) 7, as the International Energy Agency (IEA) warns [19]. Thus, the use of scarce minerals -needed in a low-carbon economy- could pose a problem for future generations due to their eventual depletion and unavailability in the future [20]. Furthermore, these raw materials are mainly extracted from mines that need fossil fuels to operate. The IEA’s World Energy Outlook 2021 indicates that oil and natural gas production could fall by 8–9% per year without new investments [2]. They have fallen from $779 billion in 2014 to $328 billion in 2020 [21], i.e., they have halved in 6 years. The combination of these factors could lead to bottlenecks of raw materials needed for decarbonization. Therefore, it is essential to strengthen raw material supply chains seeking alternative sources such as e-waste. According to Henckens 2021 [20], if the most stringent resource-saving measures were applied, it would be possible to extend the depletion periods of certain materials required for the energy transition by four times, even if the global service level increases.
This paper examines the raw materials embedded in printed circuit boards (PCBs) in Mobile Phones (MP) as a potential source of secondary resources. These devices have become, in recent years, irreplaceable devices for communication worldwide. The rapid growth in their sales evidences this. MP sales began to proliferate from 2009, reaching approximately constant annual sales of 1.5 billion phones between 2016 and 2020. Resulting in cumulative sales between 2007 and 2020 of almost 14 billion phones and almost doubling the world’s population [22]. The large number of MP, coupled with their short lifespan of around four years [23], contributes to the continuous increase of e-waste, which according to some projections, reach 52.2 million tons in 2021 [24] with an annual growth of 3–5%, a rate three times faster than the increase of municipal solid waste [25]. The most polluting part of a MP is the Printed Circuit Board (PCB) it contains. PCBs account for more than 70% of the carbon footprint of production [26]. In addition, it is the most heterogeneous and complicated fraction [24] as it is composed of a high diversity of elements -more than 40- and elemental concentrations [27]. Some of these elements -such as Pd, Ga or Ta- are scarce in nature [28] or a few countries control their production. Such is the case of Rare Earths Elements (REE), mainly controlled by China.
This issue has been studied by the European Commission (EC), which has been drawing up a list of Critical Raw Materials (CRMs) for the EU every three years since 2011 [29]. The EC criteria point to the economic importance for the EU economy and the supply risk of raw materials to assess criticality [28]. Such criteria are mainly based on geopolitical and economic aspects that are variable over time. For example, Si has soared 300% in less than two months [30], the volatility of Pd has been evidenced by the International Energy Agency [19], the price of Cu has increased 300% in 15 years [31] or REE prices increased 10-fold between 2009 and 2011 and then fell [32]. Moreover, the EC list has not stopped growing, the 2014 list contained 20 CRMs, the 2017 list 26 and the 2020 list 30 [33]. In addition, the list could expand in the future due to these price trends-characteristic in times of shortage-and the growing demand for metals needed for the energy transition [19].
In addition to economic and geopolitical factors, the criticality of an element is determined by its geological scarcity, as the first link in the supply chain of most CRM is mining, which will be one of the decisive factors for the success of renewable transition [19]. As mines become depleted and their ore grade decreases, the energy costs to extract the metal increase exponentially [34,35]. In the limit, a complete exhaustion of all mines would imply that the planet’s mineral wealth would be dispersed, reaching the maximum level of entropy. This state of the planet has been called Thanatia by Valero and Valero [36]. Using this reference, Thermodynamic Rarity (TR) is presented as an exergy-based indicator capable of measuring the thermodynamic criticality of raw materials based on their geological scarcity and the energy intensity required to extract, beneficiate, and refine commodities. Thus, TR is a long-term indicator decoupled from political and economic factors but constrained by mining technology and geological knowledge of the earth’s crust. More information about this indicator and its applications can be found in the following references [37,38,39,40]. TR has been proposed previously as a criticality indicator. For example, Calvo et al., 2018 [28] proposed to add Mo, Te, li, Ta, and V to the list of the 2014 EC CRMs due to their geological scarcity measured by TR. The last three have been added to the 2020 list of these elements. In addition, this indicator has already been successfully used in the study of Electrical and Electronic Equipment [41] and vehicles. Vehicle papers concluded that although Fe, Al and Cu contribute to more than 90% of the car by weight, they only account for 60% of the TR [42] and that many high TR elements end up downcycled as part of alloys or in landfills. Downcycled elements represent 4.5% of the vehicles, while in TR terms, it would be 27% [43]. Currently, EC legislation for End of Life Vehicles requires the recovery of 95% of the vehicle by weight. This can be met by recovering major metals, yet the minor ones become lost or downcycled, losing their functionality. Horta Arduin et al., 2020 [44] has also highlighted this problem in the case of display waste. They state that there is a contradiction between the EC criteria, which on the one hand is concerned with the criticality of CRMs through the publication of lists, but on the other hand, the WEEE recycling regulations focus on weight, causing many critical elements to be lost due to their low contribution in weight. This makes new indicators necessary to reinforce current regulations.
This paper is structured as follows. First, TR indicator is explained. Second, the sources used to calculate the composition of the MP PCBs, the assumptions for calculating the TR and the estimation of resources embedded worldwide are shown. Third, the results of the mass composition, TR and resources worldwide are presented. Finally, the main conclusions are discussed.

2. Materials and Methods

2.1. Thermodynamic Rarity Indicator

TR is an indicator, based on exergy, used to measure the thermodynamic criticality of raw materials, depending on their scarcity in the earth’s crust and the energy intensity associated with mining, beneficiation, and refining processes. Exergy is a property of a system relative to an associated reference state. It is the maximum work a system can deliver as it interacts with another large, but real, system, namely, a reservoir. Such a reservoir attracts the system toward degradation or entropy creation. The reference state selected for the exergy assessment of minerals is a planet, called “Thanatia” (from Greek Thanatos, meaning “death”) with the following characteristics [39]:
  • Crust: there are no concentrated mineral deposits (the upper continental crust can be approximated to the average mineralogical composition of the current earth’s crust), fossil fuels have been depleted, and fertile soils are entirely degraded.
  • Hydrosphere: its composition can be approximated to seawater since freshwater constitutes about 2.5% of global water, of which the most important part is composed of glaciers and ice sheets (68.7%) and groundwater (30.1%).
  • Atmosphere: CO2 concentration is comparable to the complete burning of all remaining fossil fuels.
This imaginary state of the planet does not need to be “reachable”, but it is a baseline to assess the quality of any resource physically. It further allows us to objectively identify which resource is closer to depletion in the race to exhaustion. Any mineral resource with a concentration higher than that found in Thanatia has exergy, and therefore, its quality can be quantified in energy terms [39]. TR incorporates two aspects. The first is the embedded exergy cost (kJ), i.e., the useful energy required to extract and process a given mineral from the cradle to the gate (i.e., until it becomes a raw material for the manufacturing industry). The second is, in fact, an avoided cost for having minerals concentrated in mines and not dispersed throughout the crust (i.e., it can be seen as a natural bonus). As mines become depleted, it becomes exponentially harder to obtain commodities (embedded costs increase), whereas the bonus reduces. This bonus is calculated as a hypothetical exergy cost required if the given mineral would be restored to its initial composition conditions and concentration in the original mines from an utterly dispersed state, i.e., its state in Thanatia. This is the exergy replacement cost (ERC) (kJ) and can be seen as a grave-to-cradle-approach [36] or as a natural avoided exergy cost, i.e., as a natural bonus. Thus, the TR is presented as a physical indicator, stable over time, based on thermodynamic fundamentals. However, it is conditioned by mining technology, as it could reduce the exergy costs of mineral extraction and the knowledge of the earth’s crust that would modify the composition established for Thanatia. Another advantage is that it allows classifying the elements in order of criticality since each element has a unique value, measured in exergy terms.
TR values ( R i ) of the analyzed elements, measured in GJ ton i are shown in Table 1 [28]. Nevertheless, TR values could be higher than those used. As an example, Palacios et al. [45] obtained TR values 2 to 3 orders of magnitude higher than previous values for Cu and Au, using metallurgical process simulation, more specifically the HSC Chemistry software.

2.2. Mobile Phone PCB Data: Composition, Thermodynamic Rarity Calculation and Resources Embedded Worldwide

PCB composition has been obtained by reviewing the literature. A total of 70 samples were taken from Chancerel et al., 2009 [46] (3 samples), Kasper et al., 2011 [47] (3 samples), Oguchi et al., 2011 [48] (2 samples), Yamane et al., 2011 [49] (1 sample), Silvas et al., 2015 [50] (1 sample), Ueberschaar et al., 2017 [51] (1 sample), Ueberschaar et al., 2017 [52] (2 samples), Arshadi et al., 2018 [53] (1 sample), Holgersson et al., 2018 [54] (10 samples), Li et al., 2018 [24] (1 sample), Gu et al., 2019 [27] (12 samples), Korf et al., 2019 [55] (14 samples), Sahan et al., 2019 [25] (19 samples). All data have been transformed to mg element per kg PCB and the complete results have been compiled in Appendix A. Analyzed MP were manufactured between 2004 and 2014. PCBs were subjected to mechanical processing (shredding, comminution or milling), and then the resulting powder was analyzed using different techniques such as ICP-AES, ICP-OES, ICP-MS o XRF.
The TR of a mobile phone (MP) PCB has been calculated through Equation (1). First, the TR of a kg of PCB is calculated (in parentheses). To do this, the product of the TR of an element ( R i ) by its concentration in the PCB is done and then the units of kg of PCB are transformed into units of MP.
R P C B ( u n i t ) = ( i = 1 n R i · m g i k g P C B · 1 1 e 9 )   · k g P C B k g M P · k g M P U n i t M P
Therefore, it is necessary to know its average weight and the percentage of PCBs it contains in relation to its weight. In this paper, as indicated in Equation (1), an average phone weight of 100 g and a PCB percentage by weight of 20% have been used to obtain conservative results. Table 2 shows the percentage of PCBs in phones according to different references. Equation (1) is also used to calculate the contribution of each element to the total TR to analyze the thermodynamic criticality of each element.
To estimate the mass of elements embedded in MP PCBs worldwide, the annual sales of 2020 -around 1.5 billion- and the cumulative sales between 2007 and 2021 -around 14.8 billion units- are taken [22].
Finally, the ratio between the amount of elements embedded in MP PCBs worldwide and the annual extraction of the elements is calculated. For this purpose, the quantity of each element is divided by its extraction. Thus, two percentages are obtained depending on the number of MP considered. On the one hand, the cumulative quantity is considered, i.e., 14.8 billion units between 2007 and 2021; and, on the other hand, the annual sales are considered, i.e., 1.5 billion units. Thus, the first percentage represents the annual production that could be provided if that element were recovered from all the accumulated PCBs. In addition, the second percentage of annual production could be covered with the PCBs of one year. In other words, it would be approximately the percentage of the annual production that is used to produce MP PCBs. The extraction data for the elements were obtained from the U.S. Geological Survey 2021 commodity summaries [58]. In 2020, 210 tons of Pd, 1700 tons of Ta, 3200 tons of Au, 300 tons of Ga, 20,000,000 tons of Cu, 170 tons of Pt and 900 tons of In were mined.

3. Results

3.1. Composition and Thermodynamic Rarity of Mobile Phone PCBs

The 70 MP PCBs samples reviewed are composed of 55 different chemical elements, of which 31 are considered as CRMs by the EC (Figure 1). Although the EC list contains 30 commodities, some of them are groups of elements such as light REE or platinum group metals (PGM), so the number obtained is greater than 30. Taking this into account, 25 elements in the MP PCB are considered critical by the EC. Nevertheless, the contribution by weight of these elements to the total PCBs is very different. Figure 2a,b shows the results of the mass contribution of each element. As can be seen, more than 90% of the weight of PCBs is made up of 8 elements: Cu, Si, Fe, Br, Sn, Ni, Al and Zn, being two CRMs according to the EC: Si and Al. Using the CE criterion, the remaining 47 elements constitute 10% of the overall weight, concentrating up to 23 CRMs. Therefore, most of the critical elements are characterized by low mass concentrations.
To measure criticality, this paper uses the TR indicator. Thus, Figure 2c shows the results of the TR contribution of each element in kJ per unit of MP and Figure 2d the results in percentage, according to the data and assumptions outlined in Section 2.2. If the TR of an element is unknown, it has been counted as 0, as for Te or Br (see Table 1). Taking TR as a criterion, the results are radically different. There are now 3 elements that contribute to almost 90% of the TR: Pd, Ta and Au, 4 others that account for 8%: Ga, Cu, Pt, and In, and remaining 48 for only 2%. Thus, seven elements account for 98% of the TR, being all of them CRMs according to the EC except Au and Cu -the most abundant in PCBs-.
Summing the contribution of each element as shown in Equation (1), the results indicate that the TR of a PCB is 88 MJ per MP unit. This result does not include other parts of the MP, such as the display, camera, or battery, so the TR of the complete MP is higher than obtained. Considering that between 2016 and 2020 mobile sales stagnate at around 1.5 billion mobiles per year (Figure 3b), the TR embedded in MP PCBs worldwide would increase by 1.305·1011 MJ o 36,250 GWh per year, an amount comparable to the electricity consumed by Denmark in 2019 [59].

3.2. Resources Embedded in Mobile Phone PCB Worldwide

In order to estimate the amount of resources embedded in the PCBs of MP, two sources of information have been taken. On the one hand, the number of MP sold between 2007 and 2021 is 14.8 billion, doubling the world population (Figure 3a). On the other hand, the number of MP put on sale annually considered is 1.5 billion units, which since 2016 has stagnated as shown in Figure 3b.
Figure A1 (Appendix A) shows the results for each element, and Table 3 shows the results for the highest TR (rows A and B). It indicates that these elements’ quantity embedded in MP PCBs increases by approximately 11% each year.
This strong annual increase and the short lifespan of the MP -of around four years [23]- make such devices an interesting source of valuable raw materials. Accordingly, we now explore how much of the annual production could -theoretically- be covered by the resources embedded in the MP PCBs. Table 3 shows the annual primary extraction of each element in row C. In the last two rows, the ratios between row A and C, and, B and C are calculated. These ratios indicate the percentage of a year’s global extraction that could be replaced if all of the embedded mass between 2007 and 2021 (A/C) could be recovered, or if all of the mass produced in one year could be recovered (B/C). It is important to emphasize that recovering the entire PCBs from MP is impossible. For example, in Reuter et al., 2018 [60], they only recover 22% of the metals from a MP in the best case. However, they achieve very high recovery rates for some elements such as Au (90–100%), Pd (10–100%) or Ga (80–90%), but much lower for others -Ta (0–10%). Another example is found in Valero-Navazo et al., 2014 [61], in which Pd, Au, Ag, Cu, Ni, Pb and Sn are recovered with recovery rates between 80 and 95%. As can be observed, the elements with higher TR are not always recovered, for example, Valero-Navazo et al., 2014 does not recover Ta or Ga, while in Reuter et al., 2018, the recovery efficiencies of Pd and Ta are 10% and Ga 80% in the worst cases. Therefore, the percentages in Table 3 should be interpreted as a theoretical maximum -unreachable- or, from another perspective, as the percentage of the extraction hoarded by the MP PCBs. In addition to the physical limitations, separate collection rates are very low, ranging from 2 to 16% [61], so high recovery rates are still far from being achieved.
Coincidentally, most elements with the highest ratios are those with the highest TR, i.e., Pd, Ta, Ga, Au, Pt and In, except for Cu. This may be due to the relationship between geological scarcity and low extraction rates. However, this should not necessarily be the case, as it is a result that depends on the composition of the devices to be analyzed. What is important to note is that the recovery of these elements should be prioritized, as they are not only the most critical from the point of view of TR, but if they were recovered, they could make an important contribution to world production. For example, in the case of Pd and Ta, their contribution to world production could theoretically reach 35% if the tons incorporated between 2007 and 2021 could be fully recovered. This figure would be 15% and 8% for Ga and Au, respectively. Considering only the tons embedded in a year, the contribution would drop to 3.8% for Pd and Ta; and to 1.7% and 0.8% for Ga and Au, respectively.

4. Conclusions

The volatility and increase in raw material prices and even the unavailability of some components may jeopardize the energy transition. The search for secondary raw materials and their recovery becomes necessary to alleviate this shortage situation, which could worsen in the future due to ore grade decline, among other factors. In addition, reducing primary extraction would provide other benefits such as less environmental deterioration and greater availability of resources for future generations. To this end, identifying new sources of secondary resources is essential.
This article analyzes the PCBs of the MPs, through the TR. These devices are promising candidates due to their large sales and their short useful life. The use of TR-a physical indicator based on thermodynamics allows obtaining stable values of material criticality in the medium to long term, which can only be influenced by mining technology and knowledge of the earth’s crust. This physical point of view is an essential reinforcement of the criticality assessment of any government, based on the importance of the elements for the given economy’s region and the supply risks. Being decoupled from these time-varying factors, the TR can help establish long-term policies. Another advantage of the TR is that it allows to classify and quantify the elements in order of criticality, as each element has a unique value. This helps identify products and parts with a high content of critical and valuable materials and is helpful for eco-design.
The results show that Pd, Ta, Au, Ga, Cu, Pt and In are the highest contribution to TR in MP. All are considered critical by the EC, except for Cu and Au. In addition, a considerable percentage of the world’s production of Pd, Ta, Ga and Au is hoarded in MP PCBs. These results show the need for the recovery of these elements, not only for the conservation of TR, (i.e., of the exergy embedded in the most geologically scarce elements) but also for their significant contribution to the world’s commodity production. However, 100% recovery of the resources embedded in the equipment is impossible, so to achieve the maximum recovery rate, it is necessary to develop and promote recycling processes that allow it. However, these processes are energy-intensive and require further thermodynamic analysis. This will be analyzed in a forthcoming paper.

Author Contributions

Conceptualization, J.T.; Investigation, J.T.; Methodology, A.V. (Antonio Valero) and A.V. (Alicia Valero); Writing—original draft, J.T.; Writing—review & editing, A.V. (Antonio Valero) and A.V. (Alicia Valero). All authors have read and agreed to the published version of the manuscript.

Funding

This research has been funded by the Spanish Ministry of Science and Innovation [grant number PID2020-116851RB-I00].

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A

Table A1. Samples composition (from Ag to Cu) in mg of element per kg of PCB.
Table A1. Samples composition (from Ag to Cu) in mg of element per kg of PCB.
References (mg/kg PCB)AgAlAsAuBaBeBiBrCaCdCeClCoCrCu
Chancerel et al., 20092244 50
Chancerel et al., 20093573 368
Chancerel et al., 20095540 980
Kasper et al., 20116003100 600 395,600
Kasper et al., 20116009900 1000 383,300
Kasper et al., 20115006100 900 378,100
Oguchi et al., 2011240067,000 4700 400 100 96,000
Oguchi et al., 2011380015,000 150019,000 440 280 330,000
Yamane et al., 201121002600 344,900
Silvas et al., 201521002600 1600 900 355,000
Ueberschaar et al., 2017 (a)
Ueberschaar et al., 2017 (b)158019,7410.043852914,77860.044 38,8510.0440.071 0.044 255,100
Ueberschaar et al., 2017 (b)159742,97921103819,4666666 27,83727 253 464,000
Arshadi et al., 2018147057,930480 3990 37,95032,760 5310 1590210,000
Holgersson et al., 2018264022,73693.31051215298.839.6 1556 2.1952.9342,667
Holgersson et al., 2018277323,0031411083266213860.6 762 1306.7395,000
Holgersson et al., 20182500 1200 1 2000250,000
Holgersson et al., 20184000 400 1 3000200,000
Holgersson et al., 20184000 800 1 700200,000
Holgersson et al., 20185000 1100 45 4000120,000
Holgersson et al., 20185081 19 5 801272,402
Holgersson et al., 20182100 10 344,900
Holgersson et al., 20186091 1591 590,909
Holgersson et al., 20183301 570 234,700
Li et al., 2018138010,000 350 130,000
Gu et al., 20195200 600 273,700
Gu et al., 2019600 800 385,700
Gu et al., 20191000 200 566,800
Gu et al., 2019500 100 398,600
Gu et al., 20192300 1400 428,000
Gu et al., 2019300 900 417,900
Gu et al., 2019300 100 360,000
Gu et al., 20191100 100 408,000
Gu et al., 20193400 319,500
Gu et al., 2019300 200 657,400
Gu et al., 2019 80,500
Gu et al., 20191300 1000 479,000
Korf et al., 2019811814,949 1810,739 5.6 33,9015.6 641792333,228
Korf et al., 2019412518,333 286768 5.6 40,9845.6 39139232,163
Korf et al., 201921002600 344,900
Korf et al., 20196003100 600 395,600
Korf et al., 20196009900 1000 383,300
Korf et al., 20195006100 900 378,100
Korf et al., 2019240067,000 4700 400 100 96,000
Korf et al., 2019380015,000 150019,000 440 280 330,000
Korf et al., 2019100032,700 6001600 56,70045,200 1300 200241,900
Korf et al., 201943025,200145128018,0001.853.8 94801.71.7 500123371,000
Korf et al., 201931010,600258141019,0000.839 12,3000.64.9 54042,000306,000
Korf et al., 201937017,30011155220,300 15.5 83400.92.1 270650494,000
Korf et al., 2019264022,73693.31051215298.839.6 1556 2.1952.9342,667
Korf et al., 2019277323,0031411083266213860.6 762 1306.7395,000
Sahan et al., 201925008900 2400 140190324,700
Sahan et al., 2019170013,200 650 50110370,400
Sahan et al., 2019200010,400 2900 2703900227,500
Sahan et al., 2019200011,500 1400 110330378,000
Sahan et al., 2019470012,900 1300 190510404,000
Sahan et al., 2019830010,700 1800 110130206,000
Sahan et al., 2019510011,800 1600 120290287,500
Sahan et al., 2019370014,900 1500 370460305,200
Sahan et al., 2019320016,600 820 230170451,400
Sahan et al., 2019390016,300 1100 700440313,100
Sahan et al., 2019260020,100 530 50440397,700
Sahan et al., 2019590015,900 1600 300140282,400
Sahan et al., 2019170019,700 170 10015,000409,800
Sahan et al., 2019 6100 378,000
Sahan et al., 201921002600 344,900
Sahan et al., 2019 850017,700
Sahan et al., 20191060 65 408,000
Sahan et al., 2019540 43 398,600
Sahan et al., 2019470015,200 1400 200400326,200
Average255717,62814885996266913847,32518,2286333051992985332,464
St deviation188615,30113761676635917813,25817,31113328351747838116,836
Minimum30026000.0441016000.80.04437,9507620.0440.07113000.04411017,700
Maximum830067,000480290020,30013844056,70045,200457531070042,000657,400
Number of samples66431058188152141252273166
Table A2. Sample composition (from Dy to Na) in mg of element per kg of PCBs.
Table A2. Sample composition (from Dy to Na) in mg of element per kg of PCBs.
References (mg/kg PCB)DyEuFeGaGeHfHgInKLaLiMgMnMoNa
Chancerel et al., 2009
Chancerel et al., 2009
Chancerel et al., 2009
Kasper et al., 2011 14,200
Kasper et al., 2011 65,300
Kasper et al., 2011 48,500
Oguchi et al., 2011 150,000
Oguchi et al., 2011 18,000140
Yamane et al., 2011 105,700
Silvas et al., 2015 124,900
Ueberschaar et al., 2017 (a) 140
Ueberschaar et al., 2017 (b)0.0880.0913,6400.020.5 0.5 0.0880.088
Ueberschaar et al., 2017 (b)681166,36020739 40 3093997
Arshadi et al., 2018 47,090 510 5900515010
Holgersson et al., 2018 8608 0.6 205 60216.27.7139
Holgersson et al., 2018 11,481 0.3 234 116423.48.8753
Holgersson et al., 2018 10,000
Holgersson et al., 2018 15,000
Holgersson et al., 2018 20,000
Holgersson et al., 2018 30,000
Holgersson et al., 2018 8434 8
Holgersson et al., 2018 26,300
Holgersson et al., 2018 35,000
Holgersson et al., 2018 23,500
Li et al., 2018 50,000
Gu et al., 2019
Gu et al., 2019 42,700
Gu et al., 2019 2400
Gu et al., 2019
Gu et al., 2019 46,000
Gu et al., 2019
Gu et al., 2019 10,500
Gu et al., 2019 2800
Gu et al., 2019 19,400
Gu et al., 2019 15,100
Gu et al., 2019 500
Gu et al., 2019 5000
Korf et al., 2019 15,089210 125.6236 13635654 897
Korf et al., 2019 5366184 75.6347 15947569 1197
Korf et al., 2019 105,700
Korf et al., 2019 14,200
Korf et al., 2019 65,300
Korf et al., 2019 48,500
Korf et al., 2019 150,000
Korf et al., 2019 18,000140
Korf et al., 2019 1600 300 1900
Korf et al., 2019 157,20018012.417.9 141 4.6357012407244412
Korf et al., 2019 251,00026720230.34134 6.34116804900265400
Korf et al., 2019 18,9001033.928.2 144 2.937200048075391
Korf et al., 2019 8608 0.6 205 60216.27.7139
Korf et al., 2019 11,481 0.3 234 116423.48.8753
Sahan et al., 2019 23,600
Sahan et al., 2019 20,400
Sahan et al., 2019 37,200
Sahan et al., 2019 33,900
Sahan et al., 2019 48,400
Sahan et al., 2019 10,000
Sahan et al., 2019 5000
Sahan et al., 2019 14,800
Sahan et al., 2019 6400
Sahan et al., 2019 11,900
Sahan et al., 2019 46,300
Sahan et al., 2019 10,100
Sahan et al., 2019 34,200
Sahan et al., 2019 48,500
Sahan et al., 2019 105,700
Sahan et al., 2019
Sahan et al., 2019 2800
Sahan et al., 2019
Sahan et al., 2019 14,600
Average34140,675157152348628411281568127078565
St deviation48150,00472155574104161315291882111358
Minimum0.0880.095000.020.517.90.35.62050.5130.0880.0887.7139
Maximum681251,0002673928.2121445104041590051502651197
Number of samples2261105385855131289
Table A3. Sample composition (from Nd to Sr) in mg of element per kg of PCBs.
Table A3. Sample composition (from Nd to Sr) in mg of element per kg of PCBs.
References (mg/kg PCB)NdNiPPbPdPrPtRhSSbScSiSmSnSr
Chancerel et al., 2009 241
Chancerel et al., 2009 287
Chancerel et al., 2009 285 7
Kasper et al., 2011 34,200 11,700 20,900
Kasper et al., 2011 16,700 12,600 31,100
Kasper et al., 2011 25,400 12,300 25,500
Oguchi et al., 2011 19,000 34,000300
Oguchi et al., 2011 13,000300 35,000430
Yamane et al., 2011 26,300 18,700 33,900
Silvas et al., 2015 34,100 18,700 33,900
Ueberschaar et al., 2017 (a)
Ueberschaar et al., 2017 (b)0.0888390 37071030.0881.75 1858 108,4920.08817,640
Ueberschaar et al., 2017 (b)116237,628 13256851 10 112,800826,948
Arshadi et al., 20184002790100011,190 19002660 94,250 23,540400
Holgersson et al., 20189.711,6009103747119 4.35.7 5430.466,150 19,267108
Holgersson et al., 201860.715,433144126055.4 0.88.5 30.40.656,971 32,20082.5
Holgersson et al., 2018 12,000200 1000
Holgersson et al., 2018 17,0001100 200
Holgersson et al., 2018 9000300 500
Holgersson et al., 2018 11,0003 2500
Holgersson et al., 2018 16185 22
Holgersson et al., 2018 18,700
Holgersson et al., 2018 13,636955
Holgersson et al., 2018 9900294 30
Li et al., 2018 1000 3000210 5000
Gu et al., 2019 400
Gu et al., 2019 25,400 12,200 25,800
Gu et al., 2019 100 14,000
Gu et al., 2019 4000
Gu et al., 2019 6000 600100 26,000
Gu et al., 2019 300 200
Gu et al., 2019 8600 12,100600
Gu et al., 2019 3900 13,600100 16,000
Gu et al., 2019 27,100 17,800
Gu et al., 2019 19,800 10,700100
Gu et al., 2019 100 6100 8600
Gu et al., 2019 8000 100 20,000
Korf et al., 2019 13,454 14055,6 44 334
Korf et al., 2019 6870 24955,6 8 372
Korf et al., 2019 26,300 18,700 33,900
Korf et al., 2019 34,200 11,700 20,900
Korf et al., 2019 16,700 12,600 31,100
Korf et al., 2019 25,400 12,300 25,500
Korf et al., 2019 19,000 34,000300
Korf et al., 2019 13,000300 35,000430
Korf et al., 2019100290040008900 1900 104,800 14,200500
Korf et al., 20194441,500 28399 7.3 12.21160,000 38,100284
Korf et al., 20193257,000 610178 25 9.81280,200 31,400233
Korf et al., 20199.582,900 597100 5.3 3.161745,300 41,700372
Korf et al., 20199.711,6009103747119 4.35.7 5430.466,150 19,267108
Korf et al., 201960.715,433144126055.4 0.88.5 30.40.656,971 32,20082.5
Sahan et al., 2019 32,300 160010 32 62,700
Sahan et al., 2019 13,600 1000DL 22 34,300
Sahan et al., 2019 23,800 730040 26 51,800
Sahan et al., 2019 21,000 2600260 28 28,300
Sahan et al., 2019 37,700 16,700220 50 26,200
Sahan et al., 2019 11,000 16,300360 33 29,600
Sahan et al., 2019 15,800 17,900400 19 35,500
Sahan et al., 2019 31,900 14,600820 7 29,700
Sahan et al., 2019 59,300 23,300120 12 25,300
Sahan et al., 2019 27,000 10,000470 15 13,000
Sahan et al., 2019 17,000 27,300DL 26 33,000
Sahan et al., 2019 15,000 15,600390 36 27,100
Sahan et al., 2019 20,100 1900140 26 13,700
Sahan et al., 2019 25,400 12,300 25,500
Sahan et al., 2019 26,300 18,700 33,900
Sahan et al., 2019 30,200 5800
Sahan et al., 2019 3900 13,60050 16,000
Sahan et al., 2019 3960
Sahan et al., 2019 29,300 15,500400 20 23,700
Average17221,068161710,234251431871900587677,462426,677289
St deviation34716,14711936859245601320895723,840610,939138
Minimum0.08810091013230.0880.85.719003.160.445,3000.08820082.5
Maximum116282,900400027,300110085508.51900266017112,800862,700500
Number of samples115265942225421771125015
Table A4. Sample composition (from Ta to Zr) in mg of element per kg of PCBs.
Table A4. Sample composition (from Ta to Zr) in mg of element per kg of PCBs.
References (mg/kg PCB)TaTbTeThTiVWYZnZrMeasured % of total PCB mass
Chancerel et al., 2009 0.25%
Chancerel et al., 2009 0.42%
Chancerel et al., 2009 0.68%
Kasper et al., 2011 34,300 51.52%
Kasper et al., 2011 9700 53.02%
Kasper et al., 2011 18,200 51.55%
Oguchi et al., 2011 8600 38.25%
Oguchi et al., 20112600 5000 44.45%
Yamane et al., 2011 59,200 59.34%
Silvas et al., 2015 59,200 63.30%
Ueberschaar et al., 2017 (a) 0.01%
Ueberschaar et al., 2017 (b)8970.0880.044 3061 44120.08849.28%
Ueberschaar et al., 2017 (b)1006110 6648 16,15463093.14%
Arshadi et al., 2018 2103060 931037056.12%
Holgersson et al., 2018 164011115564222549.37%
Holgersson et al., 2018 7120.71224.1674329855.50%
Holgersson et al., 2018 27.89%
Holgersson et al., 2018 24.07%
Holgersson et al., 2018 23.53%
Holgersson et al., 2018 17.36%
Holgersson et al., 2018 28.84%
Holgersson et al., 2018 39.20%
Holgersson et al., 2018 64.82%
Holgersson et al., 2018 27.23%
Li et al., 2018 20.09%
Gu et al., 2019 27.99%
Gu et al., 2019 20,700 51.39%
Gu et al., 2019 2200 58.67%
Gu et al., 2019 4600 40.78%
Gu et al., 2019 100 51.05%
Gu et al., 2019 41.96%
Gu et al., 2019 8000 40.02%
Gu et al., 2019 4100 44.97%
Gu et al., 2019 11,900 39.91%
Gu et al., 2019 2200 70.58%
Gu et al., 2019 100 9.59%
Gu et al., 2019 51.44%
Korf et al., 2019 1508 1011 43.83%
Korf et al., 2019 708 960 32.26%
Korf et al., 2019 59,200 59.34%
Korf et al., 2019 34,300 51.52%
Korf et al., 2019 9700 53.02%
Korf et al., 2019 18,200 51.55%
Korf et al., 2019 8600 38.25%
Korf et al., 20192600 5000 44.45%
Korf et al., 2019 1500 70030052.48%
Korf et al., 20192800 6450140111043377069274.29%
Korf et al., 20192000 710018786040560091083.76%
Korf et al., 20192330 730012.8174023069,000128081.70%
Korf et al., 2019 164011115564222549.37%
Korf et al., 2019 7120.71224.1674329855.50%
Sahan et al., 2019 28,100 48.72%
Sahan et al., 2019 8200 46.36%
Sahan et al., 2019 21,200 38.83%
Sahan et al., 2019 2300 48.17%
Sahan et al., 2019 17,800 57.07%
Sahan et al., 2019 3500 29.78%
Sahan et al., 2019 7200 38.82%
Sahan et al., 2019 30,200 44.82%
Sahan et al., 2019 67,000 65.39%
Sahan et al., 2019 5100 40.30%
Sahan et al., 2019 26,900 57.19%
Sahan et al., 2019 13,600 38.81%
Sahan et al., 2019 18,900 53.54%
Sahan et al., 2019 18,200 51.40%
Sahan et al., 2019 59,200 59.34%
Sahan et al., 2019 1000 6.32%
Sahan et al., 2019 4100 44.96%
Sahan et al., 2019 4570 40.77%
Sahan et al., 2019 17,000 44.86%
Average2033152103234495974716,16447564.35%
St deviation78217-2641796548218,616369
Minimum8970.0880.0442107080.71114.11000.088
Maximum28001102107300187174023069,0001280
Number of samples7221137775411
Figure A1. Approach of the element mass (a) and its annual increase (b) in Mobile Phone Printed Circuit Boards worldwide.
Figure A1. Approach of the element mass (a) and its annual increase (b) in Mobile Phone Printed Circuit Boards worldwide.
Entropy 24 00100 g0a1

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Figure 1. Periodic table showing the elements embedded in Mobile Phone PCBs. Elements considered critical by EC are highlighted in red.
Figure 1. Periodic table showing the elements embedded in Mobile Phone PCBs. Elements considered critical by EC are highlighted in red.
Entropy 24 00100 g001
Figure 2. Results of composition (a,b) and thermodynamic rarity (c,d) per element.
Figure 2. Results of composition (a,b) and thermodynamic rarity (c,d) per element.
Entropy 24 00100 g002
Figure 3. Mobile Phone sales worldwide since 2007. (a) Annual sales. (b) Cumulative sales [22].
Figure 3. Mobile Phone sales worldwide since 2007. (a) Annual sales. (b) Cumulative sales [22].
Entropy 24 00100 g003
Table 1. Embedded exergy, exergy replacement cost and thermodynamic rarity of the chemical elements that compose a mobile phone PCB. * Average between ores [28].
Table 1. Embedded exergy, exergy replacement cost and thermodynamic rarity of the chemical elements that compose a mobile phone PCB. * Average between ores [28].
Element (ore)Embedded Exergy
(GJ/ton)
Exergy Replacement Cost
(GJ/ton)
Thermodynamic Rarity
(GJ/ton)
Ag156673718938
Al (Bauxite-Gibbsite)54627682
As (Arsenopyrite)28400427
Au 110,057553,250663,308
Ba13839
Be (Beryl)457253710
Bi (Bismuthinite)56489545
Cd (Greenockite)54258986440
Ce (Monazite)52397620
Co (Linnaeite)13810,87211,010
Cr (Chromite)36541
Cu (Chalcopyrite)57292349
Fe (Hematite)141832
Ga (in Bauxite)610,000144,828754,828
Gd (Monazite)36074784085
Ge (in Zinc)49823,75024,248
Hf 11,18321,81432,997
Hg (Cinnabar)40928,29828,707
In (in Zinc)3320360,598363,917
K (Sylvite)2665667
La (Monazite)29739336
Li (Spodumene)433546979
Mg (from Ocean)102636
Mn (Pyrolusite)581674
Mo (Molibdenite)1489081056
Na (Halite)434487
Nd (Monazite)59278670
Ni (Pentlandite and Garnierite) *265465729
P (Apatite)505
Pb (Galena)43741
Pd583,3338,983,3779,566,710
Pr (Monazite)296577873
Pt291,6674,491,6904,783,357
Sb (Stibnite)13474487
Si (Quartz)77177
Sn (Cassiterite)27426453
Sr72478
Ta (Tantalite)3091482,828485,919
Ti (Ilmenite and Rutile) *1967203
W (Scheelite)59474308024
Y (Monazite)11981591357
Zn (Sphalerite)42155197
Zr (Zircon)13726542026
Table 2. Percentage by weight of PCB in a Mobile Phone.
Table 2. Percentage by weight of PCB in a Mobile Phone.
Source[24][46][54][48][51][56][57]
Minimum20%22%21%12.6%29.5%21%21.1%
Maximum30% 30.3%
Table 3. Annual increase in resources embedded in MP PCBs worldwide. Comparison between annual element production and quantity embedded in Mobile Phone PCBs. Extraction data from reference [58].
Table 3. Annual increase in resources embedded in MP PCBs worldwide. Comparison between annual element production and quantity embedded in Mobile Phone PCBs. Extraction data from reference [58].
Elements PdTaAuGaCuPtIn
(A) Tons embedded[Tons]746022544698,4235.225
(B) Tons embedded 2020[Tons/yr]86427510,5000.62.7
(A)/(B) Annual increase[%]10.8%10.6%10.6%10.9%10.7%11.5%10.8%
(C) Annual primary extraction 2020 [Tons/yr]2101700320030020,000,000170900
(A)/(C) [%]35%35%7.9%15.3%0.49%3.1%2.8%
(B)/(C)[%]3.8%3.8%0.8%1.7%0.05%0.4%0.3%
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Torrubia, J.; Valero, A.; Valero, A. Thermodynamic Rarity Assessment of Mobile Phone PCBs: A Physical Criticality Indicator in Times of Shortage. Entropy 2022, 24, 100. https://doi.org/10.3390/e24010100

AMA Style

Torrubia J, Valero A, Valero A. Thermodynamic Rarity Assessment of Mobile Phone PCBs: A Physical Criticality Indicator in Times of Shortage. Entropy. 2022; 24(1):100. https://doi.org/10.3390/e24010100

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Torrubia, Jorge, Antonio Valero, and Alicia Valero. 2022. "Thermodynamic Rarity Assessment of Mobile Phone PCBs: A Physical Criticality Indicator in Times of Shortage" Entropy 24, no. 1: 100. https://doi.org/10.3390/e24010100

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